Domain-Knowledge-Guided Candidate Generation for Surrogate-Based Injection Molding Optimization

Injection molding (IM) quality optimization involves coupled process variables, nonlinear quality responses, and a limited budget for physical trials. Numerical optimizers can use measured process data effectively, but they do not directly represent troubleshooting rules and other experience-based knowledge that is recorded in text. This study proposes large language model-based extended data-driven optimization (LLM-EDO), a domain-knowledge-guided framework for surrogate-based candidate generation. The framework converts process variables, quality curve discrepancies, feasible bounds, and retrieved expert cases into a structured request. A language model then proposes a bounded parameter vector, and a fixed long short-term memory surrogate evaluates the resulting quality curve. The framework is examined in a surrogate-in-the-loop environment using repeated and multi-start tests and is compared with Bayesian optimization, simultaneous perturbation stochastic approximation, and knowledge-informed gradient-based SPSA. The reported runs show reductions in curve distance for the tested initial configurations and competitive terminal objective values. They also show run-to-run variability, and iteration counts are reported as decision rounds rather than wall-clock measurements. The results support the feasibility of domain-knowledge-guided candidate generation for surrogate-based quality curve matching under the tested process configurations.