The Optimal Parameter Design of Aerospace Aluminum Alloy Weldment via Soft Computing
This research proposes an economic and effective experimental design method of multiple characteristics to deal with the parameter design problem with many continuous parameters and levels. It uses TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) and ANN (Artificial Neural Network) to train the optimal function framework of parameter design. It combines SC (Soft Computing) of SA (Simulated Anneal) and GA (Genetic Algorithm) to search the optimal parameters combination for the optimal parameter of aerospace aluminum alloy weldment. To improve previous experimental methods for multiple characteristics, this research method employs SA to search the optimal parameter such that the potential parameter can be evaluated more completely and objectively. Additionally, the model can learn the relationship between the welding parameters and the quality responses of different aluminum alloy materials to facilitate the future applications in the decisionmaking of parameter settings for automatic welding equipment. The research results can be presented to the industries as a reference, and improve the product quality and welding efficiency to relevant welding industries.
Aerospace Aluminum Alloy TIG Soft Computing Artificial Neural Network Genetic Algorithm Simulated Anneal
Jhy-Ping Jhang
Department of Industrial Engineering and Management Information, Hua Fan University, Taiwan, R.O.China
国际会议
2011 Seventh International Conference on Natural Computation(第七届自然计算国际会议 ICNC 2011)
上海
英文
878-881
2011-07-26（万方平台首次上网日期，不代表论文的发表时间）