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    15 May 2024, Volume 45 Issue 05
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    • Fiber Materials
      Analysis of silks from silkworms reared with artificial diet and mulberry leaves
      HUANG Qing, SU Zhenyue, ZHOU Yifan, LIU Qingsong, LI Yi, ZHAO Ping, WANG Xin
      Journal of Textile Research. 2024, 45(05):  1-9.  doi:10.13475/j.fzxb.20221108601
      Abstract ( 213 )   HTML ( 43 )   PDF (5430KB) ( 164 )   Save
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      Objective In order to understand the quality differences between silk fibers produced by artificial diet feeding and mulberry leave feeding, and to investigate the possibility of substituting artificial diet for mulberry leaves in sericulture, this research systematically analyzed the differences in morphology, composition, chemical structure, and mechanical properties between artificial diet-fed silkworm silk and mulberry leaves-fed silkworm silk.

      Method In this study, two groups of silkworms were fed separately with artificial diet and mulberry leaves.The silk cocoons and fibers were carefully examined for their morphological characteristics using scanning electron microscopy. In order to evaluate the composition of the silk, elemental analysis, amino acid content analysis, and sodium dodecyl sulfate polyacrylamide gel electrophoresis testing were conducted to compare the two feeding methods.Additionally, infrared absorption spectroscopy, two-dimensional wide-angle X-ray scattering (2D-WAXS), and silk tensile testing were employed to elucidate the differences in chemical structure and mechanical properties between artificial diet-fed and mulberry leaves-fed silkworm silk.

      Results The silk with artificial diet feeding exhibited no significant differences in fiber appearance compared to the silk with mulberry leave feeding. Protein composition analysis showed that there was no difference in the type and content of silk fibroin heavy chain protein, silk fibroin light chain protein and sericin protein between the two groups of silk samples. However, notable differences were observed in terms of element content, proline content, secondary structure, and crystallinity. The element analysis revealed that, except for Na and Si, the artificial diet silk had significantly lower content of other elements compared to the mulberry leaves silk. Furthermore, artificial diet silk exhibited lower levels of trace elements such as Al, Cr, and B, while higher levels of Fe, Mn, Zn, and Cu were detected compared to the mulberry leaves silk. Analysis of amino acid content indicated a distinct variation of proline content between the two silk types, with significantly higher proline content in the artificial diet silk. The analysis of secondary structure and crystallinity demonstrated higher β-turn content and lower random coil content in the artificial diet silk compared to the mulberry leaves silk. 2D-WAXS analysis revealed lower crystallinity (60.1%) in the artificial diet silk compared to the mulberry leaves silk (65.2%). Tensile testing showed that the artificial diet silk exhibited a higher average breaking strain (19.8±8.2)%, while the mulberry leaves silk demonstrated higher breaking strength (361.6±97.2)MPa, although the difference was not statistically significant.

      Conclusion The study findings indicated that the observed disparities in morphology, composition, chemical structure, and mechanical properties between artificial diet-fed and mulberry leaves-fed silk were not significant, suggesting the potential of artificial diet as a substitute for mulberry leaves feeding to obtain high-quality silk fibers. The two feeding methods have no significant impact on the quality of silk produced. In addition, artificial diet offered advantages such as the potential for adding beneficial substances and avoiding harmful substances, further highlighting its suitability as a replacement for mulberry leaves in silkworm feeding. Future research may focus on incorporating beneficial substances such as metal ions or proline into artificial diets, by optimizing the formula of artificial diets and adding appropriate amounts of beneficial elements for compensation, in order to selectively improve the mechanical properties of silk and enhance the wider value of artificial diet feeding silk in sericulture. Overall, this comprehensive analysis contributes valuable insights and directions for improving artificial diet in sericulture and enhancing the quality of silk and lays a solid foundation for further promoting the strategic goal of industrialized sericulture of whole age feeding in the future.

      Rheological behavior of cotton pulp cellulose/protic ionic liquid solutions
      MA Kai, DENG Lulu, WANG Xuelin, SHI Guomin, ZOU Guanglong
      Journal of Textile Research. 2024, 45(05):  10-18.  doi:10.13475/j.fzxb.20221108101
      Abstract ( 118 )   HTML ( 17 )   PDF (4136KB) ( 55 )   Save
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      Objective Cellulose is one of the most abundant renewable natural polymers but cannot be effectively dissolved by traditional solvents owing to its highly ordered hydrogen-bond network structure and high crystallinity, which limits the further development and large-scale application of cellulose. Ionic liquids with special structures, due to their strong hydrogen-bond breaking ability, are widely used as a green and efficient solvent for natural polymer dissolution and processing. However, few studies are conducted on protic ionic liquid ([DBNH][Lev]) concerning the dissolution of cellulose and their solution properties. What's more levulinic acid derived from biomass resources endows green properties to [DBNH][Lev].

      Method Protic ionic liquid was used as solvent to achieve the efficient dissolution of cotton cellulose under mild conditions. The dissolution mechanism of cellulose in ionic liquid and the steady and dynamic rheological behavior of cellulose solution were systematically studied by using nuclear magnetic resonance and rheological techniques respectively. The influence of factors such as cellulose concentration, shear rate, and temperature on the rheological behavior of cellulose/[DBNH][Lev] solution was thoroughly investigated. The morphology and mechanical properties of generated films from cellulose/PILs solution were studied in view of their potential application.

      Results The rheological properties of cellulose are closely related to solvent category, cellulose concentration, cellulose molecular weight and experimental temperature. Firstly, it was identified that [DBNH][Lev] presented satisfactory dissolution ability to cellulose and had good solubility up to 5% to cellulose at 100 ℃. The ketone group in the Lev anion may provide a new hydrogen-bonding acceptor and donor in [DBNH][Lev] due to the keto-enol tautomerism, thus strengthening the interaction via hydrogen bonds between cellulose and [DBNH][Lev]. The steady-state rheological curves of cellulose/[DBNH][Lev] solutions with different mass concentrations at 25 ℃. For all case, a shear-thinning behavior is observed with increases in the shear rate and shear-thinning behavior becomes more remarkable when cellulose increases. Newtonian plateau phenomenon is observed when all samples were sheared at low shear rate. At the same shear rate, the apparent viscosity of cellulose solution gradually decreases with increasing temperature, which is consistent with classical polymer solutions. The power law coefficient n increases with the increasing concentration from 1.01 to 2.53 at 25 ℃. The turnover concentration from dilute to the semi-dilute unentangled regime defined as the overlap concentration (C*) was 0.83%. The viscosity-temperature dependence of solution was characterized by using the Arrhenius equation, the dissolution activation energy increases when cellulose increases. The cross-over point (gelation point) resulted in a shift to lower frequency when cellulose concentration increases at 25 ℃. It is found that both G' and G″ shift to higher frequency when the temperature decreases because more cellulose chains entangle together in low temperature at C-4 cellulose solution. Finally, it is also found that the generated films have satisfactory mechanical properties, indicating their practical application potential. The generation film at C-5 cellulose solution has the maximum tensile strength of 88.21 MPa and the elongation at breakup to 7.72%.

      Conclusion A green and low-cost biomass derived protic ionic liquid was applied to successfully enhance its ability to break cellulose hydrogen bonds and achieve effective dissolution in this research. It has been demonstrated that the keto-enol tautomerism in the levulinic acid anion participates in the hydrogen-bond interaction in the cellulose dissolution process. The trend of shear rate and apparent viscosity of cellulose solutions under different mass concentration conditions is consistent, showing the characteristics of pseudoplastic fluid shear thinning. The apparent viscosity of cellulose is related to cellulose concentration and temperature; The overlap concentration for transition from diluted to semi diluted state is 0.83%, and the empirical Cox-Merz rule is not applicable to cellulose/[DBNH][Lev] solutions due to the apparent viscosity curve cannot overlap well with the complex viscosity curve. Therefore, the obtained results in this research provide a basic insight into the rheological response of cellulose in ionic liquid environment, and provide guidance for the processing of cellulose (such as coating and spinning).

      Preparation and properties of chitosan micro-nanofiber composite antibacterial air filter material
      CHEN Jinmiao, LI Jiwei, CHEN Meng, NING Xin, CUI Aihua, WANG Na
      Journal of Textile Research. 2024, 45(05):  19-26.  doi:10.13475/j.fzxb.20221003701
      Abstract ( 147 )   HTML ( 52 )   PDF (7640KB) ( 127 )   Save
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      Objective In recent years, the rapid development of the economy has been accompanied by increased air pollution, leading to frequent hazy weather conditions. Consequently, particulate matter has emerged as the primary pollutant in outdoor air pollution in our country, posing serious health risks to people. Electrospun nanofiber membranes show promise in air filtration due to their small diameter, three-dimensional porous structure, and large surface area. However, the low strength of these nanofiber membranes limits their large-scale industrial application. In this study, we employ chitosan, known for its antibacterial, biodegradable, and biocompatible properties, to prepare an environmentally friendly antibacterial air filter.

      Methods The raw materials used were chitosan spunlaced nonwovens (CS), chitosan (CHI), and polyethylene oxide (PEO). By electrospinning technology, a layer of chitosan/polyethylene oxide (CHI/PEO) nanofibers membrane was electrospun on the surface of CS, and then a composite air filter (CHI/PEO-CS) was obtained. Then, the micro-morphology, fiber diameter, pore size distribution, and air permeability of CHI/PEO nanofiber membranes with different PEO concentrations were measured. Finally, the antibacterial properties of the CHI/PEO-CS composite membrane were studied by testing the filtration performance of the composite membrane and selecting the appropriate PEO concentration.

      Results The average fiber diameter of CHI/PEO fibrous membranes gradually extends from 111 nm to 198 nm with incensing the concentration of PEO. And the average fiber diameter of the CS spunlaced nonwoven fabric is relatively large and about 11.5 μm. With the combination of CHI/PEO nanofibers and CS spunlaced nonwoven fabric, an air filtration membrane was constructed, while the electron microscopy images demonstrate a good adherence between CHI/PEO nanofibers and the CS substrate. The combination of CHI/PEO with CS is solely a physical composite, indicating that there are no chemical reactions between the components. When the concentration of PEO varies between 0.3% and 0.6%, the strength of CHI/PEO-CS remains relatively constant, indicating that the electrospun CHI/PEO nanofibers exert a minimal impact on the mechanical strength of the spunlaced nonwoven fabric. This observation suggests that CS significantly enhances the mechanical properties of CHI/PEO-CS.The pore size distribution of the CHI/PEO-CS composite membrane shows two distinct peaks. The first peak corresponds to the CHI/PEO fiber membrane, while the second represents CS, and the change in pore size follows the trend in fiber diameter. With the increase of PEO concentration, the air permeability was improved accordingly, although the filtration efficiency initially increases and then decreases. Based on these results, we chose a PEO concentration of 0.45% with the highest quality factor for further study. At this concentration, the filtration efficiency of CHI/PEO-CS for 300 nm NaCl aerosol particles significantly increased from 1.6% (in original CS) to 99.56%, with a pressure drop of 63 Pa. Furthermore, after multiple cycles and prolonged testing, the filtration performance of CHI/PEO-CS consistently remained above 99%. Additionally, the interception ratios for E.coli and S.aureus were 99.97% and 99.88%, respectively, significantly surpassing that of pure CS, providing enhanced protection in practical applications.

      Conclusion In order to prepare a kind of environmentally friendly antibacterial air filtration material, a layer of chitosan/polyethylene oxide (CHI/PEO) nanofibers membrane was electrospun on the surface of chitosan spunlaced nonwovens (CS), it was found that the CHI/PEO-CS composite membrane with PEO concentration of 0.45% had better comprehensive properties, including fiber morphology, air permeability (246.2 mm/s), and mechanical properties (2.26 MPa), excellent antibacterial performance (interception ratio >99.88%), high filtration efficiency (99.56%) and lower pressure drop (63 Pa). Therefore, a kind of composite filter material composed entirely of chitosan, was successfully prepared which has both excellent strength of micro-fiber good filtration performance of nano-fiber, and good antibacterial performance. This work provides a new idea for the research and development of functional air filtration materials.

      Analysis of flexoelectric effect of polyacrylonitrile/MoS2 composite film and its applications
      LI Zhikun, YU Ying, ZUO Yuxin, SHI Haoqin, JIN Yuzhen, CHEN Hongli
      Journal of Textile Research. 2024, 45(05):  27-34.  doi:10.13475/j.fzxb.20221104901
      Abstract ( 86 )   HTML ( 11 )   PDF (6515KB) ( 45 )   Save
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      Objective In recent years, flexible electronic devices and smart clothing have been developed rapidly. However, traditional batteries are difficult to meet the practical needs of portability and integration of flexible products. Therefore, there is a demand on the development of flexible energy devices suitable for flexible wearable electronics. Flexoelectricity is an electromechanical coupling effect that converts mechanical energy into electrical energy through the flexural deformation of thin films, thereby powering flexible and wearable electronic devices. molybdenum disulfide (MoS2) and polyacrylonitrile (PAN) composite films have good flexoelectric response, however, the factors affecting the flexoelectric voltage and flexoelectric coefficient are still to be studied.

      Method In this study, the PAN/MoS2 composite film will be prepared by electrospinning, and the structure, morphology and elemental composition of the composite film will be characterized by scanning electron microscope and X-ray diffractometer. The influence of different mass fractions of MoS2 on the flexoelectric effect of PAN/MoS2 composite films was tested through the method of cantilever beam. The flexoelectric stability of the composite films was also examined by intermittent flexoelectric testing. In addition, this paper reported a high flexoelectric voltage generated by connecting multiple composite films in series, which is applicable to micro-miniature wearable electronic devices.

      Results The characterization of composite films fully demonstrated that MoS2 was successfully loaded on the PAN fiber with good crystallinity, understanding that pure PAN film exhibits a weak flexoelectric effect. After adding MoS2 to the film, the flexoelectric voltage and flexoelectric coefficient of the composite film were increased with the increase of mass fraction of MoS2, and the optimum values were obtained when the mass fraction of MoS2 was 50%, which were 0.8 V and 1.96 nC/m, respectively. Continuously increasing the content of MoS2 in the composite film, the flexoelectric coefficient and flexoelectric voltage of the film were decreased rapidly. This is mainly because when the mass fraction of MoS2 was high, clusters were easy to appear, and this would seriously hinder the orderly arrangement of molecular chains, thereby limiting the excursion of positive and negative electrons and inhibiting the flexoelectric effect. The flexoelectric test was carried out every other day, and the test duration was 5 000 s. During the continuous 7 d test, the flexoelectric voltage was about 0.8 V, and the consistency of the voltage value was good, indicating stable the flexoelectric effect of the PAN/MoS2 composite film. In addition, multiple PAN/MoS2 composite films with 50% MoS2 mass fraction were connected in series and packaged. The flexoelectric voltage of a single composite film was about 0.8 V, and the flexoelectric voltages of 3 and 5 composite films in series were 2.3 V and 3.8 V, respectively. Connecting multiple PAN/MoS2 composite films in series obtained high flexoelectric voltage with little voltage attenuation. Experiments proved that the flexoelectric effect obtained by mechanical flexural deformation could almost meet the needs of tiny wearable electronic devices.

      Conclusion The PAN/MoS2 composite film was prepared by electrospinning, and MoS2 nanoparticles were successfully loaded on the surface of PAN fibers. The mass fraction of MoS2 in PAN/MoS2 composite film has a significant effect on its flexoelectric effect. Experiments show that when the mass fraction of MoS2 is less than 50%, the flexoelectric voltage and flexoelectric coefficient increase with the increase of mass fraction of MoS2, when the mass fraction of MoS2 reaches 50%, the optimal flexoelectric voltage and flexoelectric coefficient can be obtained, and when the mass fraction of MoS2 exceeds 50%, the MoS2 particles will cluster and weaken the flexoelectric effect. The composite film prepared in this paper has excellent flexoelectric stability through intermittent flexoelectric test. The experimental results show that connecting the composite films in series can obtain high flexural voltage with extremely weak voltage attenuation, which can meet the needs of tiny flexible wearable electronic devices.

      Preparation and thermal insulation properties of encapsulated polyacrylonitrile/SiO2 aerogel composite nanofibers
      WANG Xinqing, JI Dongsheng, LI Shuchang, YANG Chen, ZHANG Zongyu, LIU Shicheng, WANG Hang, TIAN Mingwei
      Journal of Textile Research. 2024, 45(05):  35-42.  doi:10.13475/j.fzxb.20221105701
      Abstract ( 116 )   HTML ( 21 )   PDF (5901KB) ( 87 )   Save
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      Objective Aerogel is a novel class of three-dimensional network solid materials, which are porous, high in thermal resistance, and low in volume density, and can be prepared by sol-gel method under the action of gaseous dispersion medium. Aerogels therefore have enormous application potential in the area of thermal insulators, energy conservation because they can effectively delay and block heat flow and reduce heat loss. However, low mechanical strength, high brittleness and easy breakage would hinder the actual aerogel applications. One-step forming of aerogel composite fiber can be achieved by using polymer solution and aerogel powder, and the synergistic improvement of thermal insulation/warmth retention performance can be further achieved by regulating the microstructure of monomer fiber and the macro structure of fiber assembly. However, there are few reports on this aspect.

      Method In order to effectively integrate the functional and structural advantages of nano-aerogel and nanofiber, a new production technology was designed and developed to prepare polyacrylonitrile (PAN)/SiO2 aerogel composite nanofibers by one-step method using coaxially solution blowing process. In spinning process, the SiO2 aerogel and the PAN were served as core layer and skin layer respectively, at the speed of 2 mL/h and 12 mL/h. The influences of SiO2 aerogel content in composite fibers on fiber morphology, structure, stability, mechanical properties, and thermal insulation properties were specifically studied.

      Results The PAN/SiO2 aerogel composite nanofibers prepared by coaxially solution blown spinning were continuous, uniform and loosely arranged, and the fiber diameter was distributed primarily in the range of 100-400 nm. Furthermore, three-dimensional crimps were shown in morphology and structure due to the disordered shearing effect of high-speed airflow during the fiber forming process. The introduction of SiO2 aerogel significantly affected the surface morphology of the fibers, forming a porous fold structure. PAN/SiO2 aerogel composite nanofibers were heated up in an oven at 180 ℃ for 240 min to evaluate their thermal stability. After heating, the fibers still retained their porous fold structure, showing good thermal stability. Moreover, the contents of micropores and mesoporous pores on the fiber surface were gradually increased with the increase of SiO2 aerogel content. The obtained PAN/SiO2 aerogel composite nanofibers demonstrated excellent thermal insulation, and the thermal conductivity of the sample with SiO2 aerogel mass concentration of 6 mg/mL was as low as 0.037 38 W/(m·K) at 40 ℃. Under the condition of 50 ℃, the surface temperature of the fiber tested by thermal infrared was 32.5 ℃, and under the condition of 65 ℃, the surface temperature of the fiber tested by thermal infrared was 37.5 ℃. In addition, the weighed PAN/SiO2 aerogel composite nanofibers had a low gram weight (about 70 g/m2), and felt soft and fluffy. Owing to its excellent thermal insulation and convenient and stable production process, PAN/SiO2 aerogel composite nanofibers indicate a broad future market in aspects of thermal insulation, field survival and industrial thermal insulation.

      Conclusion This paper reported a new route of macro quantization preparation of aerogel composite nanofibers by "one-step method". Specifically, PAN/SiO2 aerogel composite nanofibers were prepared by solution blowing coaxial spinning technology using PAN and SiO2 aerogel particles. In conclusion, the prepared solution blown aerogel fiber has the advantages of low weight and flexible manufacture process, and the spinning efficiency can reach 8-12 times that of electrospinning. It can play a broad application prospect in the aspects of thermal insulation, industrial thermal insulation, and military thermal infrared shielding. In the future, an important development direction of aerogel fibers and their products is to utilize simple fiber processing technology to realize one-step integrated processing.

      Structure design and performance of fiber capacitive sensor
      CHEN Ying, SHEN Nadi, ZHANG Lu
      Journal of Textile Research. 2024, 45(05):  43-50.  doi:10.13475/j.fzxb.20221002501
      Abstract ( 94 )   HTML ( 18 )   PDF (3823KB) ( 106 )   Save
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      Objective Due to the limitation of the Young's modulus of the dielectric layer of the current flexible capacitive sensor, the performance of the capacitor, such as sensitivity, cannot meet the requirements, so material selection and structural design of the electrodes and dielectric layers of fabric sensor are required to improve the sensing performance. In this paper, a series of polypyrrole composited silk fabrics were used as fabric electrodes, and wool fiber aggregates were used as dielectric layers to construct an all-fiber capacitive pressure sensor.

      Methods Based on the calculation formula of effective dielectric constant, the pores in the fabric electrode and the air dielectric layer have a positive impact on the sensitivity of the sensor. Therefore, the polypyrrole composite silk fabrics were used as the electrode, and fiber and air aggregates were used as dielectric layers to study the effects of different fabric structures, different types and contents of fibers in dielectric layers on the performance of capacitive sensors. Application explorations of human motion and safety detection were also done.

      Results The square resistance of polypyrrole composited crepe satin fabric (69 g/m2) was the smallest, which was 42 Ω/□. This is because the density of crepe satin silk is the largest, the diameters of yarns are also larger, the warp yarn is twisted, and the weft yarn is not twisted. The sensitivities of cotton and wool fiber capacitors were better than that of acrylic fiber. Because wool fiber has better elasticity and uses less, so wool fiber is finally selected as the dielectric layer. When the height of the dielectric layer is higher, the wool content is larger, the dielectric constant is larger, and the capacitance value is larger, but when the height is too high, the air content decreases, and the deformation ability of the overall dielectric layer decreases, thereby reducing the capacitance change rate, so he height of 1.4 cm as the dielectric layer had the best performance. The fabric 5# (69 g/m2 plain crepe satin) has the highest capacitance of 66 pF when it is used as the electrode. This is probably because the porosity of the fabric 5# is the smallest, the effective area of the electrodes is the largest, and the capacitance is the highest. With the increase of applied pressure, the capacitance increases and the capacitance change rate also increases. The highest sensitivity was 1.08 N-1 at 0.098 N. In the process of applying pressure, the structure of the fabric electrode will change, which will cause the change of the effective relative area and the air content in the fabric electrode, which will also affect the dielectric constant. When the pressure is greater than 1.96 N, the capacitance changes rate of the fabric 5# is the largest, and its sensitivity is the best, so the fabric 5# is used as the capacitance sensor electrode. The capacitive sensor has good stability, and is expected to be used in limb movement monitoring and safety monitoring in public places.

      Conclusion 1) The influence of fabric structure on electrical properties can be concluded as: the greater the fabric density, the denser the yarn arrangement, the more conductive paths, and the smaller the resistance; the fabric electrode not only affects the effective relative area, but also affect the dielectric constant, which in turn affects the overall capacitance. The effect pattern needs further study. 2) The optimized assembly conditions of the capacitive sensor are the wool fiber and air aggregates with a height of 1.4 cm as the dielectric layer, and the electrode is the polypyrrole composite fabric of crepe satin (69 g/m2). The existence of air in the dielectric layer has a great influence on the height of the dielectric layer and the change of the dielectric constant during the compression process; the structure of the electrode fabric will affect the dielectric constant and effective relative area during the compression process. The above factors will ultimately affect the sensitivity of the sensor. Therefore, the next step will be to further optimize the structure of the dielectric layer and fiber composition to find a quantitative relationship, thereby improving the sensitivity of the sensor. 3) Application studies have shown that the capacitive sensor has the ability to sense the bending changes of fingers and the proximity of metal objects and fingers within 10cm, which is expected to apply this multifunctional, low-cost electronic fabric sensor to artificial skin, wearable health detection and contactless detection equipment superior.

      Textile Engineering
      Design of variable porosity structure and evaluation of permeablity and moisture conductivity of single side weft knitted fabric
      FANG Xueming, DONG Zhijia, CONG Honglian, DING Yuqin
      Journal of Textile Research. 2024, 45(05):  51-59.  doi:10.13475/j.fzxb.20221202501
      Abstract ( 96 )   HTML ( 11 )   PDF (4666KB) ( 44 )   Save
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      Objective Human body is prone to perspiration, and requirements for thermal and wet comfort of clothing are essential. Permeability and moisture conductivity of fabrics are important influencing factors for heat and humidity management and regulation, and the transmission of fabric to air and implicit sweat is largely affected by its pore structure, including pore size and pore distribution.

      Method Weft knitted lace plated structures made from different yarn counts were prepared which formed a differential capillary effect inside the fabric to improve fabric moisture absorption and transmission. The lace plated structures used for making the fabrics endowed the fabric surface with different concave/convex patterns, aiming for improved wicking effect. 9.3 tex (384 f), 5.6 tex (24 f), 5.6 tex (216 f), 3.3 tex (12 f) polyester and 2.2 tex spandex were selected as raw materials, and the German Terrot S 296-2 single side circular weft knitting machine was used, and 9 types of fabrics were prepared with weft knitting lace plated structure as samples. The effects of fabric pores, raw materials and structure on fabric moisture absorption, moisture transmission and moisture dissipation were evaluated.

      Results The air permeability of the fabrics was found to be positively correlated with the bulk density, surface porosity and average pore diameter. Since most of the air flew through the fabric pores, the size and distribution of fabric pores were adopted to determine the fabric permeability. The bulk density and average pore diameter showed a great influence on the moisture absorption and conductivity of the fabric. The bulk density and average pore diameter were positively correlated with the moisture conductivity as a whole according to specific conditions. With the same raw materials and organizational structure, the size and distribution of pores were found to affect the tightness of the fabric. Higher bulk density and larger the average pore diameter resulted in tighter fabric structure and greater capillary pressure. The surface porosity was positively correlated with the moisture dissipation performance of the fabric. From the perspective of fabric raw materials and structure, the addition of polyurethane fiber increased the gradient of differential capillary effect of the fabric, leading to improvement of the moisture absorption and conductivity of the fabric, but not the moisture dissipation. Fabric structure will affect the moisture conductivity and moisture dissipation performance. The amount of meshes on the fabric surface was directly related to the specific surface area for fabric evaporation, and more meshes would lead to the better moisture dissipation performance.

      Conclusion The results show that the combination of ultrafine polyester and conventional yarn has advantage in moisture absorption and transmission. A fuzzy comprehensive evaluation method is adopted for analysis. Conclusion is drawn, fineness difference of yarns can enrich the gradient of differential capillary effect of fabrics, and achieve a better differential capillary effect, improving the moisture absorption and conductivity of the fabric. The 6#and 7# fabrics in process 4 have certain advantages in the comprehensive properties of permeability and moisture conductivity, which means the plated fabric with high surface porosity and without spandex, composed of loops and floating structure, has the best comprehensive performance of moisture transmission and permeability. The surface porosity with more meshes in the unit circulation tissue, leading up to the better comprehensive moisture absorption and perspiration performance. The nine schemes in this paper are easy to produce and do not need to obtain unidirectional moisture conduction through additives, which provides theoretical and experimental basis for the development of sportswear fabrics with good moisture and heat management ability, environmental protection and sustainable utilization.

      Structure classification of weft-knitted fabric based on lightweight convolutional neural network
      HU Xudong, TANG Wei, ZENG Zhifa, RU Xin, PENG Laihu, LI Jianqiang, WANG Boping
      Journal of Textile Research. 2024, 45(05):  60-69.  doi:10.13475/j.fzxb.20220902201
      Abstract ( 98 )   HTML ( 20 )   PDF (12677KB) ( 72 )   Save
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      Objective The structure of the fabric is one of the important parameters to guide the production of fabric. The automatic identification of the fabric structure through machine vision helps to improve the design and production efficiency. Weft knitted fabrics have complex knitting methods and various structures, and it is difficult to accurately determine the structure of the fabric only by the image of one side of the fabric. Therefore, it is necessary to design an efficient and accurate classification method for the special structure of weft knitted fabrics.

      Method A fabric image acquisition platform was built to capture images of both sides of fabric samples at multi-scale and various lighting conditions. A dataset containing images of weft knitted fabrics in nine categories was produced. The method in this paper was improved based on GhostNet which is a lightweight convolutional neural network. In order to improve the network's ability to learn different features, two strategies were adopted to introduce an attention mechanism in the feature extraction stage. The network structure was adjusted to a dual-branch architecture, so that the features of the double-sided image of knitted fabrics were simultaneously extracted through the weight-sharing sub-network, and the extracted high-dimensional feature maps were serially fused.

      Results The experimental part analyzes the effectiveness and performance of the proposed method. Multiple online augmentation methods increase the diversity of fabric sample data and improve the robustness of the model. Compared with the original data set, the model has higher accuracy rate on the validation set. Adding a dropout layer after the fully connected layer improves the generalization performance of the model. When the dropout rate is 0.4, the model has the best performance. For the fabric categories that are difficult to distinguish based on single-sided images, the proposed method has achieved a classification accuracy of more than 99%. In order to observe the feature extraction effect of the model more intuitively, the feature maps of different levels of the fabric image are visualized. The model pays more attention to important features such as the shape and texture of the fabric. Accuracy, macro precision, macro recall, and macro F1 are adopted to evaluate the performance of the model, and the number of parameters and computational complexity are calculated to measure the resource consumption of the model. The results of the ablation experiments show that the incorporation of the CBAM module effectively improves the performance of the model. Different models and methods are compared under the same hyper parameter settings. First, common CNN models are tested on the dataset constructed in this paper. The prosposed method achieves the highest classification accuracy of 99.51%, the macro precision rate is 0.994 1, the macro recall rate is 0.994 6, and the macro F1 score is 0.994 2, with lower FLOPs (0.31 G) and params (4.62 M) compared to other models.

      Conclusion Aiming at the classification of weft-knitted fabrics, a double-sided image data set is used in the classification of knitted fabrics. An end-to-end classification method of knitted fabric structure was prosposed based on GhostNet, which is a lightweight convolutional neural network. The experimental results show that the CBAM module enhances the feature discrimination between different fabrics, which improves the network performance. The double-sided features of weft-knitted fabric were efficiently extracted by dual-branch network architecture. Compared with other classification methods based on CNN, the proposed method has a higher classification accuracy and consumes less resources, which is conducive to the deployment of the model on mobile devices or embedded devices. In future work, the case that a single fabric image contains multiple fabric structure will become the focus of research, which is of great significance to further improve the recognition efficiency of the algorithm in the actual design and production process of fabrics.

      Woven fabric simulation based on variable section multifilament model
      XU Hui, ZHU Hao, PAN Suqing, SHI Hongyan, YING Di
      Journal of Textile Research. 2024, 45(05):  70-78.  doi:10.13475/j.fzxb.20230204201
      Abstract ( 59 )   HTML ( 2 )   PDF (7188KB) ( 26 )   Save
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      Objective In order to improve the simulation effect and accuracy of woven fabric simulation and solve the problem of yarn interpenetration during weaving due to the constant yarn cross section, a variable cross-section multifilament model is proposed as the model of warp and weft yarns during woven fabric simulation.

      Method Inspired by the spatial circular parameter equation, the spatial elliptical parameter equation is established as the yarn cross-section model. An ellipse bisection algorithm is proposed, and the multifilament model is established by bisecting the arc length. From the point of view of force analysis, the compression degree of cross sections at different positions on the yarn centerline is analyzed, and the interweaving model of plain and non-plain weave is established, and the flattening state of different cross sections is expressed by flattening coefficient.

      Results It is considered that the cross section of the yarn in the process of warp and weft interweaving is elliptical, and the spatial elliptical parameter equations are derived through rotation transformation and translation transformation. Different from the previous practice that the space curve cylinder is regarded as a yarn model, the proposed multifilament model represents the yarn from the fiber level, and the proposed ellipse circumference bisection algorithm simulates the visual effect of monofilaments arranged on the multifilament surface. When simulating the twisting effect of multifilament, the ellipse circumference bisection algorithm is modified, and the parameters of twist angle are added to simulate the multifilament effect diagrams with different twist angles. From the mechanical point of view, the buckling degree of warp and weft yarns and the degree of extrusion deformation at different positions are deduced, and the woven fabric interweaving model is established by geometric means. The model analyzes the flattened shape of the yarn at the interweaving point and the middle section of the adjacent interweaving point. After calculating the flattening coefficients of the cross-section and the middle cross-section of the interweaving point through the model, it is considered that the flattening coefficients of the warp (weft) yarn cross-section are both between them, or increase or decrease linearly. After calculating the three-dimensional coordinates of each data point, the curve is constructed by spline interpolation to determine the centerline trajectory of the yarn. Combining the centerline trajectory with the yarn flattening coefficient, the section of each point on the centerline trajectory is calculated by using the spatial elliptic parameter equation, and finally the complete warp and weft yarn is formed. The plain weave and twill weave are simulated. It can be seen that the woven fabric constructed by this algorithm has less warp and weft penetration and achieved the expected effect. In order to show the simulation of the algorithm, the changing and jointing weaves are also simulated. The simulation results show that the woven fabric based on multifilament variable cross-section model has high simulation degree and clear surface texture.

      Conclusion The method of deducing the spatial elliptic parameter equation in this paper can be extended to other plane graphics. Plane graphics expressed by parametric equations can be transformed into spatial graphics through rotation and translation transformation, which provides a new idea for the study of fiber and fabric simulation. The interweaving model established from the angle of force analysis conforms to the actual situation. After combining with the multifilament variable cross-section model, the yarn infiltration between different systems is less, and the simulation effect of woven fabric is realistic, which achieves the expected effect.

      Composite technology and properties of fabrics for automotive seat
      HE Fang, GUO Yan, HAN Chaoxu, LIU Mingshen, YANG Ruirui
      Journal of Textile Research. 2024, 45(05):  79-84.  doi:10.13475/j.fzxb.20220708001
      Abstract ( 89 )   HTML ( 11 )   PDF (6969KB) ( 35 )   Save
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      Objective The functional fabrics are one of the important research directions of automotive seat fabrics in recent years. The automotive seat fabrics were made of several materials with different properties to obtain composite functions. In this research, 33.3 tex flame-retardant polyester yarn and 33.3 tex ordinary polyester low-elastic yarn were used to produce the automotive seat fabric, using small jacquard stitching double structure, to achieve good decoration effect, and lame resistance. The warp-knitted spacer fabric (WKSP) offers satisfactory air permeability, breaking and tearing strength, and the elastic recovery, so it is a good material to replace sponge. The knitted fabrics are known to have excellent extension to achieve smooth fabric conformation.

      Method In this work, a simple jacquard fabric was selected as surface fabric (1#). The warp-knitted spacer fabric (WKSF) was used as the middle layer because of its good compression and resilience performance. A knitted fabric was selected as bottom fabric (2#). Then, the 1#, 2# and WKSF were glued with a new type of thermoplastic polyurethane (TPU) hot melt adhesive through the self-developed hot pressing bonding machine to perform composite lamination. The laminating process was designed and applied. Under the optimized conditions, peel strength and air permeability were evaluated to explore the influence of WKSF of different structures on lamination.

      Results Three factors, i.e. the temperature, time and glue amount, were studied and analyzed. The glue amount had a positive impact on the peeling strength, and the compounding time showed a favorable influence on the air permeability of the seat fabric. Meanwhile, TPU hot melt adhesive is a dopted to combine automotive seat fabrics, which could meet the standard or even far higher than the standard evaluation of car seat fabric in air permeability and peeling strength.The orthogonal experiment method was adopted to plan the experiments and the results revealed the optimal composite process parameters, which were composite temperature of 120 ℃, time of 100 s, and 50 g/m2glue application. A new material was WKSF of different structures as middle layer for automotive seat cover instead polymeric foam, and the number of meshes for WKSF and the density of spacer filaments could have an impact on the composite lamination results. A variety of factors of WKSF was comprehensively analyzed to obtain a better influence on the composite lamination by the gray near optimization method. When the number of meshes could be obtained 45 mesh number/(25 cm2), the thickness was 7.12 mm, and the density of spacer filaments was 39.71 pieces/cm2, composite automotive seat fabric had excellent performances in air permeability and peeling strength.

      Conclusion The results show that composite car seat fabric materials have multi-functions, such as decoration and preservation type. The surface layer use flame-retardant polyester fibers and ordinary polyester fibers to interweave, double-layer small jacquard to increase aesthetic and flame-retardant.The WKSF as middle layer in car seat cover has an important role of supporting frame, and it could help solve environmental protection problems and recycling problems.Through composite lamination, the automotive seat fabric can achieve better flame retardancy, air permeability, resilience, peeling strength, etc, which can improve the grade, comfort, beauty, green environmental protection of automotive seat fabric, and could meet requirements of automotive seat textiles completely.

      Dyeing and Finishing Engineering
      Color feature extraction of colored fibers based on two-dimensional Gaussian kernel density estimation
      QIU Kebin, CHEN Weiguo, ZHANG Zhiqiang, HUANG Weizhong
      Journal of Textile Research. 2024, 45(05):  85-93.  doi:10.13475/j.fzxb.20221100901
      Abstract ( 70 )   HTML ( 6 )   PDF (4618KB) ( 36 )   Save
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      Objective The diameters of the textile fibers are usually micrometer-grade, making it difficult to directly measure the colors of the textile fibers. A non-destructive and push-broom microscopic hyperspectral imaging system consisting of a stereomicroscope, an imaging spectrograph, and a digital detector shows an excellent spatial resolution for color measurement of colored textile fibers. In order to improve the accuracy and repeatability of the microscopic hyperspectral imaging system for colored textile fibers, a color feature extraction method of colored textile fibers based on two-dimensional Gaussian kernel density estimation was proposed.

      Method The microscopic hyperspectral images of colored fibers were acquired by the microscopic hyperspectral imaging system. After preprocessing the hyperspectral images to obtain the spectral reflectance at 10 nm intervals over 400 to 700 nm, the fiber region of interest was chosen by the remote sensing image processing software (ENVI 4.8). The spectral reflectance was converted to chromatic values CIE L*a*b*, and the ΔE00 between the average color and each pixel color in the fiber region was computed. A two-dimensional relationship was established between the color difference ΔE00and L* in the textile fiber area to estimate the density value based on the two-dimensional Gaussian kernel density. In addition, a density threshold estimation method was proposed to truncate and remove low-density outliers. Finally, the weighted spectral reflectance with the corresponding density was converted to the colorimetric values.

      Results Empirical analysis was performed using different colored wool fibers. The experimental results showed that the outliers (such as dust and highlight pixels) mainly existed in the tail in the two-dimensional spatial density distribution region, and the long tail indicated more outliers, which would result in a more serious impact on the accuracy and repeatability of color measurement results. In general, the relationship between L* and threshold T was similar among the colored wool fibers, and when T was between 0 and 0.02, the L* appeared to first decrease and then increase, indicating that the threshold value of T at the initial minimum lightness could be used as the density truncation threshold. The differences in L* among the color feature extraction methods were obvious for the majority of colored wool fibers, while the differences in C*, a* and b* were smaller. By truncating and removing the outliers, which would reduce the influence of outliers on the color measurement results, the lightness obtained by the proposed method was smallest. The lightness weighting method had worse repeatability than the proposed method, although both the proposed method and the lightness weighting method could improve the inter-class variation in the color. The possible reasons for this phenomenon could be that the lightness weighting method improved the interclass variability of fiber colors mainly by weighting the highlight pixels. The kernel density estimation method truncated and removed the low-density outliers on the one hand, and improved the weighting of normal pixels by two-dimensional Gaussian kernel density estimation on the other hand.

      Conclusion The proposed method establishes a two-dimensional relationship between color difference ΔE00and L*, and effectively eliminates the effects of low-density outliers based on the two-dimensional Gaussian kernel density estimation. From the comparison results among the proposed method, the mean value method, and the lightness weighting method, the differences in L* are obvious for the majority of colored wool fibers, while the differences in C*, a*, and b* become smaller. In terms of chromatic values, the proposed method can improve the accuracy and repeatability of color measurement based on microscopic hyperspectral imaging for colored wool fibers, which would lay a foundation for the study of dyeing and blending prediction models for colored wool fibers.

      Fully automated camouflage pattern design based on background stitching and texture templates
      ZHAN Yuting, MEI Chennan, WANG Yan, XIAO Hong, ZHONG Yueqi
      Journal of Textile Research. 2024, 45(05):  94-101.  doi:10.13475/j.fzxb.20230201501
      Abstract ( 85 )   HTML ( 7 )   PDF (14639KB) ( 34 )   Save
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      Objective In the military, the ability to reponse quickly to the battlefield environment can determine the success or failure of a battle. Therefore, It is need ed to improve the design speed of camouflage patterns and the ability to adapt to the target environment to enhance the camouflage effect of patterns and thus improve the military level of the army.

      Method First, we automatically combined the background environment dataset into a stitching map and extracted the primary color of the background stitching map by mean clustering method; then, we proposed four automatic design methods for camouflage texture templates, including multi-circular random distribution method, WGN Fourier spectrum method, texture image generation method, and layered cloud method. Finally, after the mean clustering of camouflage texture templates, the camouflage pattern was obtained by replacing the template colors with the primary colors.

      Results Experiments were conducted to construct virtual scenes and a dataset of target background images was created. We stitched the images in the dataset into a single large stitched image. The stitched image was detected based on a color histogram in RGB color space, and a cluster K value of 6 was determined. Six colors were extracted from the eight virtual jungle terrain scenes as primary colors, and four different camouflage patterns were generated based on a texture template method. The experimental time was short, and the design rate was high. In order to reduce the impact of environmental and human factors, camouflage assessment has been relatively carried out using templates based on multi-circular random distribution method, templates based on WGN Fourier spectrum method, templates based on texture image generation method, and templates based on layered cloud method as camouflage targets, and non-environment specific jungle camouflage (FLECKTARN-style jungle camouflage) as a reference. In the subjective evaluation experiments, the experimental results. Each test set had a 100% probability of discovery and different average search times, with the test set of camouflage patterns designed based on the multi-circular random distribution method having the longest average search time of 1.561 4 s. The average search times of all four camouflage patterns designed using this paper's method were greater than those of the reference pattern. Among them, the camouflage search time increased by 8.1% for the template based on the multi-circular random distribution method, 1.1% for the template based on the WGN Fourier spectroscopy method, 2.4% for the template based on the texture image generation method, and 3.7% for the template based on the layered cloud coloring method. In the objective evaluation experiments, the experimental results were shown. The average search time of the PF-Net network for the test set images was the same for both, 0.04 s, but the probability of discovery was different. The detection probability of the test sets designed according to the fully automated camouflage pattern design method based on background stitching and texture templates were both lower than that of the reference target, with the detection probability of the template camouflage based on the multi-circular random distribution method being 45% lower than the detection probability of the reference target.

      Conclusion For automatic camouflage to achieve excellent results, it is necessary to react quickly to generate camouflage colors and textures according to changes in the target environment. This paper proposes a fully automatic camouflage pattern design method based on background splicing and texture templates with a high design rate, rapid response to the target environment, and good camouflage effect according to the modern army's demand for camouflage combined with computer technology. Through the subjective and objective evaluation of camouflage detection, the camouflage effect of the camouflage pattern is evaluated with the search time and detection probability as the primary indexes. The feasibility and effectiveness of the method are verified, and the generated pattern has a good camouflage effect. The camouflage design method in this paper provides various ideas for constructing camouflage design and achieves a more accurate and convenient camouflage pattern design for target area environments.

      Preparation and dynamic adsorption properties of amphoteric cellulose porous hydrogel spheres
      ZHENG Kang, GONG Wenli, BAO Jie, LIU Lin
      Journal of Textile Research. 2024, 45(05):  102-112.  doi:10.13475/j.fzxb.20230402301
      Abstract ( 66 )   HTML ( 5 )   PDF (8117KB) ( 28 )   Save
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      Objective Cellulose porous hydrogel spheres are increasingly used as adsorption fillers by virtue of their combined properties, which have become a current research focus. However, the preparation of these spheres is currently cumbersome and they exhibit limited adsorption efficiency and single adsorption species, restricting their applications. Therefore, it is important to develop a simple and efficient biomass adsorbent with both adsorption and separation capacity for ionic dyes.

      Method In this paper, amphoteric cellulose porous microsphere (ACM) was developed by a two-step chemical modification method of esterification and Schiff base reaction. The surface morphology, pore structure and mechanical properties of the hydrogel spheres were characterized and analyzed using field emission scanning electron microscopy, specific surface area and pore size distribution instrument and universal testing machine, respectively. The adsorption column device was constructed to investigate the adsorption properties of ACM.

      Results The specific surface area, pore size distribution and porosity of ACM with abundant pore structure on the surface and inside were 123.97 m2/g, 0.633 cm3/g and 89.22%, respectively. The compressive strength of ACM was found to be 591.9 kPa at 30% compression deformation, and the mechanical properties were about 63% of the initial value after 40 cycles of compression. ACM showed good compressive strength as well as structural stability because the carboxylation reaction took place at a high temperature, which increased the skeletal density and strength of the hydrogel spheres. Cross-linking occurred during the two-step reaction, producing three-dimensional network structure that enhanced the mechanical properties. It was found that the dynamic adsorption efficiency of the adsorption column wase improved by reducing the initial concentration, increasing the loading height, and slowing down the feed rate. With 0.8 g hydrogel spheres it was possible to treat almost 7.5 L of dye containing wastewater. The hydrogel spheres were chemically modified in two steps to combine high porosity, high mechanical properties, and abundance of carboxyl/amino active groups. 80% separation of mixed dyes was achieved by ACM, which possesses both carboxyl and amino active groups and has different adsorption properties under different acid and base conditions.

      Conclusion The influence of different device parameters on the dynamic adsorption of ACM was investigated, and it was found that the dynamic adsorption efficiency of the column could be improved by decreasing the initial concentration, increasing the loading height, and slowing down the feed rate. The final device parameters were determined as 50 mg/L inlet concentration, 12 cm filling height and 4 mL/min inlet speed. 0.8 g of hydrogel spheres could treat about 7.5 L of wastewater containing dye MeB at the optimum device parameters, demonstrating the good adsorption and separation performance of the ACM.

      Fabrication of antibacterial polymers coated cotton fabrics with I2 release for wound healing
      HAN Hua, HU Anran, SUN Yiwen, DING Zuowei, LI Wei, ZHANG Caiyun, GUO Zengge
      Journal of Textile Research. 2024, 45(05):  113-120.  doi:10.13475/j.fzxb.20230500801
      Abstract ( 63 )   HTML ( 9 )   PDF (29009KB) ( 36 )   Save
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      Objective Using raw cotton fabrics as medical cotton dressings in wound treatment cannot effectively prevent wound infection. Cotton dressings with antibacterial function can effectively prevent wound infections, but most of the antibacterial agents used for antibacterial finishing on cotton fabric face issues of resistance and high toxicity. In order to enhance the value of medical cotton-based dressings by ensuring safe and effective disinfection of wounds on wound management, a safe and low toxicity preparation strategy is urgently needed to endow cotton materials with excellent bactericidal ability.

      Method Iodophor has been widely used for wound disinfection because of its good bactericidal effect and biocompatibility. However, few studies focused on the release of I2 from cotton materials to prevent wound infections, mainly due to the low chelation strength of cotton materials to I2, making it difficult to ensure sufficient I2 release. In order to provide safe antibacterial effects to ordinary cotton materials, we selected less toxic I2 as the antibacterial active ingredient. Cotton fabrics were coated with good biocompatibility of sodium carboxymethyl cellulose (CMC)/polyvinyl pyrrolidone (PVP) hybrid polymer, adding coated cotton fabrics into potassium iodide (KI) solution until full adsorption and sufficient swelling of the cotton fabrics. After that, hydrogen peroxide with a concentration of 3% was added to oxidized KI into I2. Finally, through impregnation method, complexed I2 molecules were attached onto the surface of the coated cotton fabric.

      Results Both CMC and PVP are hydrophilic polymers, and the cotton fabric coated with CMC/PVP hybrid polymer still showed good water absorption ability and were able to adsorb I2 up to 18.6 μg/mg, the existence of polyvinyl pyrrolidone made cotton fabric demonstrated a strong I2 adsorption, ensured bactericidal effect against bacteria. Owing to the excellent film-forming properties of sodium carboxymethyl cellulose (CMC), cotton fabrics coated with CMC/PVP polymers formed a smooth surface, and the PVP polymer on the surface of cotton fabrics adsorbed a large amount of I2. The inhibition zones test confirmed that cotton fabric complexing with I2 was able to continuously release I2 to form an antibacterial ring. Owing to the strong oxidation ability of I2, cotton fabric complexing with I2 was able to quickly kill bacteria in contact via destroying bacteria cell membrane. Cyto-toxicity experiments confirmed that all cotton fabrics, whether before or after coating and complexing with I2, exhibited low toxicity against fibroblasts, and the cell survival rate of all samples was above 90%. When cotton fabric was used for bacterial infection wound treatment, the released I2 killed 99.9% of bacteria at the wound tissue, significantly accelerating wound healing speed. In addition, the release of iodine ions effectively reduced the inflammatory response caused by bacterial infections, thereby accelerating wound healing.

      Conclusion Finishing cotton fabric with CMC/PVP hybrid polymer coating is proven to maintain good water absorption performance and increase the chelating cap ability of cotton fabric to I2, and after complexing with I2 molecules, cotton fabric has almost no toxic effect on cellular tissues. Owing to the adsorption of full dose I2, when using cotton fabrics complexing with I2 for wound infected with bacteria, this cotton fabric can slowly release I2 to kill bacteria at the wound tissues. In summary, using this cotton fabrics releasing I2 can not only prevent wound infections but also reduce the inflammatory response caused by bacterial contamination, thereby accelerating wound healing. This cotton fabric as a medical dressing to treat wounds can effectively prevent wound infection without the need for disinfection. Therefore, this cotton fabric dressing can be a good substitute for current wound dressings and has great potential in the clinical treatment of wound infections.

      Preparation and application performance of nonlinear cationic polyurethane modified silicone softener
      QUAN Heng, QIAN Sailong, LIU Shinan, ZOU Chunmei, NI Lijie
      Journal of Textile Research. 2024, 45(05):  121-128.  doi:10.13475/j.fzxb.20230507701
      Abstract ( 58 )   HTML ( 6 )   PDF (7765KB) ( 22 )   Save
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      Objective The softening finishing of fabric is an important part in textile processing, which improves comfort of textile products. However, the commonly used amino silicone oil softener is known to cause decrease in the hydrophilicity and elasticity of fabrics. Thus, designing and developing a new type of softener, which could improve the hydrophilicity while maintaining the unique style and superior feel of finished fabrics, is expected to solve the problem of existing amino silicone oil softeners.

      Method Triethanolamine and isophorone diisocyanate (IPDI) were adopted to prepare the prepolymer. The chain extension was carried out by introducing small molecule hydroxyalkyl silicone oil 3667(D3667) and polyethylene glycol 2000(PEG2000) into the prepolymer. A series of nonlinear cationic polyurethane modified silicone softeners (BS, LcS and NmS) were synthesized associated with different feeding ratios of IPDI, PEG2000 and D3667. The chemical structure of the products was characterized by Fourier transform infrared spectra. The thermodynamic properties and surface morphology were utilized to explore the blending state between nonlinear cationic polyurethane modified silicone softener and amino silicon oil. The crease recovery angle, hydrophilicity and comprehensive hand feel of the finished fabric were studied to reveal the influence of softener structure on the performance of textile.

      Results Through thermal performance testing, it was found that the maximum weight loss rate temperature of the composite of AS and branched cationic polyurethane modified silicone softener (BS) increased from 334 ℃ to 397 ℃ compared to amino silicone oil (AS). It was found that only one endothermic peak remained in BS/AS mixture, indicating that the BS/AS composite exhibited excellent blending performance and would be less prone to microphase separation. No significant difference was identified in the surface morphology between the AS treated bleached nylon/spandex fiber and the BS/AS composite treated fiber in the knitted fabrics, and the mass fraction of silicon element of BS/AS treated fiber surface (0.8%) was between that of the AS (1.4%) and BS (0.04%). This result confirmed that BS formed a uniform mixture with AS to resist the microphase separation. The convenitional amino silicon oil treated cotton fabric showed a crease recovery angle of 95.4° and a hydrophilic time of more than 800.0 s for nylon/spandex fabric. After composite treatment with branched cationic polyurethane modified silicone softener, the crease recovery angle of the cotton fabric was increased to 129.1°, and the hydrophilic time of nylon/spandex fabric was decreased to 48.6 s, significantly improving the hydrophilicity and crease recovery angle of amino silicone oil. This improvement can be attributed to the entanglement and bonding between the non-linear cationic polyurethane modified silicone softener and the hydrophobic chain segments of amino silicone oil. As for comprehensive hand feel, the BS/AS complex was shown to improve the smoothness, bulkiness, and warmth of the nylon/spandex fabric, but decrease the softness.

      Conclusion Nonlinear cationic polyurethane modified silicone softener can form a uniform mixture with amino silicone oil, and the thermal stability of the composite is improved. The molecular entanglement and association between the nonlinear cationic polyurethane modified silicone softener and amino silicone oil can effectively prevent the accumulation of amino silicone oil on the auxiliary/air interface, thus improving the crease recovery angle of cotton fabrics and the hydrophilicity of nylon/spandex fabrics. The BS/AS complex can improve the smoothness, bulkiness, and warmth of nylon/spandex fabrics, demonstrating promising application prospects in high-end fashion or sportswear fabrics.

      Preparation and properties of cotton fabric with poly(N-isopropylacrylamide) antibacterial hydrogel
      XUE Baoxia, YANG Se, ZHANG Chunyan, LIU Jing, LIU Yong, CHENG Wei, ZHANG Li, NIU Mei
      Journal of Textile Research. 2024, 45(05):  129-137.  doi:10.13475/j.fzxb.20230102501
      Abstract ( 51 )   HTML ( 14 )   PDF (10101KB) ( 15 )   Save
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      Objective In order to reduce the incidence of chronic wound infections, various antibacterial dressings play an important role in wound treatment, among which silver-based antibacterial hydrogel is advantageous. However, sudden release of silver antibacterial agent and poor mechanical properties of hydrogel are the two factors limit lug the application of silver-based antibacterial hydrogel in wound repair. In order to expand the application of silver-based antibacterial hydrogel and achieve sustained release antibacterial, the construction strategy of hydrogel composite fabric was adopted to prepare a new type of hydrogel composite fabric integrated dressing.

      Method Combined with the chemical crosslinking and ultra violet light initiation method, the polyazoiso propyl acrylamide (PNIPAM) /silver loaded with graphene oxide (GO-Ag) hydrogel was combined with cotton fabric to form a new kind of dressing with the integration of hydrogel and fabric. The number of cotton fabric layer used for constructing dressing was explored. The structure, antibacterial properties, tensile strength, and biological safety of dressings were discussed by scanning electron microscopy, infrared spectroscopy, fluorescence inverted microscopy, etc.

      Results Based on research of PNIPAM/GO-Ag composite antibacterial hydrogel, the PNIPAM/GO-Ag composite cotton fabric with different layers was constructed. According to the analysis results of microscopic morphology and molecular structure, the PNIPAM/GO-Ag hydrogel was evenly interpenetrated into cotton fabric because of the strong interface integration. Analyzing the antibacterial, mechanical, and biological properties of PNIPAM/GO-Ag hydrogel composite cotton fabric with different layers, it was found that the integrated dressing, composed of PNIPAM/GO-Ag hydrogel and three-layer cotton fabric, demonstrated excellent comprehensive performance. In contrast with the single cotton fabric, the tensile breaking strength of the integrated dressing were improved by 73.7% and reached 370 N in the wet state. This proved that the integrated structure of hydrogel composite cotton fabric could help strengthen the mechanical property of hydrogel, which is beneficial for the promotion and application of wound dressing. The in vitro cytotoxicity of dressing was 0 level, and the hemolysis rate was less than 5%, demonstrating the PNIPAM/GO-Ag hydrogel composite cotton fabric illustrated good biological safety, providing possibilities for its application in the field of wound repair. The inhibition rate of antibacterial dressing on the Escherichia coli and Staphylococcus aureus reached more than 98%. Compared with the slow-release antibacterial effect of PNIPAM/GO-Ag hydrogel, the PNIPAM/GO-Ag hydrogel composite cotton fabric developed slow-release effect of silver within 24 h, proving that the hydrogel composite fabric has a more durableability to continuously release silver release silver ions, and the slow-release antibacterial effect of dressing was enhanced. In addition, from the analysis of bacterial micro-structure and intracellular reactive oxygen species (ROS) content, it was concluded that the antibacterial effect of hydrogel composite cotton fabric was mainly achieved by continuously releasing silver ions, inducing bacteria to produce ROS and causing oxidative damage to bacteria, thereby destroying bacteria.

      Conclusion The combination strategy of hydrogel composite fabric is one of the methods to construct a new integrated antibacterial dressing, which has the characteristics of both hydrogel and fabric. It was found that the interface between the hydrogel and cotton fabric directly affects the structural integrity of the overall dressing. The number of cotton fabric layer determines the interface binding force between the hydrogel and each layer of the fabric, and ultimately affects its tensile properties, antibacterial properties, and biological safety. Compared with the single cotton fabric, in the wet state, the tensile breaking strength of PNIPAM/GO-Ag composite cotton fabric integrated dressing has strengthened. And the slow-release antibacterial effect of silver ions in PNIPAM/GO-Ag composite cotton fabric is more pronounced than that of PNIPAM/GO-Ag hydrogel.

      Preparation and sensing performances of flexible temperature sensor prepared from melt-blown nonwoven materials
      WANG Nan, SUN Hui, YU Bin, XU Lei, ZHU Xiangxiang
      Journal of Textile Research. 2024, 45(05):  138-146.  doi:10.13475/j.fzxb.20220603201
      Abstract ( 70 )   HTML ( 13 )   PDF (7157KB) ( 29 )   Save
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      Objective Most of high sensitivity temperature sensors are prepared from membranes, metals and other substrate materials, and flexible textile materials with good processing performance and low cost are increasingly used much for making flexible sensors, such as wearable electronics for e-skin and health monitoring and flexible temperature sensors which have advantages in simple structure, wide range of applications and low preparation cost. This research explores the preparation and sensing performance of flexible textile temperature sensors prepared from melt-blown nowoven textiles.

      Method PEDOT:PSS/CNTs/PBTNW flexible temperature sensors were prepared by co-loading poly(3,4-ethylenedioxythiophene)-poly(styrene sulfonic acid) (PEDOT:PSS) and carbon nanotubes (CNTs) with different concentration ratios on the surface of PBT melt-blown nonwoven (PBTNW) by a simple ultrasonic process. The method is simple, and the temperature sensor can monitor the human body as well as the environment temperature, which expands the application field of textile materials.

      Results SEM evaluation showed that the interstices of PBTNW loaded with PEDOT:PSS polymer were filled with a small number of one-dimensional CNTs, forming a one-dimensional and two-dimensional structure, which in turn formed a three-dimensional networls structure easy for electrical conductivity and temperature sensing. The PEDOT:PSS and the CNTs formed a complete conductive network with the PBTNW as the backbone. The presence of the polymer PEDOT:PSS mitigated the agglomeration of CNTs better than loading CNTs alone. The temperature sensing test results showed that the prepared temperature sensor achieved a sensitivity of up to -0.71%/℃ in the range of 25-80 ℃, fast response time (18 s), good linearity (R2=0.99), hysteresis as low as 4.98%, good reusability as well as a long term stability, and a sensing accuracy of 0.1 ℃ in the temperature range of 37-38 ℃. The thermal stability and mechanical properties of PBTNW and PEDOT:PSS/CNTs/PBTNW with different loading ratios were analyzed. After loading PEDOT:PSS and different ratios of PEDOT:PSS and CNTs on the surface of PBTNW, the thermal stability and mechanical properties of the prepared flexible temperature sensors were found to be the best when the ratio of PEDOT:PSS to CNTs was 1∶0.6.

      Conclusion Fast response time and high sensitivity gives flexible temperature sensors not only in the environmental temperature measurement of the possibility, but also expand its possibility in the field of human body temperature monitoring. It is indicated that textile materials as a lower cost and simple processing methods of flexible materials have the prospect for applications in the field of flexible sensors. The reliability of the prepared temperature sensors was proved experimentally. However, it is difficult to have high strength due to the characteristics of nonwoven materials themselves, which limits the long-term use of nonwoven temperature sensors.

      Apparel Engineering
      Cold and hot changes in upper torso skin temperature and division of heat regulation zones
      DING Xiaodie, TANG Hong, GAO Qiang, ZHANG Chengjiao
      Journal of Textile Research. 2024, 45(05):  147-154.  doi:10.13475/j.fzxb.20230600901
      Abstract ( 71 )   HTML ( 10 )   PDF (5975KB) ( 26 )   Save
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      Objective The upper torso is the part of the human body with the highest heat output, and the temperature changes in cold and hot environments greatly affect the thermal balance of the human body. In response to the design issue of the cold and hot regulation zone in the integrated refrigeration and heating clothing, this study explores the degree of cold and hot demand for various parts of the upper torso affected by the cold and hot environments of the human body using skin temperature as a key indicator.

      Method The skin temperature of different zones of the upper torso of young men was measured in cold and hot environments in four different movement states, including sitting, standing, walking, and brisk walking. The distribution of human organs and the distribution of human sweating were adopted to divide the upper torso skin zone, and contact temperature sensors was adopted to continuously monitor the skin temperature. The changes in skin temperature of each part were analyzed to judge the cold and hot conditions of local areas, and cluster analysis was employed to classify the skin temperature changes.

      Results By comparing the local temperature with the average skin temperature in the same environment and comparing the local skin temperature under the same movement state, it was seen that the distribution pattern of skin temperature in the upper torso of the human body in cold environments was studied and it showed that the average skin temperature of the human body demonstrated an overall decreasing tendency, with particularly significant changes in all four states. But the skin temperature of each part of the upper torso was decreasing and then increasing. During the sitting state, skin temperature in most zones was decreased significantly, and at this state, the temperature of side chest and back shoulder was generally significantly lower than the average skin temperature of the human body. At any states, the temperature of the side chest and back shoulder was at a lower level compared to other torso zones, which was lower than the thermal comfort temperature of the human body. Except for some zones close to the core organs of the human body, the skin temperature was relatively high and changes were relatively small. As the cooling time was increased, especially during the brisk walking state, the temperature change was decreased and the cold sensation gradually weakened. The distribution pattern of skin temperature in the upper torso of the human body under thermal environment showed that the overall skin temperature in various parts of the upper torso was similar to the average skin temperature of the human body, both of which was increased as experimental time got longer. The maximum temperate increase was observed at the sitting state, with temperature at the front shoulder, back shoulder, and middle back significantly becoming higher than the average skin temperature of the human body. At the same state, the temperature of all parts was generally higher, with the front and back shoulder being higher in any state and higher than the temperature of human thermal comfort. And the results obtained in the same environment and at movement state were consistent with the classification results obtained through cluster analysis. In addition, through comparison, it was found that the influence of the environment on skin temperature in various parts was much greater than that of the movement state. Therefore, it was necessary to focus on considering the impact of the environment on the local skin temperature of the human body.

      Conclusion For the design of the cold and hot regulation zone in heating clothing, combined with the cold and hot situation in local zones of the human body and the results of cluster analysis, a heating device is adopted to focus on heating the side chest and back shoulder of the first level heating zone, so as to achieve local heat adjustment. For the design of the cold and hot regulation zone in refrigeration clothing, the refrigeration device is adopted to focus on cooling the front and back shoulders of the first level cooling zone. However, when designing the cold and hot temperature adjustment zone for clothing with both heating and cooling functions, it is necessary to comprehensively consider the degree of cold and hot demand in the side chest, front and back shoulders, and other zones.

      Occlusive clothing image segmentation based on context extraction and attention fusion
      GU Meihua, HUA Wei, DONG Xiaoxiao, ZHANG Xiaodan
      Journal of Textile Research. 2024, 45(05):  155-164.  doi:10.13475/j.fzxb.20230502601
      Abstract ( 69 )   HTML ( 12 )   PDF (8697KB) ( 45 )   Save
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      Objective Visual analysis of clothing attracts attention, while convenitional methods for clothing parsing fail to capture richer information about clothing details due to various factors including complex backgrounds and mutual occlusion of clothing. Therefore, a novel clothing image instance segmentation method is proposed to effectively extract and segment the multi-pose and mutually occluded target clothing in complex scenes for the subsequent processing of clothing analysis, retrieval, and other tasks to better meet targeted needs for personalized clothing design, retrieval, and matching.

      Method The output features of ResNet were optimized by using a context extraction module to enhance the recognition and extraction of feature representations of occlusive clothing. Then the attention mechanism of residual connectivity was introduced to adaptively focus on capturing the semantic inter-dependencies in the spatial and channel dimensions of occlusive clothing images. As the last step, CIoU computational principle was used as the criterion for non-maximal suppression, while focusing on the overlapping and non-overlapping regions of the predicted box and the real box to select the optimal target box that covers the occlusive clothing to the fullest extent.

      Results In qualitative comparison with Mask R-CNN as well as Mask Scoring R-CNN and YoLact methods, the proposed method showed stronger mask perception and inference ability, effectively decoupling the overlapping relationship between masked garment instances with more accurate segmentation visual effect. In addition, accuracy (AP) was used as an evaluation index for further quantitative analysis of the improved model, and the segmentation accuracy APm under different IoU was 49.3%, which was 3.6% higher than the original model. Meanwhile, by comparing the segmentation accuracy of each improved model for different occlusion degrees, it was seen that the Mask R-CNN model had the lowest segmentation accuracy for various occlusion degrees, while with the optimization of CEM, AM and CIoU strategy, the accuracy of the improved model in minor occlusion APL1, moderate occlusion APL2 and severe occlusion APL3 was improved by 4.3%, 4.2% and 4.8%, respectively, and the most significant improvement in segmentation accuracy was for severely occluded clothing. Finally, the accuracy of the proposed method was compared with that of Mask R-CNN, Mask Scoring R-CNN, SOLOv1, and Yolact. The overall accuracy of Yolact model for segmenting clothing with different degrees of occlusion was slightly lower, the overall accuracy of Mask Scoring R-CNN for segmenting clothing was slightly higher than that of Mask R-CNN, and SOLOv1 achieved similar segmentation accuracy as Mask R-CNN. The accuracy of the proposed method was significantly better than that of other methods for segmentation of garments with different occlusion degrees, where APL3 for segmentation of severely occlusive clothing was improved the most, which was 4.8% higher than Mask R-CNN and 4.2%-11.1% higher than other models.

      Conclusion By embedding the context extraction module, attention mechanism module, and CIoU computation strategy into Mask R-CNN network, a novel clothing instance segmentation model is constructed, with enhanced recognition and extraction ability of the model for clothing features. The semantic inter-dependencies between masked clothing feature maps in spatial and channel dimensions are captured, and the segmentation accuracy for each clothing is improved. The optimal target frame is predicted for each clothing instance, which improves the accuracy of the model for segmenting occlusive clothing instances. Through a series of comprehensive experiments, the feasibility and effectiveness of the proposed method are proved, providing a new idea for the research of clothing image instance segmentation.

      Rapid extraction of edge contours of printed fabrics
      WEN Jiaqi, LI Xinrong, FENG Wenqian, LI Hansen
      Journal of Textile Research. 2024, 45(05):  165-173.  doi:10.13475/j.fzxb.20230505901
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      Objective The industrial sewing robot based on contour extraction detects fabric edge contours with visual aids and works out the robot's movement trajectory based on the fabric edge contour information to achieve the sewing of the fabric in conjunction with the sewing machine. However, the large number of raw edges of the fabric after cutting, the print pattern of the fabric and the background of the fabric image acquisition all affect the accuracy of the fabric edge contour extraction, and the extraction time directly affects the sewing efficiency.

      Method The conventional VGG-UNet model was optimized by convolutional splitting and fusion loss functions to improve the inference speed and segmentation accuracy of the model. The optimal fabric detection model was then constructed and trained using the optimized VGG-UNet to segment quickly and accurately the printed fabric and the desktop background, and the fabric burrs were removed using adaptive open operations before the Canny operator was used for edge detection to obtain accurate fabric edge contours.

      Results The optimized VGG-Unet optimal training results were 0.79%, 0.79%, 1.6%, 0.79% higher than those of the VGG-UNet model in each index, and the inference speed was reduced by 10.368 ms, and the number of total parameters of the optimized VGG-UNet model was greatly reduced. The optimal fabric detection model that was trained and constructed showed obvious advantages in terms of memory resource consumption and detection efficiency. The superimposed image showed that the contour extraction accuracy was not affected even though the printed fabric was similar in color to the desktop background and the lighting was not uniform. In addition, the contour lines were hand-drawn on the original image, and the fabric edge contour lines extracted in this paper were computed by OpenCV to find out the overlap of the two contour lines, and the accuracy of the contour line extraction was more than 99%. The complete algorithm was obtained by pytorch programming on a computer with Windows 11 operating system, GPU using NVIDIAGerforce GTX 1650 and 16 G memory, and it took only 0.216 s to extract the edge contour of the fabric in a fabric image, while the proposed conventional contour extraction method took 2.852 s. The edge contour of the printed fabric was the worst when the fabric color is close to the background color of the desktop and when the reflection of the desktop was severe. In addition, the conventional contour extraction method does not consider the burr problem generated by the fabric cutting, so the conventional contour extraction algorithm not only has a long extraction time but also cannot remove the noise and the burr efficiently, making it difficult to accurately extract the edge contour of the printed fabric.

      Conclusion This paper proposes the use of deep learning combined with conventional contour detection algorithms to extract fabric edge contours for the first time. It solves the problem that traditional fabric contour extraction methods are affected by fabric color, print pattern, fabric texture and desktop background, and has excellent performance in extracting edge contours of fabrics with complex prints. In this paper, we consider the large number of raw edges generated by the fabric edges after cutting the fabric. This method can effectively remove the raw edges and extract the fabric edge contours quickly and accurately, and the extraction process is not affected by the print pattern, table background, color, fabric texture and light source, the method is good in generality and the extraction results fit the fabric edge contours highly.

      Clothing pattern contour extraction based on computer vision and Canny algorithm
      TUO Wu, DU Cong, CHEN Qian, WU Chao, WEI Xinqiao, ZHANG Xinru, LIU Siyu
      Journal of Textile Research. 2024, 45(05):  174-182.  doi:10.13475/j.fzxb.20230502901
      Abstract ( 79 )   HTML ( 17 )   PDF (4808KB) ( 36 )   Save
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      Objective In order to improve the accuracy and convenience in collecting and converting two-dimensional pattern contour information, a method for garment pattern contour extraction based on computer vision was proposed. This research aimed to extract digitally the clothing patterns based on the use of two-dimensional pattern contour obtained from scanners or cameras. It was expected that the original physical template in the form of pattern card would be accurately transformed into a computer pattern contour image.

      Method This method involved both hardware and software. The hardware part was composed of a bracket, a smart phone, a quadrupod, a background plate and a calibration plate. The smart phone's camera was used as the image acquisition device. The part of software consists of image processing and contour extraction. The distortion parameters were obtained using the chessboard camera calibration method for image correction, and the corrected image was gray-scale processed and the matrix was simplified. The contrast between the target image and the background was improved by gamma transform ation of the gray image. The improved Canny algorithm was applied to extract the edge information of paper pattern image, and adaptive bilateral filtering was used for better edge-preserving denoising. The gradient templates in the 45°and 135°directions were added to the original Sobel convolution to calculate the gradient so as to improve the accuracy of position weighting coefficient. Non-maximum suppression and adaptive double threshold selection were carried out to refine and determine the edge. Double edge filling, smoothing and reprocessing of contour map were carried out by morphology closing operation. Finally, the skeleton was extracted from the contour map.

      Results After multiple experiments, the accuracy and effect of contour extraction were verified. In order to verify the accuracy of the contours, the function cv2.findContours() in the python library was adopted to extract the contour information which was then drawn on a white screen using Matplotlib library. The contour image was vectorized and saved as a PDF format, and the contour length was measured by the tool included in the PDF. The measurement results showed that the error was 0.15-1.50 cm compared to the length measured in reality, and the overall error was in line with the clothing error standard. More optical distortions and aberrations may be introduced when the object was placed at the edge of the camera for imaging, resulting in a larger imaging error than the central position. Therefore, it was necessary to place the paper pattern in the center of the camera during the operation to reduce the extraction error. In order to verify the effect of contour extraction, the extracted outer contour map, inner contour map and inner-outer contour map were compared with the contour map extracted by the traditional Canny edge detection algorithm. Both the conventional algorithm and the algorithm in this paper were able to outline roughly the contour map of the paper pattern. For the extraction of the outer outline of the paper pattern, both algorithms could achieve the same extraction effect by changing the sigma value and the parameters of the high and low threshold. However, a big difference existed between the two algorithms in the extraction of inner and inner-outer contours of paper patterns. When extracting the local details of the image, the effect of the conventional algorithm was not ideal, because the extracted internal symbol contour was not smooth enough, causing some small burrs and information lost. The proposed algorithm obtained the extraction of internal symbols without distinction, and the extraction effect of details such as cut, buckle and connector was good. For the whole image extraction, the effect of the conventional algorithm was not satisfactory in causing a large number of breakpoints and a small amount of noise points, especially for the inner contour extraction of paper pattern. Compared with the conventional algorithms, the proposed algorithm was found more prominent in removing noise and preserving edges and in creating clear extraction of the outer contour, inner contour and inner-outer contour of the paper pattern. It was suitable for the contour extraction of two-dimensional paper pattern, offering improved extraction efficiency.

      Conclusion The proposed improved pattern extraction algorithm combining the knowledge of camera calibration and image processing, can conveniently and efficiently achieve digital extraction of clothing pattern. Computer vision detection technology and improved Canny edge detection technology were adopted to design a non-contact two-dimensional clothing pattern contour extraction method to extract the outer contour, inner contour and inner-outer contour of paper patterns. The two-dimensional pattern contour extraction was based on the use of scanner or camera, and this technology is applicable to the garment manufacturing industry, advanced customization industry, and individual studios, with high practical application value.

      Employee efficiency prediction of garment production line based on machine learning
      JU Yu, WANG Zhaohui, LI Boyi, YE Qinwen
      Journal of Textile Research. 2024, 45(05):  183-192.  doi:10.13475/j.fzxb.20230601001
      Abstract ( 102 )   HTML ( 8 )   PDF (4058KB) ( 43 )   Save
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      Objective The significant impact of variations in employee productivity on the balance of apparel production lines has prompted the need for a solution to address the shortfall in achieving targeted productivity levels under manually scheduled operations lacking historical data analysis support. This research aims to utilize machine learning models to predict actual employee efficiency, providing management with valuable insights for goal setting and decision-making to enhance production profitability and prevent erroneous decisions to some extent.

      Method In order to achieve efficiency prediction, this research conducted on-site surveys at factory A, gathering 526 historical production records from 13 orders. Through feature engineering, 15 initial prediction datasets were constructed, and efficiency levels were categorized using quantile division. Subsequently, considering the production data characteristics, RandomForest regression and classification models were selected for efficiency prediction. In order to validate the predictive performance of the model, it was compared with eight other models. Pearson and Spearman correlation coefficient analyses were performed to investigate the impact of variables on the model predictions. Finally, recursive feature elimination was employed to optimize the model by selecting the optimal feature subset from the initial feature set for maximum predictive performance.

      Results Using a random split function, 20% of the prediction dataset was set aside for validation, while the remaining 80% was divided into training and testing sets for ten-fold cross-validation. R2 and RMSE were chosen as regression metrics, and F1 score was selected as the classification metric. The RandomForest regression model demonstrated the optimal predictive performance, showing the smallest range of fit and root mean square error in ten-fold cross-validation, with a fitting goodness value of 0.826 and an RMSE value of 0.126. In the classification task, the random forest model exhibited higher predictive performance compared to most models, with a balanced F1 score of 0.809 in the validation set, slightly lower than the gradient boosting classification model. Prior to model optimization, correlation coefficient and feature importance analyses revealed the crucial role of the auxiliary variable "annual efficiency" in predictions. Based on variable analysis, recursive feature elimination was employed to select the optimal feature parameter set for both the RandomForest regression and classification models. In the regression task, the RandomForest model achieved the optimal parameter combination with eight features, yielding a validation set R2 value of 0.836. In the classification task, the growth curve of the random forest model's predictive performance was relatively gradual, using nine features to form the optimal parameter combination, resulting in a validation F1 score of 0.823. In the optimization results, setting the threshold for the difference between RandomForestRegressor predictions and actual results to 30% identified only three outliers, accounting for 3.16% of the data. For the RandomForestClassifier model, the classification results indicated a very low recall rate for sample 3, contributing to the relatively lower F1 score.

      Conclusion Through comparative experiments on predictive performance, the RandomForest model was selected as the optimal optimization model. Recursive feature elimination was chosen for model optimization based on the analysis of variable impacts on efficiency prediction. The results demonstrate that machine learning can accurately predict employee efficiency. Due to limitations imposed by the experimental factory, parameter collection was restricted. Future efficiency prediction research could consider adding more feature parameters to enhance model generalization. Additionally, considering the influence of time series, recurrent neural networks (RNNs) could be employed for modeling production efficiency prediction. In the future, we will continue to optimize this predictive model and apply it to the scheduling and arrangement of actual apparel assembly line workers.

      Machinery & Equipment
      Design of defect detection system for glass fiber plied yarn based on machine vision
      YANG Jinpeng, JING Junfeng, LI Jiguo, WANG Yuanbo
      Journal of Textile Research. 2024, 45(05):  193-201.  doi:10.13475/j.fzxb.20230102201
      Abstract ( 78 )   HTML ( 9 )   PDF (8217KB) ( 29 )   Save
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      Objective It is important that tension abnormalities and yarn hairiness can be detected accurately and efficiently during the production of glass fiber plied yarns as a type of raw material for electronic fabric production. Manual inspection has disadvantages such as low efficiency, high leakage rate and long lag time. Therefore, a machine vision-based method for detecting defects in plied yarns is proposed to meet the need for accurate detection of defects in real time during plied yarn production.

      Method The conventional algorithm pre-processes the image with threshold segmentation, open operation and contour extraction, and makes a preliminary judgement on the image by calculating whether the limits and heights of the contours are within the normal range, while the deep learning algorithm uses YOLOv5 and is optimized and accelerated using the TensorRT framework to make a secondary judgement on the image and locate defects. The proposed system used a Jetson Nano B01 as the hardware platform, an industrial camera to capture images of the plied yarn in real time, and a relay to control the winder and alarm light circuit.

      Results In this research, the image size of the training data set was 1 280 pixel×288 pixel, and the types of defects were divided into two categories, namely uneven tension and hairiness, according to the actual requirements. The proportion of samples used in the training set, validation set and test set were 70%, 15% and 15%, respectively. The training was conducted using YOLOv5 weights, with a batch size of 32 samples, where the image size was adjusted to 640 pixel×640 pixel, and the number of processes set to 2, for a total of 300 iterations. Training and testing were conducted on a deep learning server with a primary configuration of an Intel Core(TM) i9-10900X CPU 3.70 GHz, a 24 GB GPU GeForce RTX 3090 graphics card, and 128 GB of running memory. The test results showed that pre-processing using the conventional algorithm had the advantage of higher speed and low loss, detecting much faster than using the deep learning network alone, significantly reducing the amount of network computation and increasing the detection efficiency of the system. The use of deep learning algorithms for secondary determination had the advantage of high accuracy and defect localization, effectively avoiding false detection caused by using traditional algorithms alone and improving detection accuracy and production efficiency. In the production of plied yarns, considering the extremely high production speed and the need for product quality, the accuracy of detection, the miss detection rate and the detection processing time were used as indicators, and plied yarn samples that were normal and free of defects as well as those containing tension irregularities and hairiness were selected for testing. The experimental results showed that the system achieved 99.07% detection accuracy and 1.4% leakage rate. The camera acquisition yarn processed one frame at an average of 0.007 s, the algorithm detection yarn processed one frame at an average of 0.005 6 s and the subsequent processing yarn processed one frame at an average of 0.004 9 s. The camera acquisition and detection processing speed was above 112 frames per second, which meets the actual production inspection needs and effectively improves the inspection efficiency and facilitates the automatic detection of plied yarn defects.

      Conclusion The system is based on the Jetson Nano B01 as the hardware processing platform, and uses a combination of conventional algorithms and deep learning algorithms for detection. The system takes the advantages of fast processing speed of conventional algorithms for image pre-processing and preliminary judgement, and using the advantages of high accuracy of deep learning algorithms for secondary judgement when the conventional algorithms think there is a defect. It overcomes the shortcomings of the conventional algorithm's poor defect location ability and the deep learning algorithm's slow detection speed, while ensuring detection speed and accuracy. The Tkinter human-machine interface and the logging module provide the necessary functions for industrial sites. The system meets the need for real-time defect detection during the production of plied yarns, improving production efficiency and product quality.

      Drive method and control technology of magnetic levitation needle
      XIONG Tao, LI Chengyuan, GUI Shun, WANG Yi, ZHANG Chengjun, ZUO Xiaoyan
      Journal of Textile Research. 2024, 45(05):  202-208.  doi:10.13475/j.fzxb.20221005901
      Abstract ( 60 )   HTML ( 4 )   PDF (3925KB) ( 21 )   Save
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      Objective In order to improve the process flow of magnetic levitation flat knitting machine, and to realize the transformation from pattern design to needle knitting action, this paper proposes a technology of magnetic levitation flat knitting machine. Compared with the conventional needle driving method, the magnetic levitation flat knitting machine uses a new weft knitting machine technology that alleviates the problems of noise, heat and needle breakage. The magnetic levitation flat knitting machine has a special driving structure, which requires a corresponding drive solution and process knitting flow.

      Method Based on the working principle of the hybrid magnetic levitation flat knitting machine system and the special driving structure, a stepper type magnetic levitation needle drive solution was designed. A mathematical matrix method was adcpted to model the pattern grid of two-color single-layer jacquard fabric on a magnetic levitation knitting flat knitting machine. Finally, the experimental platform for needle driving was built to verify the feasibility of the stepper drive scheme, and the simulation software was adcpted to ensure the accuracy of the pattern grid conversion algorithm.

      Results The magnetic levitation flat knitting machine is a weft knitting machine that applies magnetic levitation technology to the knitting driving structure. According to the stepper magnetic needle driving scheme, eight coils in the head work together to complete the knitting motion of the needle. Each coil has a different current signal to raise the needle to a different knitting height. Mathematical form is considered to define the pattern grid of the two-color single-jersey jacquard fabric, then the mathematical matrix method is adopted to convert the pattern of the pattern grid into the form of data matrix, and the data conversion algorithm is obtained which can transform the flower matrix into the braided matrix. According to the weaving method of two-color single-jersey jacquard fabric, the design pattern of pattern grid is converted into weaving action data by mathematical algorithm. The current signal corresponding to the knitting action of the needle is defined according to the drive scheme of the stepper maglev needle, which allows the design of a two-color single-jersey jacquard fabric pattern to be converted into a current signal for knitting on a magnetic levitation flat knitting machine. It was determined that a stepper drive solution would allow the needle to gradually reach the process knitting height through the construction of the experimental platform for the needle drive. The simulation software is adopted to build the design interface of the grid pattern and the data conversion algorithm is adopted to gradually convert the grid pattern into the form of data. The simulation software shows that the two-color single-jersey jacquard conversion algorithm accurately converts the pattern data of the pattern grid into needle-driven weave data.

      Conclusion the proposed stepping magnetic levitation needle drive scheme and pattern grid data conversion algorithm completes the knitting process of the magnetic levitation flat knitting machine, and different current signals will be adopted to represent the different knitting motions of the needles. The algorithm converts the two-color single-jersey jacquard design pattern into weave data, and each weave data represents the weave action of each needle. At the same time, it completes the two-color single-jersey jacquard fabric process from grid pattern design to magnetic levitation flat knitting machine, which can allow the magnetic levitation flat knitting machine to weave two-color single-jersey jacquard fabrics with greater ease. Therefore, the feasible route is explored for the process design of magnetic levitation flat knitting machine, which simplifies the process flow of two-color single-jersey jacquard fabric by magnetic levitation flat knitting machine.

      Model and system construction of non-contact fabric stack separation
      MA Zihong, CHEN Huimin, DING Mengmeng, YUE Xiaoli
      Journal of Textile Research. 2024, 45(05):  209-217.  doi:10.13475/j.fzxb.20230505401
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      Objective In the process of garment production, fabrics need to go through the processes of cutting, sewing, and ironing, and the garment production heavily relies on the manual transfer of fabric cuts, which is time-consuming and labor-intensive. Due to the lightweight, softness, permeability and other characteristics of the fabrics, the automatic fabric stack separation becomes a recognized technical problem, which restricts the automatic transformation and upgrading of the material flow link in the garment production line.

      Method Aimed at fabric stack separation, the permeability, drape deformation, and electrostatic properties of the fabrics in the stack were characterized using theories of porous media, elastic thin plate bending deformation, and Coulomb's law. Subsequently, a mathematical model for fabric stack separation was established. In ordert validate the reliability and effectiveness of this model, an experimental platform for fabric stack separation was constructed.

      Results The experimental platform for fabrics stack separation was constructed by considering the Bernoulli suction cup suction force control system and the Bernoulli suction cup posture control system. This study analyzed the process of layered suction on the fabrics stack using Bernoulli suction cup and identified the main influencing factors, which include 1) atmospheric pressure loss caused by fabric permeability, 2) the change in spacing height h caused by fabric drape deformation, and 3) interlayer electrostatic forces for fabrics that are prone to static electricity, this is something that was not taken into account by the general model constructed by previous researchers. Based on these, a pressure field calculation model that is more in line with the non-contact suction of preamthable soft materials such as fabrics, as well as a suction force model were constructed. The ctrape deformation analysis models for grey fabric and denim under the non-contact suction based on the elastic thin plate theory were found to better express their respective ctrape deformation. Comparison between the measured and theoretical values of deflection suggested a 4.9% error, and the deviation between the suction theoretical model and the general model is 26.17%. The suction process parameters of Bernoulli suction cups mainly included inlet flow rate Q and spacing height h. When the spacing height h was kept constant, the suction force demonstrated an increase with the increase of inlet flow rate Q. On the other hand, when the intake flow rate Q remained constant, the suction force was increased with the increase of spacing height h. The success rate of stack separation using the suction model calculation results reach as high as 93%.

      Conclusion The deviation between the suction theoretical model and the general model is 26.17%, which compensates for the deficiency of the general model's suction capacity and can provide more accurate theoretical guidance for the process parameters required for fabric stack separation. The proposed suction model can accurately predict the changes in suction force, and the calculated results of the model differ by 9.4% from the experimental results. The success rate of stack separation using the suction model calculation results can reach 93%, and the proposed suction model and system have high effectiveness and reliability.

      Comprehensive Review
      Review on preparation of electrospun chitosan-based nanofibers and their application in water treatment
      FENG Ying, YU Hanzhe, ZHANG Hong, LI Kexin, MA Biao, DONG Xin, ZHANG Jianwei
      Journal of Textile Research. 2024, 45(05):  218-227.  doi:10.13475/j.fzxb.20221203002
      Abstract ( 126 )   HTML ( 23 )   PDF (3433KB) ( 90 )   Save
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      Significance Chitosan is a natural polymeric alkaline polysaccharide derived from a wide range of sources, and its molecular chain is rich of reactive groups, which can be used as adsorbent in the field of water treatment. However, conventional chitosan adsorbents have the disadvantages of small specific surface area, poor stability and difficulties in secondary recovery, resulting in low adsorption rate and poor economic efficiency, which seriously limits the industrial applications. Chitosan nanofibers are functional biomass regeneration fibers with large specific surface area, high porosity, flexible surface function and certain strength prepared by a series of spinning methods with chitosan as the main component, and fibrillation of chitosan can significantly eliminate the defects in the conventional chitosan adsorbents. Fibers can be formed by various techniques such as electrostatic spinning, wet spinning and chemical vapor deposition spinning, among which electrostatic spinning is the most common method for preparing chitosan-based nanofibers with uniform morphology. This paper presents a review of domestic and international studies on the preparation of chitosan-based nanofibers using electrostatic spinning technology, aiming to provide guidance for improving the spinnability of chitosan and the physical morphology and mechanical properties of chitosan-based nanofibers.

      Progress In order to enhance the spinnability of chitosan and improve the physical morphology and chemical properties of chitosan-based nanofibers, researchers have carried out a lot of studies in the aspect of preparing chitosan nanofibers using electrostatic spinning technology, and found that the parameters of spinning liquid and process parameters of electrostatic spinning device are the important factors determining the properties of nanofibers. First of all, only the spinning solution with good viscosity and conductivity can make chitosan nanofibers with uniform diameter and good mechanical properties by electrostatic spinning technology. In recent years, researchers have prepared ideal spinning solution by modifying chitosan through cross-linking, grafting and derivatization, but this still falls short of the standard for industrial application. Researchers have used natural/synthetic polymers to further enhance the viscosity and conductivity of the spinning solution, but synthetic polymers such as polylactic acid, polycaprolactone, polyurethane and other synthetic polymers have a certain degree of toxicity leading to the final production of fibers with a limited range of applications, while natural polymers such as cellulose, collagen and so on, have become a hotspot of the research on the preparation of excellent chitosan spinning solution in recent years because of their non-toxic and non-hazardous advantages. Secondly, in addition to the preparation of spinning solution with good viscosity and conductivity, suitable process parameters are also important prerequisites for the preparation of excellent chitosan nanofibers. For example, the appropriate voltage value in the electrostatic spinning process is an important guarantee to ensure that the fibers have good morphology and excellent performance, and it is found that the fiber diameter decreases with the increase of voltage, but the fiber diameter starts to increase when the voltage is higher than the critical range. Finally, this paper summarizes the effectiveness of chitosan-based nanofibers as adsorbents for the treatment of heavy metal ions such as Ni2+, Cu2+, Cr6+ and U6+ and dyes such as Congo Red, methylene blue and carmine in wastewater, and finds that the resulting fibers can be used for the simultaneous adsorption of a variety of heavy metal ions, anionic and cationic dyes as the spinning technology improves, and elucidates the repetitive regeneration properties of chitosan-based nanofibers in the adsorption of different pollutants.

      Conclusion and Prospect Chitosan-based nanofiber is a new type of adsorbent material with the advantages of easy separation, large specific surface area and flexible surface function, which can effectively improve the economic efficiency and avoid secondary pollution, and it is of great significance to help the early realization of "double carbon". Chitosan fibrillation based on electrostatic spinning technology can be divided into two steps: preparing of spinning solution and spinning formation. The preparation of spinning solution by dissolving chitosan in acid is the first step to enable chitosan spinnable, and changes in parameters such as spinning solution, process and environment during spinning formation ultimately change fiber morphology by affecting the ease of jet stretching. In addition, modification methods such as cross-linking, graft copolymerization, derivatization and blending can not only improve the spinnability of chitosan, but also enhance the acid resistance, thermal stability, antibacterial properties and adsorption of chitosan-based nanofibers. In the co-blending spinning process, the electrostatic interaction between chitosan and natural/synthetic polymers and the entanglement resulting from the reaction of different groups can improve the spinnability of chitosan. The search for new green, non-toxic and post-treatment-free solvents in the preparation of spinning solution, the search for new natural/synthetic polymers as co-spinning agents for improving chitosan spinnability during spinning and forming, and the use of multi-template molecular imprinting technology to enhance the adsorption for contaminants are the future trends of chitosan-based nanofibers.

      Research progress on applications of machine learning in flexible strain sensors in context of material intelligence
      LU Yan, HONG Yan, FANG Jian
      Journal of Textile Research. 2024, 45(05):  228-238.  doi:10.13475/j.fzxb.20221105502
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      Significance Because of the rapid progress and growth of smart materials and smart textiles, increasing attention hasbeen focused on the research, development, and optimization of flexible strain sensors. Flexible strain sensors for smart textiles are capable of detecting the precise motion trajectory of the human body, mechanical-acoustic characteristics, and information on various physiological indicators. With the continuous optimization of the performance of flexible strain sensors, the flexible sensor devices need to achieve the acquisition and analysis of high-dimensional and high-frequency complex superimposed signals in very complex application environments, which in turn puts forward higher requirements for data processing algorithms. The implementation of machine learning, a more advanced method, has significantly contributed to the improvement in the overall performance of the flexible strain sensing system. This paper presents a systematic review of the research progress of flexible strain sensors based on smart textiles combined with machine learning. The goal of the review is to understand and broaden the application of machine learning in the field of flexible strain sensors.

      Progress This paper firstly made an in-depth analysis of the fundamental structure and previous research on a variety of conventional flexible strain sensors such as piezoresistive, piezoelectric, capacitive, optical, magnetic, and triboelectric. In addition, this paper introduced the workflow of machine learning, which can be divided into the following four main steps: data preprocessing, machine learning and model training, model evaluation, and prediction of new data. According to the learning method, machine learning can be classified into supervised learning, unsupervised learning, reinforcement learning, and a mixture of the above three types. This paper then paper provided a detailed description of the information processing process of flexible strain sensors based on machine learning, as well as summarized the advantages and disadvantages of some typical machine learning algorithms for time-frequency analysis, dimensionality reduction, and classification. Furthermore, this paper analyzed the most recent research on flexible strain sensors based on smart textiles combined with machine learning in the fields of healthcare, life assistance, communication and exchange, as well as teaching and entertainment, which placed a significant amount of emphasis on the benefits that can be gained from utilizing machine learning in flexible strain sensors. In the field of healthcare, flexible strain sensing systems can continuously track various mechanical and acoustic features of the human body by combining with specific machine learning algorithms, which can help users to understand their own health status in real time, and thus achieve the purpose of health monitoring. Secondly, in the field of life assistance, the large amount of information provided by the machine learning-based strain sensing system can help in the design of bionic hearing, touch, and prosthetic manipulator, which can greatly improve the convenience of life for the disabled and the blind. Moreover, free-life monitoring by flexible strain sensing systems has the potential advantage of accurately detecting and measuring clinically relevant features, including fall risk and abnormal gait, so that abnormal movement symptoms of the elderly can be detected in a timely manner, which can ensure the safety of the elderly's life to a considerable extent. In the field of communication and exchange, the application of flexible strain sensors based on machine learning can improve the recognition performance of various features, such as sign language recognition, micro-expression detection, and perceptual interaction, thus facilitating human-to-human communication. In addition, the strain sensing system combined with specific machine learning algorithms enriches the application of smart textiles in teaching and entertainment scenarios, which improves the teaching efficiency and enhances the fun of teaching at the same time, and the application in gaming and entertainment greatly enriches people's lives.

      Conclusion and Prospect Flexible strain sensors have excellent characteristics, such as high sensitivity, high resolution, and good elasticity. With the help of new sensor structures, new sensitive materials, and cutting-edge machine learning algorithms, smart textiles have been of great value in a variety of different fields. However, in the context of material intelligence, the research on flexible strain sensors based on smart textiles is still in its infancy and still faces many challenges, such as the fact that researchers have carried out little research on the optimal design of flexible strain sensor arrays, that it is difficult to simulate real human touch with flexible strain sensors designed according to existing technologies, and that the process of human pose recognition with flexible strain sensor systems can easily cause confusion in the recognition system. In a word, there is no doubt that machine learning has evolved into a valuable tool in the realm of smart wearables. It is believed that in the near future, with the continuous development of computer science and computing methods, machine learning will play a huge application value in various aspects such as the research and development of smart textile materials, process improvement, device performance evaluation, signal transmission, data processing, etc., and will further promote the intelligent development of the whole material field.

      Advances in application of soft robot in apparel field
      WANG Jianping, ZHU Yanxi, SHEN Jinzhu, ZHANG Fan, YAO Xiaofeng, YU Zhuoling
      Journal of Textile Research. 2024, 45(05):  239-247.  doi:10.13475/j.fzxb.20230500702
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      Significance With the continuous progress of robotics and automatic control technology, robotics has been widely used in various fields such as medical treatment agriculture, and industry. China is a large producer of industrial robots, but the application of robotics in the apparel field is seriously lagging behind. Therefore, it is imperative to promote the combination of robotics with the apparel industry and enhance its application in automated apparel manufacturing and intelligent apparel. Soft robots are made from deformable materials, which have the advantages of high flexibility and adaptability compared with rigid robots and have now become a research hotspot in the field of robotics. The use of flexible materials enables soft robots to safely collaborate with users, which meets the requirement of co-integration in the apparel field and has great potential in accelerating the process of apparel intelligence.

      Progress This paper reviews the research progress of soft robotics in the apparel field. The paper starts by focusing on the key technology of soft robot. Research status is summarized in four aspects, which are manufacturing materials, manufacturing methods, driving methods, control and modeling. The different driving methods are widely used in textile grasping and transferring, and medical-assisted garments, respectively. Among them, the soft body gripper represented by gas drive shows excellent application prospects in textile fabric gripping and transfer, and the combined gripper and multi-point layout model further simplifies the automated clothing transfer system. The soft robotic garments are divided into upper limb assisted garments and lower limb assisted garments. Hand function rehabilitation gloves in upper limb assistive devices mainly enhance hand muscle strength with the help of pneumatic artificial muscle or tendon drive. The other parts of the upper limb and lower limb assisted flexible robot garments are employed to reduce metabolic costs so as to improve motor performance by means of shape memory alloy fabric muscles, unpowered exoskeleton devices, and so on. It is also pointed out that the development of garment-assisted strategies should focus on the importance of the human-machine system.

      Conclusion and Prospect In oder to address the shortcomings in the existing research, the driving method can be optimized with the help of smart materials, and the sensing and control elements can be reduced in combination with micromachining technology to improve the soft robot manufacturing efficiency and precise control. By analyzing the textile fabric characteristics, the accuracy of textile fabric gripping and dropping, and improve the versatility of fabric gripper to face the complicated fabric handling process are proposed for improvement. The research and development of intelligent garments should adhere to the principle of "human-centered" and optimize the performance of robot-assisted devices with the help of "human-in-the-loop" approach. The research of soft robots is still in its infant stage, and its use in the apparel field is of profound interdisciplinary and system complexity. It is necessary to further explore the industrial model of apparel smart manufacturing, and to integrate soft robotics with the apparel industry based on human needs.

      Research progress in three-dimensional garment virtual display technology
      CHENG Bilian, JIANG Gaoming, LI Bingxian
      Journal of Textile Research. 2024, 45(05):  248-257.  doi:10.13475/j.fzxb.20221106202
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      Significance The virtual display tehnology of three-dimensional (3-D) clothing is utilized to simulate the clothing state and deformation phenomenon of different human bodies in different postures and various activities. It moves away from the conventional real-life fitting method and can display clothing statically or dynamically in a virtual environment. The wide application of 3-D garment virtual display in textile and garment CAD software can produce the simulation effect of flexible fabric more real, and help designers realize the design and development of visual textiles. The virtual display technology of 3-D clothing is applied to the online game and animation industry, which enables the clothing effect of virtual characters closer to reality. Under the environment of the rapid development of clothing e-commerce, the application of 3-D clothing virtual display technology in the field of clothing e-commerce will help users quickly choose the right model of clothing. This technology can significantly affect the effect of consumer purchase wishes and reduce the amount of returns, so as to improve the business efficiency and promote the commercial development of the clothing industry.

      Progress 3-D virtual clothing display involves the integration of technology in many disciplines, committed to produce realistic and dynamic display. By systematically introducing the 3-D human modeling technology, the development status of parametric human modeling and non-parametric human modeling is analyzed to provide a basis for the development of 3-D human modeling. The research process of clothing modeling is described in detail from three methods of geometric modeling, physical modeling and hybrid modeling in clothing modeling. The research and exploration of scholars at home and abroad for many years are analyzed, and the development process of 3-D virtual display technology from single static simulation to dynamic simulation with physical attributes is summarized. The advantages and disadvantages of the existing achievements of clothing simulation technology and clothing animation simulation technology are summarized. For any new human motion, a reliable deformation distribution prediction can be given to effectively adjust the fabric mesh. The prediction results of the multi-precision cloth model have high reliability and can be used for further dynamic adaptation of cloth mesh in animation.

      Conclusion and Prospect 3-D human body and clothing modeling are widely used in the fields of textile and garment CAD software, personalized entertainment, animation design and e-commerce. However, there are still some shortcomings such as high computational cost and insufficient simulation accuracy. Therefore, it is urgent to further develop 3-D human body modeling and virtual clothing simulation. Studying the 3-D virtual display of clothing will have deep theoretical value and practical application significance. This paper analyzes the parametric method of human body modeling and non-parametric method of human body modeling development present situation, elaborated the clothing modeling in the geometric modeling method, physical modeling method and hybrid modeling method of the research process, the development of 3-D virtual display technology has been developed from a single static simulation to the dynamic simulation of physical properties, static 3-D dressing model has the advantages of high simulation accuracy and good stability, dynamic clothing animation simulation can vividly show the overall effect of clothing on the human body, but it needs huge computational cost and memory reserves, simulation accuracy needs to be improved. Therefore, the modeling technology, interactive technology, machine learning and other related technologies involved in clothing dress simulation and clothing animation simulation still have great room for improvement and research value, which is worthy of further exploration and exploration. There are three main research trends and difficulties in the future research of 3-D virtual display technology: a) research on fast, low-cost and accurate 3-D human model reconstruction method, including 3-D posture and human geometry model; b) 3-D dress simulation, that is, quickly and stably try on clothing to different body shapes and postures; c) realistic dynamic try-on effects, including fast, low-cost human motion capture and efficient clothing animation simulation technology.

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