Development of Fault Detection system for quality control in PCB fabrication Using CNN Model.

Development of Fault Detection system for quality control in PCB fabrication Using CNN Model.

Prof.D.N.Chaudhari1,Sanket Suresh Dabhade2 ,Shubham Santosh Vaidya3,  Gokul Nachan4.

1234ZEAL COLLEGE OF ENGINEERING & RESEARCH, PUNE – 41

Department of Robotics & Automation Engineering

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Abstract - The defects of printed circuit boards (PCBs) generated during the manufacturing process have an adverse effect on product quality, which further directly affects the stability and reliability of equipment performance. However, there are still great challenges in accurately recognizing tiny defects on the surface of PCB under the complex background due to its compact layout. To address the problem, a novel YOLO Hornet-MCBAM-CARAFE (YOLO-HMC) network based on improved YOLOv5 framework is proposed in this article to identify the tiny-size PCB defect more accurately and efficiently with fewer model parameters.

Keywords:-Machine learning (ML), Defect defection, Machine vision, Printed circuit boards (PCBs),YOLOv8