Assessing product quality from the production process logs

Autor: Audine Subias, Le Toan Duong, Louise Travé-Massuyès, Nathalie Barbosa Roa
Přispěvatelé: Équipe DIagnostic, Supervision et COnduite (LAAS-DISCO), Laboratoire d'analyse et d'architecture des systèmes (LAAS), Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Institut National des Sciences Appliquées (INSA)-Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT), Vitesco Technologies, ANR-19-P3IA-0004,ANITI,Artificial and Natural Intelligence Toulouse Institute(2019), Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Zdroj: International Journal of Advanced Manufacturing Technology
International Journal of Advanced Manufacturing Technology, 2021, 117, pp.1615-1631. ⟨10.1007/s00170-021-07764-2⟩
International Journal of Advanced Manufacturing Technology, Springer Verlag, 2021, 117, pp.1615-1631. ⟨10.1007/s00170-021-07764-2⟩
ISSN: 0268-3768
1433-3015
Popis: International audience; A real challenge for manufacturing industry is to be able to control not only the manufacturing process but also the production quality. Products that are suspected to be faulty are deviated from their nominal path in the production line and inspected more closely. The fact that some products deviate from the nominal path and others fail at some check operations can be used as an indicator of poor product quality. Based on this idea, this paper proposes a method to compute a product quality index or more exactly a penalty index taking into account both product path and production batches. The method relies on categorizing the products according to how they follow the production path and process mining techniques. The originality of the proposed index is to be built from advanced data analysis techniques enhanced by expert know-how. The quality index highlights risk of customer return, which is highly relevant information for the after sales service. The significance of the method is illustrated on a printed circuit board production line using surface mount technology at Vitesco Technologies. Data is collected from the real manufacturing execution system. The results obtained over more than 10000 single electronic boards show that 91.7% of the products are in good compliance with respect to the requirements. For the other products, the method identifies the root causes of poor quality that may call for maintenance or reconfiguration actions.
Databáze: OpenAIRE
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