Multiobjective optimization of injection molding using a calibrated predictor based on physical and simulated data |
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| Authors: | María G Villarreal‐Marroquín Po‐Hsu Chen Rachmat Mulyana Thomas J Santner Angela M Dean José M Castro |
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| Affiliation: | 1. Centro de Investigación en Matemáticas, CONACYT, Monterrey, NL, Mexico;2. Department of Statistics, The Ohio State University, Columbus, Ohio;3. Integrated Systems Engineering Department, The Ohio State University, Columbus, Ohio |
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| Abstract: | This paper presents a method for improving injection molding processes having competing performance measures using multiobjective optimization. The procedure uses calibrated predictors that combine physical and simulated data to estimate the values of the performance measures. After the predictors are built, the values of the selected performance measures are estimated at a grid of process control variables, and a set of predicted Pareto solutions is identified using nondominance criteria. A refinement of the original Pareto solutions is obtained by predicting the performance measures at a finer grid of the process variables near the original Pareto set. Finally, as validation, a subset of these solutions is evaluated on the physical process. A case study with three performance measures is presented to show how the calibrated predictors allow the injection molding manufacturer to identify the processing conditions that optimize a process having competing objectives. POLYM. ENG. SCI., 57:248–257, 2017. © 2016 Society of Plastics Engineers |
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