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Calo, Keanu
- Use of Resnet Modelling for Tig Weld Feature Digitization And Correlation – A Technique for AI Based Welding System
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Authors
Affiliations
1 University of Cape Town, ZA
2 Indian Institute of Technology, Kanpur, IN
1 University of Cape Town, ZA
2 Indian Institute of Technology, Kanpur, IN
Source
Manufacturing Technology Today, Vol 22, No 1 (2023), Pagination: 25-32Abstract
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
- Bacioiu, D., Melton, G., Papaelias, M., Shaw, R. (2019). Automated defect classification of Aluminium 5083 TIG welding using HDR camera and neural networks. Journal of Manufacturing Processes, 45, 603-613. 10.1016/j.jmapro.2019. 07.020
- Das, D., Pratihar, D., Roy, G., Pal, A. (2017). Phenomenological model-based study on electron beam welding process, and input-output modelling using neural networks trained by back-propagation algorithm, genetic algorithms, particle swarm optimization algorithm and bat algorithm. Applied Intelligence, 48(9), 2698-2718. 10.1007/s10489- 017-1101-2
- Fande, A. W., Taiwade, R. V., Raut, L. (2022). Development of activated tungsten inert gas welding and its current status: A review. Materials and Manufacturing Processes, 37(8), 841-876. 10.1080/10426914.2022.2039695
- Gyasi, E., Handroos, H., Kah, P. (2019). Survey on artificial intelligence (AI) applied in welding: A future scenario of the influence of AI on technological, economic, educational and social changes. Procedia Manufacturing, 38, 702-714. 10.1016/ j. promfg.2020.01.095
- Kesse, M., Buah, E., Handroos, H., Ayetor, G. (2020). Development of an artificial intelligence powered TIG welding algorithm for the prediction of bead geometry for TIG welding processes using hybrid deep learning, Metals, 10(4), 451, 2020. 10.3390/met10040451
- Plato.stanford.edu, (2022) Fuzzy Logic (Stanford Encyclopaedia of Philosophy), Plato.stanford.edu, 2022 Available: https://plato.stanford.edu/ entries/ logic-fuzzy/
- Xia, C., Pan, Z., Fei, Z., Zhang, S., Li, H. (2020). Vision based defects detection for Keyhole TIG welding using deep learning with visual explanation. Journal of Manufacturing Processes, 56, 845-855. 10.1016/j.jmapro.2020.05.033