Studying and Predicting the Wear Performance of Destabilized High-Cr WIs Using Machine Learning and Artificial Intelligence Techniques

Studying and Predicting the Wear Performance of Destabilized High-Cr WIs Using Machine Learning and Artificial Intelligence Techniques | 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 Studying and Predicting the Wear Performance of Destabilized High-Cr WIs Using Machine Learning and Artificial Intelligence Techniques Kh. Abd El-Aziz, Ibrahim B. M. Taha, D. Saber This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6980191/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The current work aims to study and prediction of wear performance of destabilized high-Cr WIs using machine learning and artificial intelligence techniques. High Cr-WI alloys with various compositions and Cr/C ratios were tested under various sliding distances of 350, 700, and 1000 m at 20, 40, and 60N loads. Experimental results showed that, high-Cr WI with a higher ratio of Cr/C demonstrated the lower wear resistance at all sliding distances and applied loads in both the as cast and destabilized situations. Moreover, destabilization heat treatment of HCWCI alloys exhibited substantial variations in abrasive wear performance of these iron alloys, these may be due to the changes of the microstructure and the formation of hard-martensitic matrix enclosed by M 7 C 3 carbides network of as explained by SEM. The alloys with hard-martensitic matrix structure gave the better abrasive wear performance than the alloys with both austenitic and pearlitic matrices. Subcritical heat treatments at a temperature below 500 o C on the destabilized alloys gave little changes of wear resistance without any indications to secondary hardening. The wear resistance deteriorated significantly after tempering above 500 o C due to the formation of ferrite/carbide aggregates as a result of martensite decomposition. In addition, the wear behavior of destabilized high-Cr WIs results is used to identify the wear behavior of the destabilized high-Cr WIs using optimal machine learning regression (OMLR) methods. OMLR methods are implemented and performed using MATLAB/software. The OMLR methods utilize the input parameters of the destabilized high-Cr WIs, including sliding distance and load, as well as the weight loss due to abrasive wear, to build their optimal models. OMLR methods particularly predict outcomes with low errors, specifically the Ensemble regression (EN) method. The proposed EN method results were compared to those attained from the other OMLR models with the efficacy of the EN model. High Cr-WIs (HCWIs) Destabilization heat treatment Abrasive wear Subcritical heat treatment Statistical regression analysis Optimize machine learning regression methods Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-6980191","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":478476068,"identity":"6fa97b4b-65ee-4f13-8402-777c378afcd1","order_by":0,"name":"Kh. 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High Cr-WI alloys with various compositions and Cr/C ratios were tested under various sliding distances of 350, 700, and 1000 m at 20, 40, and 60N loads. Experimental results showed that, high-Cr WI with a higher ratio of Cr/C demonstrated the lower wear resistance at all sliding distances and applied loads in both the as cast and destabilized situations. Moreover, destabilization heat treatment of HCWCI alloys exhibited substantial variations in abrasive wear performance of these iron alloys, these may be due to the changes of the microstructure and the formation of hard-martensitic matrix enclosed by M\u003csub\u003e7\u003c/sub\u003eC\u003csub\u003e3\u003c/sub\u003e carbides network of as explained by SEM. The alloys with hard-martensitic matrix structure gave the better abrasive wear performance than the alloys with both austenitic and pearlitic matrices. Subcritical heat treatments at a temperature below 500\u003csup\u003eo\u003c/sup\u003eC on the destabilized alloys gave little changes of wear resistance without any indications to secondary hardening. The wear resistance deteriorated significantly after tempering above 500\u003csup\u003eo\u003c/sup\u003eC due to the formation of ferrite/carbide aggregates as a result of martensite decomposition. In addition, the wear behavior of destabilized high-Cr WIs results is used to identify the wear behavior of the destabilized high-Cr WIs\u003cstrong\u003e \u003c/strong\u003eusing optimal machine learning regression (OMLR) methods. OMLR methods are implemented and performed using MATLAB/software. The OMLR methods utilize the input parameters of the destabilized high-Cr WIs, including sliding distance and load, as well as the weight loss due to abrasive wear, to build their optimal models. OMLR methods particularly predict outcomes with low errors, specifically the Ensemble regression (EN) method. The proposed EN method results were compared to those attained from the other OMLR models with the efficacy of the EN model.\u003c/p\u003e","manuscriptTitle":"Studying and Predicting the Wear Performance of Destabilized High-Cr WIs Using Machine Learning and Artificial Intelligence Techniques","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-01 03:50:05","doi":"10.21203/rs.3.rs-6980191/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a4263b39-73d3-41c8-a712-278c2498c411","owner":[],"postedDate":"July 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-07-18T02:09:00+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-01 03:50:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6980191","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6980191","identity":"rs-6980191","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}