Optimization of process parameters in explosive welding using machine learning
Kusammanavar Basavaraj, Satyanarayan, Anand Kulkarni
Abstract: A solid-state welding technique that joins two pieces of metal by controlled explosive detonation is called explosive welding (EXW), which has become a promising area of the study. However, it is well known that explosive welding is an expensive experiment. It is tough to expect the experimental results based on a practical approach by repeated attempts which are continued until success. In the present paper, though several Artificial Intelligence (AI) algorithms are implemented and trained using the dataset, the current state of AI algorithms based on the previous studies and their findings applied to the optimization of the welding process is reviewed and explained. Also, the types of optimization techniques available in order to predict the best results and most relevant input factors of explosive welding are reviewed. Based on the survey, the best optimisation technique is suggested for researchers.
Keywords
Optimization, Explosive Welding, Machine Learning, Wavy Interface, Techniques
Published online 6/1/2025, 6 pages
Copyright © 2025 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA
Citation: Kusammanavar Basavaraj, Satyanarayan, Anand Kulkarni, Optimization of process parameters in explosive welding using machine learning, Materials Research Proceedings, Vol. 55, pp 51-56, 2025
DOI: https://doi.org/10.21741/9781644903612-9
The article was published as article 9 of the book Materials Joining and Manufacturing Processes
Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
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