Interpretable Hybrid Surrogate Modeling for Diameter Deviation Prediction in Injection Molding Using Symbolic Regression and Gaussian Process Residual Correction
This study presents an interpretable hybrid surrogate model that combines symbolic regression with Gaussian Process residual correction to accurately predict diameter deviations in injection molding under limited experimental data, achieving improved accuracy and providing a transparent framework for process optimization.