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Machine Learning Models for Weld Quality Monitoring in Shielded Metal Arc Welding Process Using Arc Signature Features

Journal Article
05-15-04-0023
ISSN: 1946-3979, e-ISSN: 1946-3987
Published May 31, 2022 by SAE International in United States
Machine Learning Models for Weld Quality Monitoring in Shielded Metal
                    Arc Welding Process Using Arc Signature Features
Sector:
Citation: Rameshkumar, K., Vignesh, A., Gokula Chandran, P., Kirubakaran, V. et al., "Machine Learning Models for Weld Quality Monitoring in Shielded Metal Arc Welding Process Using Arc Signature Features," SAE Int. J. Mater. Manf. 15(4):347-365, 2022, https://doi.org/10.4271/05-15-04-0023.
Language: English

Abstract:

Welding is a dominant joining process employed in fabrication industries, especially in critical areas such as boiler, pressure vessels, and marine structure manufacturing. Online monitoring of welding processes using sensors and intelligent models is increasingly used in industries for predicting weld conditions. Studies are conducted in a Shielded Metal Arc Welding (SMAW) process using sound, current, and voltage sensors to predict the weld conditions. Sensor signatures are acquired from the good weld and defective weld conditions established in this study. Signal processing is carried out, and time-domain statistical features are extracted. Statistical features are also extracted from the power waveform derived from the current and voltage data for all the weld conditions. Classification And Regression Tree (CART) and Support Vector Machine (SVM) algorithms are used to build the statistical models to predict the weld conditions. SVM algorithm with Quadratic Kernel function trained using power signature features predicts weld conditions considered in this study with an accuracy of 99%.