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Single and Multi Objective Optimization for Injection Molding Using Numerical Simulation with Surrogate Models and Genetic Algorithms

  • J. Zhou , L.-S. Turng and A. Kramschuster
Published/Copyright: May 10, 2022
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Abstract

The objective of this study is to develop an integrated computer-aided engineering (CAE) optimization system that can quickly and intelligently determine the optimal process conditions for injection molding. This study employs support vector regression (SVR) to establish the surrogate model based on executions of three-dimensional (3D) simulation for a selected dataset using the latin hypercube sampling (LHS) technique. Once the surrogate model can satisfactorily capture the characteristics of simulations with much less computing resources, a hybrid optimization genetic algorithm (GA) or a multi-objective optimization GA is then used to evaluate the surrogate model to search the global optimal solutions for the single or multiple objectives, respectively. The performance and capabilities of other surrogate modeling approaches, such as polynomial regression (PR) and artificial neural network (ANN), are also investigated in terms of accuracy, robustness, efficiency, and requirements for training samples. Experimental validations and applications of this work for process optimization of a special box mold and a precision optical lens are presented.


* Mail address: L.-S. Turng, Polymer Engineering Center, Department of Mechanical Engineering, University of Wisconsin-Madison, 1513 University Avenue, Madision WI 53706, USA


Acknowledgements

The authors would like to thank Moldex3D for generously providing the computer simulation software for this study. This work was partially supported by the 3M Precision Optics and the National Science Foundation (DMI-0323509).

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Received: 2006-04-07
Accepted: 2006-07-13
Published Online: 2022-05-10

© 2006 Walter de Gruyter GmbH, Berlin/Boston, Germany

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