Application of Artificial Neural Network for the Optimal Welding Parameters Design of Aerospace Aluminum Alloy Thick Plate

Jhy-Ping Jhang ()
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Jhy-Ping Jhang: Hua Fan University

A chapter in Proceedings of 20th International Conference on Industrial Engineering and Engineering Management, 2013, pp 601-609 from Springer

Abstract: Abstract 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 Artificial Neural Network (ANN) to train the optimal function framework of parameter design for the thick plate weldment of aerospace aluminum alloy. To improve previous experimental methods for multiple characteristics, this research method employs ANN and all combinations 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 materials to facilitate the future applications in the decision-making 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.

Keywords: ANN; Aerospace aluminum alloy; TIG; TOPSIS; Taguchi method (search for similar items in EconPapers)
Date: 2013
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-642-40072-8_60

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DOI: 10.1007/978-3-642-40072-8_60

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