Abstract:

Objective For customized socks, such as medical socks and sports socks, the existing customized design techniques can only ensure the accuracy of key data for foot length and foot height, but cannot meet the accuracy requirements for the target user's foot that needs to be better fitted. In such cases, a trial-and-error excercise would be necessary to meet the needs of users after obtaining the parameters, which makes the hosiery customization process inefficient.

Method According to the actual knitting process based on the circular sock machine, the yarn starts from the top and is knitted circularly along the direction of the tube until the sock toe part is knitted. For the proposed technique, the sampling point also started from the top and was iteratively knitted along the sampling axis until the toe part was sampled. The model sampling axis was obtained as the first step. Since the model skeleton was represented the direction of the model, it was selected as the sampling axis. The Shapira L method was adopted and simplified as sampling axis extraction method. The model surface was iteratively sampled to obtained the knitting path graph including the coil size. In this research, the sock top ring was used as the initial sampling ring, the sampling axis was used as the sampling direction for vertical iterative sampling, and the coil height was adjusted during sampling according to the angle between the normal vector of the model surface point and the sampling axis. When calculating the next sampling row, if the number of vertices on the model surface was small, the sampling row would be uneven. Therefore, the weighted average method was used to smooth the sampling points. Since the upper and lower loops were essentially the same yarn during the knitting process of the circular socks machine, it was necessary to connect the sampling point at the end of the current row with the sampling point at the beginning of the next row during iterative sampling. When the position of the next sampling line exceeded the current sampling axis segment, the sampling axis segment was switched to a new sampling axis segment. Since the actual weaving process was cyclically knitted by a single yarn, the end point of the current sampling row was connected to the beginning point of the next sampling row during the sampling process.

Results In this research, the model of socks was selected, and the number of stitches input was 144 stitches. The knitting parameters were then calculated by the system for the WLF-6F fully formed circular sock machine for knitting. The needle number of the needle cylinder was set to 144 needles, and the yarn material was cotton yarn. The generated results from the model was compared with the dimensions of each part of the knitted socks, and the error was below 4.1%.

Conclusion Based on the idea of simulating knitting, The model created in this research can output the knitting path diagram that simulates the actual hosiery knitting process after inputting the hosiery model and the required number of stitches. After seelcting the hosiery model and specifying the number of stitches, the parameters required for knitting on the circular hosiery machine can be generated. The modeled sample socks are produced by knitting on the machine with good accuracy, improving the production design rate for customized hosiery.

Key words: simulated weaving, preparation of path diagram, iterative sampling, skeleton extraction, knitting parameter, custom hosiery, knitting with circular knitting machine