A machine learning approach for the prediction of tensile deformation behavior in wire arc additive manufacturing-Reference-Cited by-同舟云学术

A machine learning approach for the prediction of tensile deformation behavior in wire arc additive manufacturing

Published:2023-11-17 Issue: Volume: Page:
ISSN:1955-2513
Container-title:International Journal on Interactive Design and Manufacturing (IJIDeM)
language:en
Short-container-title:Int J Interact Des Manuf

Author:

Chigilipalli Bharat Kumar^ORCID,Veeramani Anandakrishnan

Publisher

Springer Science and Business Media LLC

Subject

Industrial and Manufacturing Engineering,Modeling and Simulation

Link

https://link.springer.com/content/pdf/10.1007/s12008-023-01617-w.pdf

Reference57 articles.

1. Manjunath, A., Anandakrishnan, V., Ramachandra, S., Parthiban, K., Sathish, S.: Optimization of tribological parameters of pre-positioned wire based electron beam additive manufactured Ti-6al-4v alloy. Arch. Metall. Mater. 67(2), 447–454 (2022)

2. Hassel, T., Carstensen, T.: Properties and anisotropy behaviour of a nickel base alloy material produced by robot-based wire and arc additive manufacturing. Weld. World. 64(11), 1921–1931 (2020)

3. Chigilipalli, B.K., Veeramani, A.: Investigation of the corrosion behavior of wire arc additively manufactured alloy 825. Trans. Indian Inst. Met. 76, 1–8 (2022)

4. Chigilipalli, B.K., Veeramani, A.: Investigations on dry sliding wear behavior of a wire arc additively manufactured nickel-based superalloy. Tribol. Trans. 65(5), 912–923 (2022)

5. Kumar, C.B., Anandakrishnan, V.: Experimental investigations on the effect of wire arc additive manufacturing process parameters on the layer geometry of Inconel 825. Mater. Today Proc. 21, 622–627 (2020)

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