Optimization of garment sewing operation standard minute value prediction using an IPSO-BP neural network

Abstract

Abstract Standard minute value serves as a pivotal metric guiding the arrangement and balancing of production cycles in clothing production lines, and plays a crucial role in cost pricing and production order arrangement for clothing products. Given the complexity of the garment sewing process, ten influencing factors including fabric weight, fabric thickness, fabric density, stitching length, stitching shapes, cut pieces numbers, notch numbers, sewing technologies, sewing machine, and auxiliary accessories were identified. Upon this foundation, a standard sewing time prediction model, Improved particle swarm optimization - Back-propagation neural network (IPSO-BP), was proposed, focusing on non-quantitative factors. The IPSO-BP model was trained using actual sewing data from a women’s clothing production company. Compared to the unoptimized BP neural network, the IPSO-BP model demonstrated significant advantages in terms of convergence speed and prediction accuracy. Therefore, the IPSO-BP model proposed in this study holds promise for predicting standard sewing hours effectively.