Development of an injection molding production condition inference system based on diffusion model

Abstract

Plastic injection molding is a crucial process for the mass production of various products. However, traditional methods for setting production conditions have heavily relied on skilled operators to adjust parameters through trial and error. This approach is not only inefficient but also results in inconsistent quality control. To address these challenges, this study proposes a new machine learning based model that automatically infers process parameters, enabling real time adaptation to external environmental changes. A surrogate model is first developed to learn the relationship between process parameters, environmental variables, and product quality, predicting whether a given set of parameters will result in a good or defective product. Building on this, a diffusion model, a type of deep generative model, was employed to generate diverse sets of process parameters likely to yield defect free products under specific environmental conditions. The proposed diffusion model outperforms existing generative models such as generative adversarial network (GAN) and variational autoencoder (VAE) in both accuracy and diversity of generated parameters. Notably, the diffusion model achieved an error rate of 1.63%, significantly outperforming GAN and VAE, which exhibited error rates of 23.42% and 44.54%, respectively. Additionally, the applicability of the proposed diffusion model was experimentally validated in a real world testbed. Several experiments conducted under various external environmental conditions demonstrated that the quality of the products produced using the process parameters generated by the diffusion model matched the quality predicted by the model. This study introduces a novel approach to improving both the efficiency and quality of injection molding processes and holds promise for broader applications in manufacturing.