Autors: Petkov N., Ivanova, M. S.
Title: Detection and Classification of Defects on Printed Circuit Board Assembly through Deep Learning
Keywords: deep learning, defects detection, image classification, PCBA testing, TensorFlow, visual inspection

Abstract: Contemporary production of electronic devices and modules requires the final product to be of high quality and at the lowest possible price. To ensure this, the production process includes various testing phases, thus resulting defects can be detected in time. The paper analyzes and evaluates a process for classifying defected and non-defected Printed Circuit Board Assemblies (PCBAs) by applying a deep learning algorithm. Our own datasets of PCBAs images with and without defects are created, experiments are performed utilizing TensorFlow, and classification models are evaluated. The results show that the discussed approach is characterized with high accuracy in detecting defective PCBAs at the conductance of two-class classification tasks.

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Issue

2024 9th International Conference on Smart and Sustainable Technologies, SpliTech 2024, pp. 1-5, 2024, , https://doi.org/10.23919/SpliTech61897.2024.10612667

Copyright Institute of Electrical and Electronics Engineers Inc.

Цитирания (Citation/s):
1. Jang, J., Tang, Q., Jung, H., PCB Defect Classification with Data Augmentation-Based Ensemble Method for Sustainable Smart Manufacturing, Sustainability (Switzerland), 16(23),10417, 2024. ISSN: 20711050, DOI: 10.3390/su162310417. - 2024 - в издания, индексирани в Scopus или Web of Science

Вид: публикация в международен форум, публикация в реферирано издание, индексирана в Scopus