TY - GEN
T1 - Enabling Process-Aware Design Optimization for Mitigating Residual Stress and Distortion in Metal Additive Manufacturing
AU - Dong, Wen
AU - Vulimiri, Praveen S.
AU - To, Albert C.
N1 - Publisher Copyright:
Copyright© (2022) by American Society for Precision Engineering (ASPE) All rights reserved.
PY - 2022
Y1 - 2022
N2 - The MIS method can effectively predict the residual stress and distortion of metal parts manufactured by the LPBF process. Its low-cost and high-accuracy computation facilitates machine learning by providing many data samples. The ML model trained on the samples provides a good approximation of the residual stress and strain quickly for use in design optimization of the end-use application and the build process.
AB - The MIS method can effectively predict the residual stress and distortion of metal parts manufactured by the LPBF process. Its low-cost and high-accuracy computation facilitates machine learning by providing many data samples. The ML model trained on the samples provides a good approximation of the residual stress and strain quickly for use in design optimization of the end-use application and the build process.
UR - http://www.scopus.com/inward/record.url?scp=85139761482&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85139761482
T3 - 2022 ASPE and euspen Summer Topical Meeting on Advancing Precision in Additive Manufacturing
SP - 1
EP - 5
BT - 2022 ASPE and euspen Summer Topical Meeting on Advancing Precision in Additive Manufacturing
PB - American Society for Precision Engineering, ASPE
T2 - 2022 ASPE and euspen Summer Topical Meeting on Advancing Precision in Additive Manufacturing
Y2 - 11 July 2022 through 14 July 2022
ER -