Abstract
Quality control has long been one of the most challenging fields of manufacturing. The development of advanced sensors and the easier collection of high amounts of data designate the machine learning techniques as a timely natural step forward to leverage quality decision support and manufacturing challenges. This paper introduces an original dataset provided by the automotive supplier company VALEO, coming from a production line, and hosted by the École Normale Supérieure (ENS) Data Challenge to predict defects using non-anonymised features, without access to final test results, to validate the part status (defective or not). We propose in this paper a complete workflow from data exploration to the modelling phase while addressing at each stage challenges and techniques to solve them, as a benchmark reference. The proposed workflow is validated in series of experiments that demonstrate the benefits, challenges and impact of data science adoption in manufacturing.
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Acknowledgment
The authors thank VALEO for providing access to ENS Challenge dataset, the SAFI consortium for the opportunity to collaborate, and the following VALEO colleagues for the valuable discussions and presentations: Aitor Gonzalez, Miroslav Zima, Romain Delente, Aurele Pilloud-Passin, Benoit Veger, Fabrice Blasenhauer, Jean-Jacques Lopez, Remi Laronde, Sebastien Odouard, Stephane De-Clercq, Stephane Wysocki.
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Khalfaoui, S. et al. (2022). Defect Prediction on Production Line. In: Jansen, T., Jensen, R., Mac Parthaláin, N., Lin, CM. (eds) Advances in Computational Intelligence Systems. UKCI 2021. Advances in Intelligent Systems and Computing, vol 1409. Springer, Cham. https://doi.org/10.1007/978-3-030-87094-2_47
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DOI: https://doi.org/10.1007/978-3-030-87094-2_47
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