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Optimization of LPDC Process Parameters Using the Combination of Artificial Neural Network and Genetic Algorithm Method

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Abstract

In this article, the low-pressure die-cast (LPDC) process parameters of aluminum alloy thin-walled component with permanent mold are optimized using a combining artificial neural network and genetic algorithm (ANN/GA) method. In this method, an ANN model combining learning vector quantization (LVQ) and back-propagation (BP) algorithm is proposed to map the complex relationship between process conditions and quality indexes of LPDC. The genetic algorithm is employed to optimize the process parameters with the fitness function based on the trained ANN model. Then, by applying the optimized parameters, a thin-walled component with 300 mm in length, 100 mm in width, and 1.5 mm in thickness is successfully prepared and no obvious defects such as shrinkage, gas porosity, distortion, and crack were found in the component. The results indicate that the combining ANN/GA method is an effective tool for the process optimization of LPDC, and they also provide valuable reference on choosing the right process parameters for LPDC thin-walled aluminum alloy casting.

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Acknowledgments

The authors gratefully acknowledge research support from Hunan Science Fund for Distinguished Young Scholars No. 09JJ1007, International Cooperation and Exchanges MOST No.2008DFA50990 and the Science Fund of State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body No. 60870005.

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Authors and Affiliations

  1. State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha, 410082, Hunan, China

    Liqiang Zhang & Luoxing Li

  2. College of Materials Science Engineering, Hunan University, Changsha, 410082, Hunan, China

    Liqiang Zhang, Luoxing Li, Shiuping Wang & Biwu Zhu

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  1. Liqiang Zhang
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  2. Luoxing Li
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  3. Shiuping Wang
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  4. Biwu Zhu
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Correspondence to Luoxing Li.

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Zhang, L., Li, L., Wang, S. et al. Optimization of LPDC Process Parameters Using the Combination of Artificial Neural Network and Genetic Algorithm Method. J. of Materi Eng and Perform 21, 492–499 (2012). https://doi.org/10.1007/s11665-011-9933-0

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  • DOI: https://doi.org/10.1007/s11665-011-9933-0

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