A comparative study of machine learning in predicting the mechanical properties of the deposited AA6061 alloys via additive friction stir deposition

A comparative study of machine learning in predicting the mechanical properties of the deposited AA6061 alloys via additive friction stir deposition

Abstract Additive friction stir deposition (AFSD) provides strong flexibility and better performance in component design, which is controlled by the process parameters. It is an essential and difficult task to tune those parameters. The recent exploration of machine learning (ML) exhibits great pote...

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Bibliographic Details
Main Authors: Qian Qiao, Quan Liu, Jiong Pu, Haixia Shi, Wenxiao Li, Zhixiong Zhu, Dawei Guo, Hongchang Qian, Dawei Zhang, Xiaogang Li, C. T. Kwok, L. M. Tam
Format: Article
Language:English
Published: Wiley-VCH 2024-03-01
Series:Materials Genome Engineering Advances
Subjects:
additive friction stir deposition
in‐situ monitored
machine learning
mechanical properties
prediction
Online Access:https://doi.org/10.1002/mgea.31
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