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arXiv · 2201.06735

AI Augmented Digital Metal Component

Eunhyeok Seo·Hyokyung Sung·Hayeol Kim·Taekyeong Kim·Sangeun Park·Min Sik Lee·Seung Ki Moon·Jung Gi Kim·Hayoung Chung·Seong-Kyum Choi·Ji-hun Yu·Kyung Tae Kim·Seong Jin Park·Namhun Kim·Im Doo Jung
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Abstract

The aim of this work is to propose a new paradigm that imparts intelligence to metal parts with the fusion of metal additive manufacturing and artificial intelligence (AI). Our digital metal part classifies the status with real time data processing with convolutional neural network (CNN). The training data for the CNN is collected from a strain gauge embedded in metal parts by laser powder bed fusion process. We implement this approach using additive manufacturing, demonstrate a self-cognitive metal part recognizing partial screw loosening, malfunctioning, and external impacting object. The results indicate that metal part can recognize subtle change of multiple fixation state under repetitive compression with 89.1% accuracy with test sets. The proposed strategy showed promising potential in contributing to the hyper-connectivity for next generation of digital metal based mechanical systems

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Eunhyeok Seo, Hyokyung Sung, Hayeol Kim, Taekyeong Kim, Sangeun Park, Min Sik Lee, Seung Ki Moon, Jung Gi Kim, Hayoung Chung, Seong-Kyum Choi, Ji-hun Yu, Kyung Tae Kim, Seong Jin Park, Namhun Kim, Im Doo Jung. 2022-01-18. AI Augmented Digital Metal Component. https://arxiv.org/abs/2201.06735

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