Predicting the ultimate tensile strength of AISI 1045 steel and 2017-T4 aluminum alloy joints in a laser-assisted rotary friction welding process using machine learning: a comparison with response surface methodology-Reference-Cited by-同舟云学术

Predicting the ultimate tensile strength of AISI 1045 steel and 2017-T4 aluminum alloy joints in a laser-assisted rotary friction welding process using machine learning: a comparison with response surface methodology

Published:2021-06-30 Issue:3-4 Volume:116 Page:1247-1257
ISSN:0268-3768
Container-title:The International Journal of Advanced Manufacturing Technology
language:en
Short-container-title:Int J Adv Manuf Technol

Author:

Barrionuevo Germán Omar,Mullo José Luis,Ramos-Grez Jorge Andrés

Publisher

Springer Science and Business Media LLC

Subject

Industrial and Manufacturing Engineering,Computer Science Applications,Mechanical Engineering,Software,Control and Systems Engineering

Link

https://link.springer.com/content/pdf/10.1007/s00170-021-07469-6.pdf

Reference35 articles.

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3. Cai W, Daehn G, Vivek A, Li J, Khan H, Mishra RS, Komarasamy M (2019) A state-of-the-art review on solid-state metal joining. J Manuf Sci Eng Trans ASME 141:1–35. https://doi.org/10.1115/1.4041182

4. Maalekian M (2007) Friction welding - critical assessment of literature. Sci Technol Weld Join 12:738–759. https://doi.org/10.1179/174329307X249333

5. Ananda Rao G, Ramanaiah N (2019) Dissimilar metals AISI 304 steel and AA 2219 aluminium alloy joining by friction welding method. Mater Today Proc 19:902–907. https://doi.org/10.1016/j.matpr.2019.09.028

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