Laser powder bed fusion for AI assisted digital metal components

ABSTRACT

This paper proposes a novel method to impart intelligence to metal parts using additive manufacturing. A sensor-embedded metal bracket is prototyped via a metal powder bed fusion process to recognise partial screw loosening or total screw missing or identify the source of vibration with the assistance of artificial intelligence (AI). The digital metal bracket can recognise subtle changes in the screw fixation state with 90% accuracy and identify unknown sources of vibration with 84% accuracy. The von Mises stress distribution in the prototyped metal bracket is evaluated using a finite element analysis, which is learned by AI to match the real-time deformation analysis of the metal bracket in augmented reality. The proposed prototype can contribute to hyper-connectivity for developing next-generation metal-based mechanical components.

KEYWORDS:

• Laser powder bed fusion
• artificial intelligence
• sensor embedding
• digital metal component
• augmented reality

CrediT authorship contribution statement

Eunhyeok Seo, Hyokyung Sung: Formal analysis, Visualisation, Writing – original draft, Writing – review & editing, Investigation, Conceptualisation. Taekyeong Kim, Hongryoung Jeon, Sangeun Park, Min Sik Lee, Jung Gi Kim: Formal analysis, Investigation. Ji-hun Yu, Kyung Tae Kim: Visualisation, Investigation. Hayoung Chung, Seong Jin Park, Namhun Kim: Methodology. Hayeol Kim, Seung Ki Moon, Seong-Kyum Choi: Visualisation, Writing – review & editing. Im Doo Jung: Conceptualisation, Formal analysis, Supervision, Funding acquisition, Writing – original draft, Writing – review & editing.

Disclosure statement

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

Additional information

Funding

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) [grant Nos. 2021M2D2A1A01050059 and 2021R1F1A1046079].

Notes on contributors

Eunhyeok Seo

Eunhyeok Seo is a graduate student at the Ulsan National Institute of Science and Technology. His main direction of scientific activity is deep learning for intelligence manufacturing. His current research interests include artificial intelligence for manufacturing and additive manufacturing.

Hyokyung Sung

Hyokyung Sung is an associate professor at the Department of Materials Engineering and Convergence Technology (Center for K-metals), Gyeongsang National University. His research interests include damage tolerance behaviors and environmental effects on materials.

Hongryoung Jeon

Hongryoung Jeon is a bachelor student at the Ulsan National Institute of Science and Technology.

Hayeol Kim

Hayeol Kim is a graduate student at the Ulsan National Institute of Science and Technology.

Taekyeong Kim

Taekyeong Kim is a graduate student at the Ulsan National Institute of Science and Technology.

Sangeun Park

Sangeun Park is a graduate student at the Gyeongsang National University

Min Sik Lee

Min Sik Lee is a research engineer at the Department of Mechanical Engineering, Ulsan National Institute of Science and Technology (UNIST).

Seung Ki Moon

Seung Ki Moon is an associate professor at the Department of Mechanical and Aerospace Engineering, Nanyang Technological University.

Jung Gi Kim

Jung Gi Kim is an assistant professor at the Department of Materials Engineering and Convergence Technology, Gyeongsang National University.

Hayoung Chung

Hayoung Chung is an assistant professor at the Department of Mechanical Engineering, Ulsan National Institute of Science and Technology (UNIST).

Seong-Kyum Choi

Seong-Kyum Choi is an associate professor at the Department of Mechanical Engineering, Georgia Institute of Technology.

Ji-Hun Yu

Ji-Hun Yu is a director of the Powder Materials Division, Korea Institute of Materials Science.

Kyung Tae Kim

Kyung Tae Kim is a head of the Department of 3D printing Materials, Korea Institute of Materials Science.

Seong Jin Park

Seong Jin Park is a professor at the Department of Mechanical Engineering, Pohang University of Science and Technology (POSTECH) and director of the Industry-University-Research Cooperation in POSCO.

Namhun Kim

Namhun Kim is a professor at the Department of Mechanical Engineering, Ulsan National Institute of Science and Technology (UNIST) and director of the Center for 3D Printing Advanced Additive Manufacturing.

Im Doo Jung

Im Doo Jung is an assistant professor at the Department of Mechanical Engineering, Ulsan National Institute of Science and Technology (UNIST). His research interest includes A.I. for digitalization of manufacturing and metal/electrical material development.