Deep Neural Network-Based Approach for Modeling, Predicting, and Validating Weld Quality and Mechanical Properties of Friction Stir Welded Dissimilar Materials-Reference-Cited by-同舟云学术

Deep Neural Network-Based Approach for Modeling, Predicting, and Validating Weld Quality and Mechanical Properties of Friction Stir Welded Dissimilar Materials

Published:2023-09-25 Issue:11 Volume:75 Page:4562-4578
ISSN:1047-4838
Container-title:JOM
language:en
Short-container-title:JOM

Author:

Maheshwari Shrushti,Kar Amlan,Alam Zafar^ORCID,Kumar Lalan

Publisher

Springer Science and Business Media LLC

Subject

General Engineering,General Materials Science

Link

https://link.springer.com/content/pdf/10.1007/s11837-023-06121-w.pdf

Reference54 articles.

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2. S.R. Nathan, A. Visvalingam Balasubramanian, G. Rao, T. Sonar, M. Ivanov, and K. Suganeswaran, Int. J. Lightweight Mater. Manuf. 6(4), 469 (2023).

3. C.B Smith, J.F Hinrichs, and W.A. Crusan. Robotic friction stir welding: the state of the art, in 4th Friction Stir Welding International Symposium, pp. 14–16, (2003)

4. K.J. Colligan, The friction stir welding process: an overview, in Friction Stir Welding. ed. by D. Lohwasser, and Z. Chen. Woodhead Publishing Series in Welding and Other Joining Technologies. (Woodhead Publishing, Cambridge, 2010), pp. 15–41

5. L.E. Murr, Friction-Stir Welding and Processing (Springer International Publishing, Cham, 2015), pp1–18.

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