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; machine learning; printed circuits; support vector machines

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

    4th Artificial Intelligence and Cloud Computing Conference, pp. 221 - 228, 2021, Japan, Association for Computing Machinery, New York, United States, ISBN: 978-145038416-2/DOI: 10.1145/3508259.3508291

    Copyright ACM

    Вид: пленарен доклад в международен форум, публикация в издание с импакт фактор, публикация в реферирано издание, индексирана в Scopus