Work

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

Year: 2025

Type: article

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

Source: INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT

DOI: 10.55041/ijsrem44123

Authors: D.N. Chaudhari, Sanket Suresh Dabhade, ,Shubham Santosh Vaidya, Gokul Nachan

Institution: Zeal College of Engineering and Research, Pune

Language: English

Topic: Industrial Vision Systems and Defect Detection

Subfield: Industrial and Manufacturing Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: bronze

OpenAlex ID: W4409345169

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