A novel quality inspection method for aerosol jet printed sensors through infrared imaging and machine learning

• Main Authors: Moon, Seung Ki, Ng, Nicholas Poh Huat, Chen, Lequn, Ahn, Dong-Gyu
• Other Authors: School of Mechanical and Aerospace Engineering
• Format: Article
• Language: English
• Published: 2023
• Subjects: Engineering::Mechanical engineering; Additive Manufacturing; Aerosol Jet
• Online Access: https://hdl.handle.net/10356/170602
• Institution: Nanyang Technological University

The quality of printed electronic sensors by aerosol jet printing (AJP) process is hard to guarantee due to an insufficient reproducibility of the AJP process. This paper proposes a novel quality inspection method to identify defects on the printed sensor by AJP process using infrared imaging and machine learning. Potentially defective regions with high temperature distributions on printed lines are estimated from the infrared imaging when the current is applied. Demanded regions for the repair are identified by a machine learning algorithm. Finally, the applicability of the proposed method has been demonstrated by repair experiments.