  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

Signal Processing