This is not the latest version of this item. The latest version can be found here.
Loading...
Defect prediction on production line
Khalfaoui, S. Manouvrier, E. Briot, A. Delaux, D. Butel, S. Ibrahim, Jesutofunmi Kanyere, Tatenda Orimogunje, Bola Abdullatif, Amr A.A. Neagu, Daniel
Khalfaoui, S.
Manouvrier, E.
Briot, A.
Delaux, D.
Butel, S.
Ibrahim, Jesutofunmi
Kanyere, Tatenda
Orimogunje, Bola
Abdullatif, Amr A.A.
Neagu, Daniel
Publication Date
2022
End of Embargo
Supervisor
Rights
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG. Reproduced in accordance with the publisher's self-archiving policy.
The final publication is available at Springer via https://doi.org/10.1007/978-3-030-87094-2_47.
Peer-Reviewed
Yes
Open Access status
openAccess
Accepted for publication
2021
Institution
Department
Awarded
Embargo end date
Additional title
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.
Version
Accepted manuscript
Citation
Khalfaoui S, Manouvrier E, Briot A et al (2022) Defect prediction on production line. In: Jansen T, Jensen R, Mac Parthalain N et al (Eds.) Advances in Computational Intelligence Systems. UKCI 2021. Advances in Intelligent Systems and Computing. 1409. Springer, Cham.
Link to publisher’s version
Link to published version
Link to Version of Record
Type
Book chapter