Generative AI-Driven PCB Defect Detection and Classification System

Gayatri Phade, Sahil Papal, Saish Aher, Sanket Chaudhari

2025

Abstract

Traditionally, PCB testing is done manually. It has limitation like it is a time-consuming process, high cost, human error, low accuracy etc. This manual process leads to high processing time, increased product cost and quality. To overcome this limitations Generative AI enabled PCB testing System is proposed that will detect and classify the defects in PCB. The Generative AI Based PCB Tester (GABPT) system uses AI enabled image processing to make the process faster and more accurate. The GABPT classify defects like scratch, missing holes, Missing Path, Mouse-bite, Short-circuit, missing Conductor etc. The main objectives to reduce human error, speed up testing, and lower the testing costs.it will give an approach on machine-based methods like emphasizing image pre-processing, image acquisition and analysis. The system uses high-resolution cameras to capture the relative image of the PCB to be tested, an Arm Cortex A72 processes for analysing the captured images and LCD to monitor the tested result. It identifies and categorizes defects, providing immediate feedback to help users resolve issues efficiently. This paper improves the efficiency of PCB testing by using automation. This helps save time and money while making the process more accurate. As a result, we proposed a better--quality products and reduce human errors, which encourages innovation in the industry. By using resources more efficiently, we also support more sustainable manufacturing practices.

Paper Citation

in Harvard Style

Phade G., Papal S., Aher S. and Chaudhari S. (2025). Generative AI-Driven PCB Defect Detection and Classification System. In Proceedings of the 3rd International Conference on Futuristic Technology - Volume 2: INCOFT; ISBN 978-989-758-763-4, SciTePress, pages 181-188. DOI: 10.5220/0013588900004664

in Bibtex Style

@conference{incoft25,
author={Gayatri Phade and Sahil Papal and Saish Aher and Sanket Chaudhari},
title={Generative AI-Driven PCB Defect Detection and Classification System},
booktitle={Proceedings of the 3rd International Conference on Futuristic Technology - Volume 2: INCOFT},
year={2025},
pages={181-188},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013588900004664},
isbn={978-989-758-763-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 3rd International Conference on Futuristic Technology - Volume 2: INCOFT
TI - Generative AI-Driven PCB Defect Detection and Classification System
SN - 978-989-758-763-4
AU - Phade G.
AU - Papal S.
AU - Aher S.
AU - Chaudhari S.
PY - 2025
SP - 181
EP - 188
DO - 10.5220/0013588900004664
PB - SciTePress