Multi-objective optimization of injection molding process using interpretable extreme gradient boosting model based on improved north eagle optimization algorithm - Zhu, Liuyu; Fan, Xiying; Guo, Yonghuan; Wang, Zhijiang; Hua, Junyi; Li, Lie (Journal of mechanical science and technology, 2025)
During the injection molding of sensor housings, plastic parts often suffer from warpage deformation and volumetric shrinkage. To address this, Moldflow simulation and central composite face (CCF) design were used to generate a dataset. An improved northern goshawk optimization (INGO)-XGBoost model, enhanced by three strategies, was compared against NGO-XGBoost, AdaBoost-SVM, and AdaBoost-ELM models, demonstrating superior performance. SHAP analysis was applied to interpret the INGO-XGBoost model, and multi-objective multiverse optimization (MOMVO) was used to generate the Pareto front. The CRITIC-TOPSIS method was then employed to select the optimal process parameters. Results show that warpage deformation and volumetric shrinkage were reduced by 30.9 % and 8.7 %, respectively, compared to the initial settings. The proposed integrated prediction–optimization framework significantly improves the molding quality and dimensional stability of plastic parts, providing both theoretical support and a practical pathway for intelligent injection molding.