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A technical perspective on integrating artificial intelligence to solid‑state welding

The implementation of artificial intelligence (AI) techniques in industrial applications, especially solid-state welding (SSW), has transformed modeling, optimization, forecasting, and controlling sophisticated systems. SSW is a better method for joining due to the least melting of material thus m...

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Main Authors:	Yaknesh, Sambath, Rajamurugu, Natarajan, Babu, Prakash K., Subramaniyan, Saravanakumar, Khan, Sher Afghan, Saleel, C. Ahamed, Alam, Mohammad Nur‑E, Soudagar, Manzoore Elahi Mohammad
Format:	Article
Language:	English English
Published:	Springer 2024
Subjects:	TS200 Metal manufactures. Metalworking
Online Access:	http://irep.iium.edu.my/111930/1/Article13524.pdf http://irep.iium.edu.my/111930/2/111930_A%20technical%20perspective%20on%20integrating%20artificial%20intelligence.pdf http://irep.iium.edu.my/111930/ https://link.springer.com/journal/170/how-to-publish-with-us#Fees%20and%20Funding
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spelling	my.iium.irep.1119302024-04-25T09:14:25Z http://irep.iium.edu.my/111930/ A technical perspective on integrating artificial intelligence to solid‑state welding Yaknesh, Sambath Rajamurugu, Natarajan Babu, Prakash K. Subramaniyan, Saravanakumar Khan, Sher Afghan Saleel, C. Ahamed Alam, Mohammad Nur‑E Soudagar, Manzoore Elahi Mohammad TS200 Metal manufactures. Metalworking The implementation of artificial intelligence (AI) techniques in industrial applications, especially solid-state welding (SSW), has transformed modeling, optimization, forecasting, and controlling sophisticated systems. SSW is a better method for joining due to the least melting of material thus maintaining Nugget region integrity. This study investigates thoroughly how AI-based predictions have impacted SSW by looking at methods like Artificial Neural Networks (ANN), Fuzzy Logic (FL), Machine Learning (ML), Meta-Heuristic Algorithms, and Hybrid Methods (HM) as applied to Friction Stir Welding (FSW), Ultrasonic Welding (UW), and Diffusion Bonding (DB). Studies on Diffusion Bonding reveal that ANN and Generic Algorithms can predict outcomes with an accuracy range of 85 – 99%, while Response Surface Methodology such as Optimization Strategy can achieve up to 95 percent confidence levels in improving bonding strength and optimizing process parameters. Using ANNs for FSW gives an average percentage error of about 95%, but using metaheuristics refined it at an incrementally improved accuracy rate of about 2%. In UW, ANN, Hybrid ANN, and ML models predict output parameters with accuracy levels ranging from 85 to 96%. Integrating AI techniques with optimization algorithms, for instance, GA and Particle Swarm Optimization (PSO) significantly improves accuracy, enhancing parameter prediction and optimizing UW processes. ANN’s high accuracy of nearly 95% compared to other techniques like FL and ML in predicting welding parameters. HM exhibits superior precision, showcasing its potential to enhance weld quality, minimize trial welds, and reduce costs and time. Various emerging hybrid methods offer better prediction accuracy. Springer 2024-04-22 Article PeerReviewed application/pdf en http://irep.iium.edu.my/111930/1/Article13524.pdf application/pdf en http://irep.iium.edu.my/111930/2/111930_A%20technical%20perspective%20on%20integrating%20artificial%20intelligence.pdf Yaknesh, Sambath and Rajamurugu, Natarajan and Babu, Prakash K. and Subramaniyan, Saravanakumar and Khan, Sher Afghan and Saleel, C. Ahamed and Alam, Mohammad Nur‑E and Soudagar, Manzoore Elahi Mohammad (2024) A technical perspective on integrating artificial intelligence to solid‑state welding. International Journal of Advanced Manufacturing Technology, 154 (13524). pp. 1-26. ISSN 0268-3768 E-ISSN 1433-3015 https://link.springer.com/journal/170/how-to-publish-with-us#Fees%20and%20Funding 10.1007/s00170-024-13524-9
institution	Universiti Islam Antarabangsa Malaysia
building	IIUM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	International Islamic University Malaysia
content_source	IIUM Repository (IREP)
url_provider	http://irep.iium.edu.my/
language	English English
topic	TS200 Metal manufactures. Metalworking
spellingShingle	TS200 Metal manufactures. Metalworking Yaknesh, Sambath Rajamurugu, Natarajan Babu, Prakash K. Subramaniyan, Saravanakumar Khan, Sher Afghan Saleel, C. Ahamed Alam, Mohammad Nur‑E Soudagar, Manzoore Elahi Mohammad A technical perspective on integrating artificial intelligence to solid‑state welding
description	The implementation of artificial intelligence (AI) techniques in industrial applications, especially solid-state welding (SSW), has transformed modeling, optimization, forecasting, and controlling sophisticated systems. SSW is a better method for joining due to the least melting of material thus maintaining Nugget region integrity. This study investigates thoroughly how AI-based predictions have impacted SSW by looking at methods like Artificial Neural Networks (ANN), Fuzzy Logic (FL), Machine Learning (ML), Meta-Heuristic Algorithms, and Hybrid Methods (HM) as applied to Friction Stir Welding (FSW), Ultrasonic Welding (UW), and Diffusion Bonding (DB). Studies on Diffusion Bonding reveal that ANN and Generic Algorithms can predict outcomes with an accuracy range of 85 – 99%, while Response Surface Methodology such as Optimization Strategy can achieve up to 95 percent confidence levels in improving bonding strength and optimizing process parameters. Using ANNs for FSW gives an average percentage error of about 95%, but using metaheuristics refined it at an incrementally improved accuracy rate of about 2%. In UW, ANN, Hybrid ANN, and ML models predict output parameters with accuracy levels ranging from 85 to 96%. Integrating AI techniques with optimization algorithms, for instance, GA and Particle Swarm Optimization (PSO) significantly improves accuracy, enhancing parameter prediction and optimizing UW processes. ANN’s high accuracy of nearly 95% compared to other techniques like FL and ML in predicting welding parameters. HM exhibits superior precision, showcasing its potential to enhance weld quality, minimize trial welds, and reduce costs and time. Various emerging hybrid methods offer better prediction accuracy.
format	Article
author	Yaknesh, Sambath Rajamurugu, Natarajan Babu, Prakash K. Subramaniyan, Saravanakumar Khan, Sher Afghan Saleel, C. Ahamed Alam, Mohammad Nur‑E Soudagar, Manzoore Elahi Mohammad
author_facet	Yaknesh, Sambath Rajamurugu, Natarajan Babu, Prakash K. Subramaniyan, Saravanakumar Khan, Sher Afghan Saleel, C. Ahamed Alam, Mohammad Nur‑E Soudagar, Manzoore Elahi Mohammad
author_sort	Yaknesh, Sambath
title	A technical perspective on integrating artificial intelligence to solid‑state welding
title_short	A technical perspective on integrating artificial intelligence to solid‑state welding
title_full	A technical perspective on integrating artificial intelligence to solid‑state welding
title_fullStr	A technical perspective on integrating artificial intelligence to solid‑state welding
title_full_unstemmed	A technical perspective on integrating artificial intelligence to solid‑state welding
title_sort	technical perspective on integrating artificial intelligence to solid‑state welding
publisher	Springer
publishDate	2024
url	http://irep.iium.edu.my/111930/1/Article13524.pdf http://irep.iium.edu.my/111930/2/111930_A%20technical%20perspective%20on%20integrating%20artificial%20intelligence.pdf http://irep.iium.edu.my/111930/ https://link.springer.com/journal/170/how-to-publish-with-us#Fees%20and%20Funding
_version_	1797904724164345856
score	13.211869

A technical perspective on integrating artificial intelligence to solid‑state welding

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