Weld quality prediction in friction stir welding using wavelet analysis

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• 作者：Bipul Das ; Sukhomay Pal ; Swarup Bag
• 关键词：Friction stir welding ; Weld quality monitoring ; Wavelet packet analysis ; Neural network modeling ; Strength prediction ; Mother wavelet selection
• 刊名：The International Journal of Advanced Manufacturing Technology
• 出版年：2017
• 出版时间：March 2017
• 年：2017
• 卷：89
• 期：1-4
• 页码：711-725
• 全文大小：
• 刊物类别：Engineering
• 刊物主题：Industrial and Production Engineering; Media Management; Mechanical Engineering; Computer-Aided Engineering (CAD, CAE) and Design;
• 出版者：Springer London
• ISSN：1433-3015
• 卷排序：89

文摘

The paper aims to present the application of wavelet packet transformation for feature extraction from the signals acquired during friction stir welding of aluminum alloy. One of the challenges encountered while implementing wavelet packet transformation is the selection of an appropriate mother wavelet function. In this study, a new method is proposed for the selection of an appropriate mother wavelet function based on the ratio of energy of the signal to the entropy of the decomposed wavelet packets. Main spindle motor and feed motor current signals are acquired during 65 welding experiments designed through full factorial method by varying three process parameters in four levels. Features obtained from wavelet packet transformation along with process parameters are fed to two artificial neural network models: multi-layer feed-forward neural network model trained with back propagation algorithm and radial basis function neural network model for the prediction of ultimate tensile strength and yield strength of the welds. The prediction performance of the former model is found to be superior to the later model for both ultimate tensile strength and yield strength.