Towards an instant structure-property prediction quality control tool for additive manufactured steel using a crystal plasticity trained deep learning surrogate

• Main Authors: TU, Yuhui, LIU, Zhongzhou, Carneiro, Luiz, Ryan, Caitriona M., Parnell, Andrew C., Leen, Sean B.
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2022
• Subjects: Crystal plasticity; Deep neural network; 17-4PH stainless steel; Additive manufacturing; Micromechanics; Numerical Analysis and Scientific Computing; Operations Research, Systems Engineering and Industrial Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/6953; https://ink.library.smu.edu.sg/context/sis_research/article/7956/viewcontent/11356_2021_Article_15738_pvoa__1_.pdf
• Institution: Singapore Management University

The ability to conduct in-situ real-time process-structure-property checks has the potential to overcome process and material uncertainties, which are key obstacles to improved uptake of metal powder bed fusion in industry. Efforts are underway for live process monitoring such as thermal and image-b...