ANN AND GA-BASED PROCESS PARAMETER OPTIMIZATION FOR MIMO PLASTIC INJECTION MOLDING

摘要：

Determining optimal initial process parameter settings critically influences productivity, quality, and costs of production in the plastic injection molding (PIM) industry, Up to now, most production engineers have either used trial-and-error or Taguchis parameter design method to determine initial settings for a number of parameters, including melt temperature, injection pressure, injection velocity, injection time, packing pressure, packing time, cooling temperature, and cooling time.But due to the increasing complexity of product design and multi-response quality characteristics, these multiple input- multiple output (MIMO) methods have some definite shortcomings.This research integrates Taguchis parameter design methods with back-propagation neural networks, genetic algorithms, and engineering optimization concepts, to optimize the initial process settings of plastic injection molding equipment.The research results indicate that the proposed approach can effectively help engineers determine optimal initial process settings, reduce set-test iterations, and achieve competitive advantages on product quality and costs.

关键词： Plastic injection molding Back-propagation neural networks Taguchis parameter design Genetic algorithms

作者: WEN-CHIN CHEN GONG-LOUNG FU PEI-HAO TAI WEI-JAW DENG YANG-CHIH FAN

作者单位: Graduate School of Industrial Engineering and System Management, Chung Hua University, No.707, Sec.2 Graduate Institute of Management of Technology, Chung Hua University, No.707, Sec.2, Wu Fu Rd., Hsin Graduate Institute of Management of Technology, Chung Hua University, No.707, Sec.2, Wu Fu Rd., Hsin Graduate School of Business Administration, Chung Hua University, No.707, Sec.2, Wu Fu Rd., Hsinchu

会议类型: 国际会议

会议名称: 2007 International Conference on Machine Learning and Cybernetics(IEEE第六届机器学习与控制论国际会议)

会议地点: 香港

会议语种:英文

页码: 1909-1917

在线出版日期: 2007-08-19（万方平台首次上网日期，不代表论文的发表时间）