Journal
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
Volume 89, Issue 1-4, Pages 711-725Publisher
SPRINGER LONDON LTD
DOI: 10.1007/s00170-016-9140-0
Keywords
Friction stir welding; Weld quality monitoring; Wavelet packet analysis; Neural network modeling; Strength prediction; Mother wavelet selection
Funding
- SERB (Science & Engineering Research Board), INDIA [SERB/F/2767/2012-13]
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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.
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