Statistical learning-driven parameter tuning in injection molding using modified simplex method
The optimization of product parameters, including length and standard deviation, is a persistent challenge, despite the fact that injection molding is a critical manufacturing technique. Nonlinear interactions and quality inconsistencies are frequently encountered by existing methods. The Statistica...
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Elsevier
2025-09-01
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| Series: | Results in Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123025027574 |
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| author | Pongchanun Luangpaiboon Walailak Atthirawong Anucha Hirunwat Pasura Aungkulanon |
| author_facet | Pongchanun Luangpaiboon Walailak Atthirawong Anucha Hirunwat Pasura Aungkulanon |
| author_sort | Pongchanun Luangpaiboon |
| collection | DOAJ |
| description | The optimization of product parameters, including length and standard deviation, is a persistent challenge, despite the fact that injection molding is a critical manufacturing technique. Nonlinear interactions and quality inconsistencies are frequently encountered by existing methods. The Statistical Learning-Driven Modified Simplex Method (SLMSM) is a novel hybrid optimization framework that is introduced to resolve this gap. The SLMSM fine-tunes injection molding process (IMP) parameters for improved production consistency by combining the Modified Simplex Method (MSM) with regression-based statistical learning (SL). The Taguchi design of experiments and desirability function analysis were employed to establish a baseline. Subsequently, SLMSM was implemented to iteratively optimize process parameters, with an emphasis on the average product length and its variability. The results suggest that SLMSM effectively accomplishes target specifications while minimizing standard deviation. SLMSM addresses nonlinear, noisy objective functions with enhanced precision and fewer experimental trials by utilizing adaptive sampling and predictive modeling. Significant enhancements in length uniformity, reduced process variation, and alignment with client specifications were observed. Practical implications for manufacturing, such as enhanced product quality, cost savings, and reduced waste, are suggested by these results. Additionally, SLMSM exhibits adaptability to changing production environments, rendering it appropriate for a wider range of industrial applications. This research provides a data-driven framework for optimizing IMPs, thereby improving both operational efficiency and product reliability. The integration of real-time monitoring systems, the extension to other manufacturing domains, and the incorporation of advanced machine learning techniques for predictive quality control should be the focus of future research. |
| format | Article |
| id | doaj-art-0d38244dd35d4d2e9a419c9f5be9554b |
| institution | Kabale University |
| issn | 2590-1230 |
| language | English |
| publishDate | 2025-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj-art-0d38244dd35d4d2e9a419c9f5be9554b2025-08-20T03:36:39ZengElsevierResults in Engineering2590-12302025-09-012710669010.1016/j.rineng.2025.106690Statistical learning-driven parameter tuning in injection molding using modified simplex methodPongchanun Luangpaiboon0Walailak Atthirawong1Anucha Hirunwat2Pasura Aungkulanon3Industrial Statistics and Operational Research Unit (ISO-RU), Department of Industrial Engineering, Faculty of Engineering, Thammasat School of Engineering, Thammasat University, Pathumthani, 12120, ThailandH.S.M. Office of the Vorakij, Wattana District, Bangkok, 10110, ThailandDepartment of Materials Handling and Logistics Engineering, Faculty of Engineering, King Mongkut's University of Technology North Bangkok, Bangkok, 10800, Thailand.; Corresponding authors at: Department of Materials Handling and Logistics Engineering, Faculty of Engineering, King Mongkut's University of Technology North Bangkok, Bangkok, 10800, Thailand.Department of Materials Handling and Logistics Engineering, Faculty of Engineering, King Mongkut's University of Technology North Bangkok, Bangkok, 10800, Thailand; Corresponding authors at: Department of Materials Handling and Logistics Engineering, Faculty of Engineering, King Mongkut's University of Technology North Bangkok, Bangkok, 10800, Thailand.The optimization of product parameters, including length and standard deviation, is a persistent challenge, despite the fact that injection molding is a critical manufacturing technique. Nonlinear interactions and quality inconsistencies are frequently encountered by existing methods. The Statistical Learning-Driven Modified Simplex Method (SLMSM) is a novel hybrid optimization framework that is introduced to resolve this gap. The SLMSM fine-tunes injection molding process (IMP) parameters for improved production consistency by combining the Modified Simplex Method (MSM) with regression-based statistical learning (SL). The Taguchi design of experiments and desirability function analysis were employed to establish a baseline. Subsequently, SLMSM was implemented to iteratively optimize process parameters, with an emphasis on the average product length and its variability. The results suggest that SLMSM effectively accomplishes target specifications while minimizing standard deviation. SLMSM addresses nonlinear, noisy objective functions with enhanced precision and fewer experimental trials by utilizing adaptive sampling and predictive modeling. Significant enhancements in length uniformity, reduced process variation, and alignment with client specifications were observed. Practical implications for manufacturing, such as enhanced product quality, cost savings, and reduced waste, are suggested by these results. Additionally, SLMSM exhibits adaptability to changing production environments, rendering it appropriate for a wider range of industrial applications. This research provides a data-driven framework for optimizing IMPs, thereby improving both operational efficiency and product reliability. The integration of real-time monitoring systems, the extension to other manufacturing domains, and the incorporation of advanced machine learning techniques for predictive quality control should be the focus of future research.http://www.sciencedirect.com/science/article/pii/S2590123025027574Parameter designModified simplex methodDesirability functionRegression modelStatistical learning |
| spellingShingle | Pongchanun Luangpaiboon Walailak Atthirawong Anucha Hirunwat Pasura Aungkulanon Statistical learning-driven parameter tuning in injection molding using modified simplex method Results in Engineering Parameter design Modified simplex method Desirability function Regression model Statistical learning |
| title | Statistical learning-driven parameter tuning in injection molding using modified simplex method |
| title_full | Statistical learning-driven parameter tuning in injection molding using modified simplex method |
| title_fullStr | Statistical learning-driven parameter tuning in injection molding using modified simplex method |
| title_full_unstemmed | Statistical learning-driven parameter tuning in injection molding using modified simplex method |
| title_short | Statistical learning-driven parameter tuning in injection molding using modified simplex method |
| title_sort | statistical learning driven parameter tuning in injection molding using modified simplex method |
| topic | Parameter design Modified simplex method Desirability function Regression model Statistical learning |
| url | http://www.sciencedirect.com/science/article/pii/S2590123025027574 |
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