NDLI: Weld quality prediction in friction stir welding using wavelet analysis

Content Provider	Springer Nature Link
Author	Das, Bipul Pal, Sukhomay Bag, Swarup
Copyright Year	2016
Abstract	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.
Starting Page	711
Ending Page	725
Page Count	15
File Format	PDF
ISSN	02683768
Journal	The International Journal of Advanced Manufacturing Technology
Volume Number	89
Issue Number	1-4
e-ISSN	14333015
Language	English
Publisher	Springer London
Publisher Date	2016-07-08
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Friction stir welding Weld quality monitoring Wavelet packet analysis Neural network modeling Strength prediction Mother wavelet selection Industrial and Production Engineering Media Management Mechanical Engineering Computer-Aided Engineering (CAD, CAE) and Design
Content Type	Text
Resource Type	Article
Subject	Industrial and Manufacturing Engineering Control and Systems Engineering Mechanical Engineering Computer Science Applications Software

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