Knowledge transfer using Bayesian learning for predicting the process-property relationship of Inconel alloys obtained by laser powder bed fusion

ABSTRACT

In this study, we investigate the transferability of the process-property relationship between two Inconel alloys for laser powder bed fusion (LPBF). By developing a Bayesian learning approach, the process-property model of Inconel 625 learned from Inconel 718 demonstrates high accuracy with R of 0.95, which verifies the feasibility of this innovative concept. It is further found that the accuracy of the knowledge transfer model of Inconel 625 is increased if the data on Inconel 625 is more abundant. In this regard, the mean RMSE and MAE on relative density are decreased by about 0.45% and 0.35% when the Inconel 625 dataset size is increased from 15 to 60. In addition, both accuracy and robustness of the Inconel 625 model are increased with transferred knowledge from Inconel 718 regardless of the dataset size of Inconel 625, in which the mean RMSE and MAE are decreased by up to 0.7% and 0.5%, respectively.

KEYWORDS:

• Laser powder bed fusion
• Inconel alloys
• process-property relation
• transfer learning
• model accuracy and robustness

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data files of both collected and experimental relative density and four primary process parameters of LPBF-produced IN718 and IN625 are available upon request.

Additional information

Notes on contributors

Cuiyuan Lu

Ms. Cuiyuan Lu is a Ph.D. candidate in the Department of Mechanical and Materials Engineering at University of Cincinnati. Her research interests are in the intersected fields of process-structure-property relationship, additive manufacturing, machine learning and artificial intelligence.

Xiaodong Jia

Dr. Xiaodong Jia is a research assistant professor in the Department of Mechanical and Materials Engineering at University of Cincinnati. He received a Ph.D. in mechanical engineering at University of Cincinnati in 2018. His research focuses on developing advanced analytical solutions, including advanced sensing and monitoring and advanced data analytics, for maintenance and service innovations in next-generation industry systems (industry 4.0). Dr. Jia has published 45+ peer-reviewed articles and led 10+ research projects sponsored by industry companies and government agencies.

Jay Lee

Dr. Jay Lee is Ohio Eminent Scholar, L.W. Scott Alter Chair, and University Distinguished Professor at University of Cincinnati, and founding director of National Science Foundation (NSF) Industry/University Cooperative Research Center on Intelligent Maintenance Systems. He was on leave from UC to serve as Vice Chairman and Board Member for Foxconn Technology Group during 2019-2021. He also served as Director for Product Development and Manufacturing at United Technologies Research Center as well as Program Directors for a number of programs at NSF. Dr. Lee has published more than 300 peer reviewed papers and have received more than 28,000 citations according to Google Scholar.

Jing Shi

Dr. Jing Shi is a professor of mechanical engineering, with joint appointments from materials science and engineering, and industrial & systems engineering programs, at University of Cincinnati. His expertise mainly covers materials design and processing, additive manufacturing, and modeling and optimization of complex systems. He is a frequent recipient of teaching and research awards and an editorial member of multiple international journals. He has published more than 200 refereed papers, which have been cited for about 6000 times according to Google Scholar.