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Acoustic Signature Based Weld Quality Monitoring for SMAW Process Using Data Mining Algorithms

Publication Type : Journal Article

Publisher : Trans Tech Publications

Source : Applied Mechanics and Materials , Trans Tech Publications, Volume 813-814, p.1104-1113 (2015)

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Keywords : Acoustic Signature, Confusion Matrix, Data Mining Algorithms, Naive Bayes Support Vector Machines, neural network, Weld Quality Monitoring

Campus : Coimbatore

School : School of Engineering

Department : Electronics and Communication, Mechanical Engineering

Year : 2015

Abstract : The quality of weld depends upon welding parameters and exposed environment conditions. Improper selection of welding process parameter is one of the important reasons for the occurrence of weld defect. In this work, arc sound signals are captured during the welding of carbon steel plates. Statistical features of the sound signals are extracted during the welding process. Data mining algorithms such as Naive Bayes, Support Vector Machines and Neural Network were used to classify the weld conditions according to the features of the sound signal. Two weld conditions namely good weld and weld with defects namely lack of fusion, and burn through were considered in this study. Classification efficiencies of machine learning algorithms were compared. Neural network is found to be producing better classification efficiency comparing with other algorithms considered in this study.

Cite this Research Publication : A. Sumesh, Dinu Thomas Thekkuden, Dr. Binoy B. Nair, K. Ramesh Kumar, and K. Mohandas, “Acoustic Signature Based Weld Quality Monitoring for SMAW Process Using Data Mining Algorithms”, Applied Mechanics and Materials , vol. 813-814, pp. 1104-1113, 2015.

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