Explainable artificial intelligence and multi-stage transfer learning for injection molding quality prediction

High-precision optical products made of polymeric materials have been surging in recent years due to the prevalence of smartphones and their camera modules. Manufacturing fast-changing generations of high-precision optical lenses with accurately predicted qualities is a challenging task. Simulations and modern artificial intelligence (AI) techniques play crucial roles in accelerating precise process development. Coupled with computer simulation, this research employs a fusion of explainable AI (XAI) and multi-stage transfer learning (TL) approaches with artificial neural network (ANN) models to predict the surface profile variation of injection-molded polycarbonate (PC) lenses. The proposed method efficiently bridges preliminary simulations to injection molding experiments, covering a complete process development workflow from feature selection, process modeling, to experimental investigation in the same modeling domain. Only one model from scratch is required, which carries knowledge to the final quality prediction model. When compared with the conventional TL and the na & iuml;ve model, the multi-stage TL approach provides better predictions with a maximum reduction of 64% and 43% in simulation and actual manufacturing data requirement, respectively. This research demonstrates a viable connection between each stage in the injection molding (IM) process development in predicting the qualities of high-precision optical lenses. Meanwhile, the combined usage of XAI and (multi-stage) TL confirms model explanations and pinpoints a potential pathway to assess future TL capabilities from the modeling perspectives.