A Neuro-Fuzzy Risk Prediction Methodology in the Automotive Part Industry

Ammar Chakhrit (), Abdelmoumene Guedri, Mohammed Bougofa, Islam H. M. Guetarni, Nour El Houda Benharkat, Abderraouf Bouafia and Mohammed Chennoufi
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Ammar Chakhrit: Mohamed Cherif Messaadia University
Abdelmoumene Guedri: University of Souk Ahras
Mohammed Bougofa: Sonatrach Company, Exploration & Production Activity, Production Division
Islam H. M. Guetarni: Université Mohamed Ben Ahmed Oran 2, Sécurité Industrielle Et Environnement
Nour El Houda Benharkat: National Polytechnic School
Abderraouf Bouafia: The Université of 20 Août 1955
Mohammed Chennoufi: Université Mohamed Ben Ahmed Oran 2, Sécurité Industrielle Et Environnement

SN Operations Research Forum, 2024, vol. 5, issue 4, 1-26

Abstract: Abstract Failure mode and effects analysis (FMEA) is a systematic and structured method employed across diverse industries to proactively identify and evaluate potential failure modes. In a traditional FMEA, for all failure modes, three criticality parameters, severity, detection, and frequency, are assessed to evaluate criticality. Nevertheless, it frequently has certain flaws. Therefore, in this work, a fuzzy risk proposed model is used to improve the use of the FMEA methodology. The new model uses a fuzzy inference technique in place of the conventional criticality calculation. The fuzzy logic technique is used where the various factors are given as members of a fuzzy set fuzzified by employing adequate membership functions to evaluate the risk and then ranking failure modes and preferring measures to control the risks of undesired events. The adaptive neuro-fuzzy inference system (ANFIS) is suggested as a dynamic, intelligently proposed model to improve and validate the results acquired by the fuzzy inference system and effectively predict the criticality evaluation of failure modes. Finally, an automotive parts industry case is presented to show the potential of the suggested model. This analysis offers a different ranking of failure modes and improves decision-making by providing a “preventive–corrective plan.” A comparison with existing approaches is presented to demonstrate the efficiency of the suggested approach.

Keywords: Risk assessment; FMEA; Fuzzy logic; RPN; Adaptive neuro-fuzzy inference system (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s43069-024-00380-2

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