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Decision Tree Based Weld Defect Classification using Current and Voltage Signatures in GMAW Process

Publication Type : Conference Paper

Publisher : Materials Today: Proceedings,

Source : Materials Today: Proceedings, Volume 5, p.8354-8363 (2018)

Keywords : Current and voltage signatures, Decision Tree, GMAW process, Weld defect classification

Campus : Coimbatore

School : School of Engineering

Department : Mechanical Engineering

Verified : Yes

Year : 2018

Abstract : The quality of the weld is most important in industries manufacturing boilers and pressure vessels which will work in severe operating conditions. In an automated environment, developing a process monitoring and control system will ensure the weld quality and prevent the occurrence of defects. In this paper, an attempt is made using the decision tree algorithm to establish a correlation between the current and voltage signatures with the quality of the weld. Carbon steel plates are welded using GMAW processes and experimental design is established to obtain weld without any defects (good weld) and weld with porosity and burn-through defects. “KUKA” robotic GMAW welding setup integrated with “Fronius” power source is used in this study for experimentation. “TVC” data acquisition system is used to capture the current and voltage signatures. Statistical features are extracted from the current and voltage signatures. Decision tree algorithm with split criterions such as “gini index”, “towing”, and “deviance” are used to classify the weld defects. Results indicate the effectiveness of decision tree algorithms in classifying the weld defects using the current and voltage signatures.

Cite this Research Publication : A. Sumesh, Dr. Binoy B. Nair, K. Ramesh Kumar, Santhakumari, A., Raja, A., and Mohandas, K., “Decision tree based weld defect classification using current and voltage signatures in GMAW process”, in Materials Today: Proceedings, 2018, vol. 5, pp. 8354-8363.

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