Abstract
In this paper, we propose a decision-making tool based on supervised learning techniques that detects defects and proposes to the Surface-Mount Technology (SMT) operator a probability of being a false call. In this work, we compare four tree-based learning methods. The result of our experiments shows that a XGBoost model trained with our real-world dataset can accurately classify most real defects and false calls with an accuracy score of about 99.4% and a recall of about 98.6%. Moreover, we investigated the computing time of our prediction model and concluded that integration of our classification tool based on the XGBoost algorithm is realistic and feasible in the SMT production line. We believe that our tool will significantly improve the daily work of the SMT verify operator.
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This work is supported by Continental Automotive FRANCE.
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Jabbar, E., Besse, P., Loubes, JM., Roa, N.B., Merle, C., Dettai, R. (2019). Supervised Learning Approach for Surface-Mount Device Production. In: Nicosia, G., Pardalos, P., Giuffrida, G., Umeton, R., Sciacca, V. (eds) Machine Learning, Optimization, and Data Science. LOD 2018. Lecture Notes in Computer Science(), vol 11331. Springer, Cham. https://doi.org/10.1007/978-3-030-13709-0_21
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DOI: https://doi.org/10.1007/978-3-030-13709-0_21
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