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

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

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...