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Use of Resnet Modelling for Tig Weld Feature Digitization And Correlation – A Technique for AI Based Welding System


Affiliations
1 University of Cape Town, South Africa
2 Indian Institute of Technology, Kanpur, India
     

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TIG Welding is being practiced in the manufacturing industry and it demands highly skilled labour. Artificial Intelligence (AI) is developing rapidly as researchers are constantly finding new ways in which intelligent machines can add value to their industry. An AI-based welding system stands to add value by increasing production rates, improving safety, and decreasing the human input required. Weld monitoring is a key activity in the TIG welding process and successful use of AI system will enable failure prediction and the proactive corrective actions. The aim of this project is to explore, test, and compare ResNet modelling based machine learning algorithms and examine their ability to monitor welds. In this project the weld monitoring process includes collecting images of weld joint for weld feature digitization. Also, the study enables predicting whether the weld shows good quality, contamination, burn through, misalignment, lack of fusion, or lack of penetration through a ResNet modelling based image analysis.

Keywords

ResNet Modelling, TIG Welding, Image Analysis, AI Based Weld System.
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  • Use of Resnet Modelling for Tig Weld Feature Digitization And Correlation – A Technique for AI Based Welding System

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Authors

Ramesh Kuppuswamy
University of Cape Town, South Africa
Keanu Calo
University of Cape Town, South Africa
J. Ramkumar
Indian Institute of Technology, Kanpur, India

Abstract


TIG Welding is being practiced in the manufacturing industry and it demands highly skilled labour. Artificial Intelligence (AI) is developing rapidly as researchers are constantly finding new ways in which intelligent machines can add value to their industry. An AI-based welding system stands to add value by increasing production rates, improving safety, and decreasing the human input required. Weld monitoring is a key activity in the TIG welding process and successful use of AI system will enable failure prediction and the proactive corrective actions. The aim of this project is to explore, test, and compare ResNet modelling based machine learning algorithms and examine their ability to monitor welds. In this project the weld monitoring process includes collecting images of weld joint for weld feature digitization. Also, the study enables predicting whether the weld shows good quality, contamination, burn through, misalignment, lack of fusion, or lack of penetration through a ResNet modelling based image analysis.

Keywords


ResNet Modelling, TIG Welding, Image Analysis, AI Based Weld System.

References