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

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.

Keywords: Weld quality monitoring, Acoustic signature, Data Mining Algorithms, Naive Bayes ,Support Vector Machines, Neural Network, Confusion Matrix