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An intelligent system for low-pressure die-cast process parameters optimization

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Abstract

Low-pressure die-cast (LPDC) is widely used in manufacturing thin-walled aluminum alloy products. Since the quality of LPDC parts are mostly influenced by process conditions, how to determine the optimum process conditions becomes the key to improve the part quality. In this paper, a combining artificial neural network and genetic algorithm (ANN/GA) method is proposed to optimize the LPDC process. In this method, considering the more complicated preparation process of thin-walled casting, an ANN model combining learning vector quantization and back-propagation (BP) algorithm is proposed to map the complex relationship between process conditions and quality indexes of LPDC. Meanwhile, the orthogonal array design and numerical simulation is applied to obtain the training samples instead of carrying out a real experiment for the sake of cost saving. 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 of 300 mm in length, 100 mm in width, and 1.5 mm in thickness is successfully prepared. The results indicate that the proposed intelligent system is an effective tool for the process optimization of LPDC.

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Authors and Affiliations

  1. College of Mechanical and Electrical Engineering, Central South University of Forestry and Technology, Changsha, 410004, Hunan, People’s Republic of China

    Liqiang Zhang & Rongji Wang

  2. State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha, 410082, Hunan, People’s Republic of China

    Liqiang Zhang

Authors
  1. Liqiang Zhang
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  2. Rongji Wang
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Correspondence to Rongji Wang.

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Zhang, L., Wang, R. An intelligent system for low-pressure die-cast process parameters optimization. Int J Adv Manuf Technol 65, 517–524 (2013). https://doi.org/10.1007/s00170-012-4190-4

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  • DOI: https://doi.org/10.1007/s00170-012-4190-4

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