Abstract:

Objective Speed skating suits are an important sport equipment, where the generation of efficient and good-fit patterns are a primary challenge in customization. In order to achieve the personalized pattern, a pattern generating method for speed skating suits is proposed based on the mass-spring system. The aim of the research is to determine precise pattern shapes according to individual body build. It is helpful and significant to improve fitness of the suits and to promote the pattern-making efficiency.

Method 3-D model of athlete's body shape was obtained by scanning, and 3-D mesh models of each part of the suit were extracted based on the body shape features of athlete and the structural line distribution of suits., and the mass-spring system models were then constructed based on 3-D meshing of suit parts. Multi-angle tensile tests were carried out on the elastic fabric used in the suits to obtain the mechanical and physical properties of the fabric, and the parameters of the mass-spring model were determined by fabric properties. 3-D models of suit parts were flattened into the 2-D plane, and the mass-spring models were repeatedly used in the 2-D plane under the given model parameters and constraints. After the garment CAD pattern adjustment, accessories supplement, cutting and sewing and other processing steps, the sample suits were manufactured.

Results In experiments, 3-D body models of one male athlete and one female athlete were acquired through scanning, and their main body shape parameters were obtained. Instron 3343 material testing machine was adopted to test the mechanical properties of the elastic fabrics and the data were analyzed aiming at the making of speed skating suits. The measured properties were employed to calculate the elastic modulus of the virtual springs in the mass-spring models, which were constructed by 3-D meshes of pattern pieces extracted from scanned human models. They were used in 3-D flattening to obtain the 2-D pattern pieces' shape. The models converged under the objective constraint of internal energy minimization, and the boundary shapes obtained were considered to be the 2-D pattern of the suit components. In a typical suit, 5 pieces including front, back, arm, upper leg and lower leg were extracted and flattened. Owing to symmetry, only the left parts were selected. The mean errors of 3-D flattening algorithm were limited in a low level from 3-D to 2-D flattening. The basic shapes of pieces were obtained, and then made a series of adjustments were performed to achieve the final pattern. Flattened pattern shapes from body poses of standing and skating action were compared and the results showed obvious shape differences, indicating that the material properties constrained 3-D flattening pattern-making method. Personal body shape as well as that in designated pose could be used directly to determine the optimal pattern shapes. Furthermore, comparisons have been conducted between sample suits of our algorithm-based pattern-making and conventional manual pattern-making. After the athletes' try-on and evaluate, the final sample suits were formed to enable the personalized pattern making. The experimental results showed that the measurement errors of the generated pattern were within the acceptable range, and the athletes' subjective try-on feedback suggested that the sample suits were fitting well.

Conclusion The method can effectively facilitate the personalized pattern generation of speed skating suits, so as to provide effective technical support and practical tools for personal customized pattern making of speed skating suits. The achieved twofold advantages of proposed method are. One is that the flattening-based algorithm is helpful to determine optimal pattern shapes directly and quickly according to personal human body. Another is that the method is semi-automatically implemented by programs to promote pattern-making efficiency. The main limitations are that the method relies on the accuracy of scanned human models and the pattern structure needs to be given beforehand. The future meaningful work includes parametric pattern-making and intelligent models based on a large number of pattern-body shape practices.

Key words: speed skating, suit, pattern making, personal, generating, mass-spring system