Comparative Study of Statistical and Soft Computing Approaches for Forecasting Material Removal Rate and Temperature in the EDM Process of Inconel 718

Comparative Study of Statistical and Soft Computing Approaches for Forecasting Material Removal Rate and Temperature in the EDM Process of Inconel 718 | 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 Comparative Study of Statistical and Soft Computing Approaches for Forecasting Material Removal Rate and Temperature in the EDM Process of Inconel 718 Apurva Kulkarni, Ganesh Dongre This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6594743/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 14 You are reading this latest preprint version Abstract Machining challenging materials such as Inconel 718 is typically accomplished through the use of electro discharge machining (EDM). This work presents a comprehensive comparative analysis of statistical and soft computing approaches aimed at forecasting temperature distribution and material removal rate (MRR) in the electrical discharge machining of Inconel 718. Utilizing Gaussian heat flow distribution alongside temperature-dependent material properties, a three-dimensional transient thermal model was developed in ANSYS Workbench. The model exhibited strong consistency when validated against experimental results and existing literature. The effects of pulse-on time, discharge current, and heat transfer fraction (F = 0.2–0.4) on thermal behavior and crater development were thoroughly examined, revealing significant findings. The findings indicate that Inconel 718 exhibits distinct thermal gradients due to its low thermal conductivity, with increased pulse-on duration and current leading to higher material removal rates and peak temperatures. Responses for EDM were projected utilizing various regression analyses, including ANOVA, alongside an artificial neural network model. The impressive predicted accuracy of the ANN model demonstrates its effectiveness for complex nonlinear modeling tasks. The work establishes a validated computational and data-driven framework aimed at optimizing EDM parameters, with significant implications for enhancing machining productivity, reducing tool wear, and improving surface quality in aerospace and tooling applications. ANOVA Regression Electro Discharge Machining Finite Element Analysis Temperature Distribution Material Removal Rate Inconel 718 Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 21 Aug, 2025 Reviews received at journal 13 Aug, 2025 Reviewers agreed at journal 01 Aug, 2025 Reviewers agreed at journal 31 Jul, 2025 Reviewers agreed at journal 30 Jul, 2025 Reviews received at journal 23 Jul, 2025 Reviewers agreed at journal 17 Jul, 2025 Reviewers agreed at journal 11 Jul, 2025 Reviews received at journal 04 Jul, 2025 Reviewers agreed at journal 17 Jun, 2025 Reviewers invited by journal 12 Jun, 2025 Editor assigned by journal 15 May, 2025 Submission checks completed at journal 15 May, 2025 First submitted to journal 05 May, 2025 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. 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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-6594743","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":470527665,"identity":"2805f7c4-7d98-4eb5-ae5d-0c26a763a73d","order_by":0,"name":"Apurva Kulkarni","email":"data:image/png;base64,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","orcid":"","institution":"Vishwakarma Institute of Technology","correspondingAuthor":true,"prefix":"","firstName":"Apurva","middleName":"","lastName":"Kulkarni","suffix":""},{"id":470527666,"identity":"b6d36d0e-57b1-4b72-bbda-bfac90e7f4bc","order_by":1,"name":"Ganesh Dongre","email":"","orcid":"","institution":"Vishwakarma Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Ganesh","middleName":"","lastName":"Dongre","suffix":""}],"badges":[],"createdAt":"2025-05-05 13:08:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6594743/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6594743/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84674331,"identity":"4467856c-5fba-4839-8776-060b6dee5f48","added_by":"auto","created_at":"2025-06-16 07:20:26","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1119351,"visible":true,"origin":"","legend":"","description":"","filename":"DMEManuscriptRevised140525.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6594743/v1_covered_6b537124-f158-49b0-9af7-98c0d378d3b0.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Comparative Study of Statistical and Soft Computing Approaches for Forecasting Material Removal Rate and Temperature in the EDM Process of Inconel 718","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-mechanical-engineering","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"discmecheng","sideBox":"Learn more about [Discover Mechanical Engineering](https://www.springer.com/journal/44245)","snPcode":"44245","submissionUrl":"https://submission.nature.com/new-submission/44245/3","title":"Discover Mechanical Engineering","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"ANOVA, Regression, Electro Discharge Machining, Finite Element Analysis, Temperature Distribution, Material Removal Rate, Inconel 718","lastPublishedDoi":"10.21203/rs.3.rs-6594743/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6594743/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMachining challenging materials such as Inconel 718 is typically accomplished through the use of electro discharge machining (EDM). This work presents a comprehensive comparative analysis of statistical and soft computing approaches aimed at forecasting temperature distribution and material removal rate (MRR) in the electrical discharge machining of Inconel 718. Utilizing Gaussian heat flow distribution alongside temperature-dependent material properties, a three-dimensional transient thermal model was developed in ANSYS Workbench. The model exhibited strong consistency when validated against experimental results and existing literature. The effects of pulse-on time, discharge current, and heat transfer fraction (F\u0026thinsp;=\u0026thinsp;0.2\u0026ndash;0.4) on thermal behavior and crater development were thoroughly examined, revealing significant findings. The findings indicate that Inconel 718 exhibits distinct thermal gradients due to its low thermal conductivity, with increased pulse-on duration and current leading to higher material removal rates and peak temperatures. Responses for EDM were projected utilizing various regression analyses, including ANOVA, alongside an artificial neural network model. The impressive predicted accuracy of the ANN model demonstrates its effectiveness for complex nonlinear modeling tasks. The work establishes a validated computational and data-driven framework aimed at optimizing EDM parameters, with significant implications for enhancing machining productivity, reducing tool wear, and improving surface quality in aerospace and tooling applications.\u003c/p\u003e","manuscriptTitle":"Comparative Study of Statistical and Soft Computing Approaches for Forecasting Material Removal Rate and Temperature in the EDM Process of Inconel 718","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-16 06:56:20","doi":"10.21203/rs.3.rs-6594743/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-08-21T16:10:56+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-13T09:36:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"129881139866402679185171180933250604601","date":"2025-08-01T13:24:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"26734112400254142381031915078301164855","date":"2025-07-31T04:23:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"293130003903050775306207083392469167551","date":"2025-07-30T14:10:05+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-23T08:45:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"66937318868809976012387093076326229789","date":"2025-07-17T12:23:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"159169708063050123428102741210499624617","date":"2025-07-11T21:34:14+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-04T07:34:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"300127903073050117806014207568147919415","date":"2025-06-17T18:39:33+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-12T17:18:17+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-15T06:17:21+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-15T06:16:19+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Mechanical Engineering","date":"2025-05-05T13:06:59+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-mechanical-engineering","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"discmecheng","sideBox":"Learn more about [Discover Mechanical Engineering](https://www.springer.com/journal/44245)","snPcode":"44245","submissionUrl":"https://submission.nature.com/new-submission/44245/3","title":"Discover Mechanical Engineering","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ff759bf7-15e2-499f-985d-c46549b3486a","owner":[],"postedDate":"June 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-11-06T12:38:12+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-16 06:56:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6594743","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6594743","identity":"rs-6594743","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}