Unraveling process-microstructure-property correlations in powder-bed fusion additive manufacturing through information-rich surface features with deep learning

Unraveling process-microstructure-property correlations in powder-bed fusion additive manufacturing through information-rich surface features with deep learning

A machine learning (ML)–based framework has been developed to optimize the process parameters and unravel the paramount process–microstructure–property (PMP) relationships rapidly and precisely, which is demonstrated using electron beam melting (EBM®)-processed Ti–6Al–4V alloy. The process maps are...

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Bibliographic Details
Main Authors: Wang, Chengcheng, Chandra, Shubham, Huang, Sheng, Tor, Shu Beng, Tan, Xipeng
Other Authors: School of Mechanical and Aerospace Engineering
Format: Journal Article
Language:English
Published: 2023
Subjects:
Engineering::Mechanical engineering
Additive Manufacturing
Machine Learning
Online Access:https://hdl.handle.net/10356/170435

Internet

https://hdl.handle.net/10356/170435

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