Multi-objective optimization of injection molding process using interpretable extreme gradient boosting model based on improved north eagle optimization algorithm
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,...
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| Published in: | Journal of mechanical science and technology Vol. 39; no. 10; pp. 6171 - 6180 |
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| Main Authors: | , , , , , |
| Format: | Journal Article |
| Language: | English |
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Seoul
Korean Society of Mechanical Engineers
01.10.2025
Springer Nature B.V 대한기계학회 |
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| ISSN: | 1738-494X, 1976-3824 |
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| Abstract | 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. |
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| AbstractList | 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. 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 multiobjective 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. KCI Citation Count: 0 |
| Author | Guo, Yonghuan Fan, Xiying Hua, Junyi Li, Lie Wang, Zhijiang Zhu, Liuyu |
| Author_xml | – sequence: 1 givenname: Liuyu surname: Zhu fullname: Zhu, Liuyu organization: School of Mechanical Engineering, Jiangsu Normal University – sequence: 2 givenname: Xiying surname: Fan fullname: Fan, Xiying email: fxy8441@163.com organization: School of Mechanical Engineering, Jiangsu Normal University – sequence: 3 givenname: Yonghuan surname: Guo fullname: Guo, Yonghuan organization: School of Mechanical Engineering, Jiangsu Normal University – sequence: 4 givenname: Zhijiang surname: Wang fullname: Wang, Zhijiang organization: School of Mechanical Engineering, Jiangsu Normal University – sequence: 5 givenname: Junyi surname: Hua fullname: Hua, Junyi organization: School of Mechanical Engineering, Jiangsu Normal University – sequence: 6 givenname: Lie surname: Li fullname: Li, Lie organization: School of Mechanical Engineering, Jiangsu Normal University |
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| SubjectTerms | Accuracy Control Cooling Datasets Deformation Dimensional stability Dynamical Systems Engineering Housings Industrial and Production Engineering Injection molding Integrated approach Mechanical Engineering Multiple objective analysis Optimization algorithms Original Article Pareto optimization Process parameters Science Sensors Simulation Support vector machines Vibration Warpage 기계공학 |
| Title | Multi-objective optimization of injection molding process using interpretable extreme gradient boosting model based on improved north eagle optimization algorithm |
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