Thielen N, Werner D, Schmidt K, Seidel R, Reinhardt A, Franke J (2020)
Publication Type: Conference contribution
Publication year: 2020
Publisher: IEEE Computer Society
Book Volume: 2020-May
Conference Proceedings Title: Proceedings of the International Spring Seminar on Electronics Technology
Event location: Demanovska Valley
ISBN: 9781728167732
DOI: 10.1109/ISSE49702.2020.9121044
False calls are an undesired occurrence in manufacturing of printed circuit boards that have to be verified manually after the automated optical inspection} (AOI). In this paper, the feasibility of identifying false calls of the AOI using subsequent machine learning models is investigated. Different algorithms, data set sizes and thresholds integrated into the model are considered. Thus, depending on the use case, the accuracy of the model should be maximized without increasing the error slip. Therefore, the numerical measurement data generated by the AOI is usedfor model training, testing and validation. The investigated models use a k-nearest neighbors (knn) classifier as well as an artificial neural network (ANN). Since metrics of ML models can be strongly dependent on data quantity and quality the models are tested and compared on different data sets. While the knn-classifier achieves the best results for small data sets, its use becomes problematic for large data sets due to increasing computing time. For large data sets ANNs as well as Random Forest Classifiers surpass the performance of the knn-classifier.
APA:
Thielen, N., Werner, D., Schmidt, K., Seidel, R., Reinhardt, A., & Franke, J. (2020). A Machine Learning Based Approach to Detect False Calls in SMT Manufacturing. In Proceedings of the International Spring Seminar on Electronics Technology. Demanovska Valley, SK: IEEE Computer Society.
MLA:
Thielen, Nils, et al. "A Machine Learning Based Approach to Detect False Calls in SMT Manufacturing." Proceedings of the 43rd International Spring Seminar on Electronics Technology, ISSE 2020, Demanovska Valley IEEE Computer Society, 2020.
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