kumoh national institute of technology
Networked Systems Lab.

Gabriel Avelino Sampedro, Danielle Jaye Agron, Jae-Min Lee and Dong-Seong Kim,Exploring Machine Learning-Based Fault Monitoring: Challenges and Opportunities, IEEE Industrial Electronics Magazine, 2022(S)
By : Gabriel(Gino)
Date : 2022-03-04
Views : 128

3D printing, often known as additive manufacturing (AM), is a groundbreaking
technique that enables rapid prototyping. Monitoring AM delivers benefits, including
print quality monitoring and material cost reduction. Machine learning is often applied
in automation, especially AM. This paper explores recent research on machine learningbased
mechanical fault monitoring systems in FDM and SLM machines. According to
the results, most studies used FDM machines. Furthermore, other studies use either
attitude, acoustic emission,acceleration, and vibration signals to analyze mechanicalbased
fault. Due to a lack of research in this field, there is an opportunity to research
SLM-based mechanical fault monitoring.

Submitted: 1 Mar 2022