Mass spring parameters identification for knitted fabric simulation based on FAST testing and particle swarm optimization

In computer graphics, Mass-Spring model is used to obtain fast and visual results in physical simulations. A disadvantage of the method is to obtain accurate result because of the difficulty to define parameters of a Mass-Spring Model. Different works and results have been carried out to define model parameters. In this field, researchers have used optimization technique based on meta-heuristic method or applied fabric properties such as FAST and Kawabata test to determine model parameters. So far no research has been done using combination of two mentioned methods to recover mass spring parameters. Therefore; the purpose of this paper is to determine parameters of mass spring model applying Particle Swarm Optimization techniques and FAST test. For this point, the effective properties on fabric drape including stretch, shear, and bending properties are measured using the Fast System. Then, in order to reduce error value between simulated and actual fabric behavior, parameters of the mass spring model such as super elasticity rate, mesh topology and natural length of spring are optimized by applying the Particle Swarm Optimization (PSO). The PSO parameters are specified by using Taguchi Design of Experiment. Finally, fabrics drape are simulated in other situations and compared with its actual result to validate the model parameters. Results show that the optimized model is able to predict the drape behavior of knitted fabric with error value of 2.9 percent as compared with the real result.