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
@article{arxiv.2201.06735,
title = {AI Augmented Digital Metal Component},
author = {Eunhyeok Seo and Hyokyung Sung and Hayeol Kim and Taekyeong Kim and Sangeun Park and Min Sik Lee and Seung Ki Moon and Jung Gi Kim and Hayoung Chung and Seong-Kyum Choi and Ji-hun Yu and Kyung Tae Kim and Seong Jin Park and Namhun Kim and Im Doo Jung},
journal= {arXiv preprint arXiv:2201.06735},
year = {2022}
}