| Autors: Ivanova, M. S., Petkov N. Title: Machine Learning for In-Circuit Testing of Printed Circuit Board Assembly Keywords: artificial neural networks; circuit testing; decision trees; Abstract: Testing is an important procedure in a manufacturing process that leads to fabrication of high quality electronic components and modules. It can be facilitated through applying machine learning techniques and development of predictive and analytical models. The paper presents a method in support of test engineers at the In-Circuit testing of Printed Circuit Board Assembly when decision making has to be performed and testing problem has to be solved. Supervised machine learning algorithms: Support Vector Machine for resolving binary classification tasks and Random Forest for deciding the multi-class classification problem are utilized. The accuracy of Support Vector Machine and Random Forest algorithms is compared to the accuracy of a deep learning algorithm. The proposed approach gives precise analysis and classification regarding the defects occurred during the mounting process on the Printed Circuit Board Assembly. References Issue
Copyright ACM |
Цитирания (Citation/s):
1. Said, A., Shabbir, M., Broll, B. et al. Circuit design completion using graph neural networks. Neural Comput & Applic (2023). https://doi.org/10.1007/s00521-023-08346-x - 2023 - в издания, индексирани в Scopus или Web of Science
2. Bretones Cassoli, B., Metternich, J, Challenges for Predictive Quality in Multi-stage Manufacturing: Insights from Literature Review, SEA4DQ 2023 - Proceedings of the 3rd International Workshop on Software Engineering and AI for Data Quality in Cyber-Physical Systems/Internet of Things, Co-located with: ESEC/FSE 2023, pp. 16 - 23, 2023, DOI: 10.1145/3617573.3618030 - 2023 - в издания, индексирани в Scopus или Web of Science
Вид: пленарен доклад в международен форум, публикация в издание с импакт фактор, публикация в реферирано издание, индексирана в Scopus
