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

A Neuro-Fuzzy Risk Prediction Methodology in the Automotive Part Industry | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A Neuro-Fuzzy Risk Prediction Methodology in the Automotive Part Industry Ammar Chakhrit, Imene Djelamda, Mohammed Bougofa, Islam H.M. Guetarni, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4208702/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 08 Oct, 2024 Read the published version in Operations Research Forum → Version 1 posted 10 You are reading this latest preprint version 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. 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 the decision-making by providing a “preventive –corrective plan. A comparison with existing approaches is presented to demonstrate the efficiency of the suggested approach. Risk assessment FMEA Fuzzy logic RPN Adaptive neuro-fuzzy inference system Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 08 Oct, 2024 Read the published version in Operations Research Forum → Version 1 posted Editorial decision: Revision requested 12 Aug, 2024 Reviewers agreed at journal 13 Jul, 2024 Reviews received at journal 12 Jul, 2024 Reviews received at journal 10 Jul, 2024 Reviewers agreed at journal 10 Jul, 2024 Reviewers agreed at journal 07 Jul, 2024 Reviewers invited by journal 24 May, 2024 Submission checks completed at journal 03 Apr, 2024 Editor assigned by journal 03 Apr, 2024 First submitted to journal 02 Apr, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4208702","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":287683657,"identity":"7762876b-632f-4e28-85e0-dc966c4af196","order_by":0,"name":"Ammar Chakhrit","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7ElEQVRIiWNgGAWjYDACCQZmZhAJARVAzMzcQJQWqJ4zIC2MRGmBWsPYBibxa+Gf3fzYuHCPRR3/tNOJnyvn1UbztwO1/KjYhtuSO8eMk2c8k5CQuJ27WfLstuO5Mw4zNjD2nLmNU4uBRILxYZ4DQL/czt0g2bjtWG4DUAszYxs+LemfwVrkgbb8bJxzLHc+YS05xskgLQa3c7dJNjbU5G4gpEXiRk6xMVCL5EagFsuGYwdyNwK1HMTnF/4Z6ZuleQ7U8csBHXazoaYud975wwcf/KjArQUdHAaTB4hWDwR1pCgeBaNgFIyCEQIAUtlaoKnv3aQAAAAASUVORK5CYII=","orcid":"","institution":"Mohamed Cherif Messaadia University","correspondingAuthor":true,"prefix":"","firstName":"Ammar","middleName":"","lastName":"Chakhrit","suffix":""},{"id":287683658,"identity":"f0adf4a0-93ea-456f-8700-161080f1aba6","order_by":1,"name":"Imene Djelamda","email":"","orcid":"","institution":"Mohamed Cherif Messaadia University","correspondingAuthor":false,"prefix":"","firstName":"Imene","middleName":"","lastName":"Djelamda","suffix":""},{"id":287683660,"identity":"0bf7d5b1-1339-4c93-b003-2965258616a3","order_by":2,"name":"Mohammed Bougofa","email":"","orcid":"","institution":"Sonatrach company, Exploration \u0026 Production Activity, Production Division, Illizi, Algeria","correspondingAuthor":false,"prefix":"","firstName":"Mohammed","middleName":"","lastName":"Bougofa","suffix":""},{"id":287683661,"identity":"7fbfdca7-becc-44cd-9242-c9793779e677","order_by":3,"name":"Islam H.M. 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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. 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. 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