A novel approach based on the elastoplastic fatigue damage and machine learning models for life prediction of aerospace alloy parts fabricated by additive manufacturing

• Main Authors: Zhan, Zhixin, Li, Hua
• Other Authors: School of Mechanical and Aerospace Engineering
• Format: Article
• Language: English
• Published: 2022
• Subjects: Engineering::Aeronautical engineering; Elastoplastic Fatigue Damage; Machine Learning
• Online Access: https://hdl.handle.net/10356/159864
• Institution: Nanyang Technological University

In the aerospace engineering, many metal parts produced using Additive Manufacturing (AM) technique often bear cyclic loadings, so the fatigue failures of AM alloy parts become very common phenomena. In this work, a new method is proposed to investigate the fatigue damage behavior of AM aerospace al...