Optimization of Resistance Spot Welding parameters in steel sheets via Genetic Algorithm Implementation

Optimization of Resistance Spot Welding parameters in steel sheets via Genetic Algorithm Implementation | 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 Optimization of Resistance Spot Welding parameters in steel sheets via Genetic Algorithm Implementation Anand Kumar Mandal, Rakesh Kumar, Bikash Kumar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6613611/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 06 Aug, 2025 Read the published version in Multiscale and Multidisciplinary Modeling, Experiments and Design → Version 1 posted 11 You are reading this latest preprint version Abstract This study quantitatively assesses the efficacy of Genetic Algorithm in the optimization of input process parameters of resistance spot welding. Two joints were fabricated (a) IFHS-IFHS of thickness 1.2 mm and (b) Mild steel- Mild steel of thickness 1.5 mm in a lap-shear joint arrangement using a single pulse welding. A D-optimal, Response surface methodology was used for designing and conducting the experiment. Regression models comprising of three continuous input variables (~ Weld-Time, Weld Current, Pressure) and one discrete variable (~ material type), with two responses (~ Failure load and Elongation) were used to formulate the input-output relationship. Henceforth, the developed regression models were used as fitness function in genetic algorithm optimization. The input variables were used as genetic codes for the chromosomes, which go through the process of selection, cross-over, mutation for the optimization process to complete. The sensitivity analysis of the Pareto-optimal scatter plots, (~ decision variables vs responses) were carried out. It was found that there was an increase of 5% and 6.6% in failure loads, with no substantial increase in elongation for both the IFHS and Mild steel joints respectively. The upper boundary limits of weld-time (~ 25 cycles), weld current (~ 15 KA) and pressure varying in the range [IFHS (~ 4.8–5.5 bar), Mild steel (~ 4.2–5.2 bar)] were found to be optimal input parameters for fabricating robust joints. This research provides a descriptive methodology of the application of genetic algorithm in the optimisation of process parameters for resistance spot welding process. Resistance spot welding (RSW) Design of Experiment (DOE) D-optimal Response Surface Methodology (RSM) Genetic Algorithm (GA) Material Type (Z) Full Text Additional Declarations No competing interests reported. Supplementary Files Table1ChemicalandMechanicalpropertiesofsteelmaterialsbeingwelded.docx Table2RecommendedpracticesofRSWjointinlowcarbonsteelsheets.docx Table3ParameterrangeforDesignofExperiments.docx Table4DOEdevelopedasperDoptimaldesignforfabricatingRSWjointsforbothmaterialtypes.docx Table5DevelopedquadraticregressionmodelsforFLandEL.docx Table6OptimizeddecisionvariablesforIFHSsteelusingGA.docx Table7OptimizeddecisionvariablesforIFHSsteelusingGA.docx Table8ComparisonofPredictedandExperimentalResults.docx Appendix1.docx Cite Share Download PDF Status: Published Journal Publication published 06 Aug, 2025 Read the published version in Multiscale and Multidisciplinary Modeling, Experiments and Design → Version 1 posted Editorial decision: Revision requested 09 Jun, 2025 Reviews received at journal 07 Jun, 2025 Reviews received at journal 13 May, 2025 Reviews received at journal 12 May, 2025 Reviewers agreed at journal 12 May, 2025 Reviewers agreed at journal 11 May, 2025 Reviewers agreed at journal 09 May, 2025 Reviewers invited by journal 09 May, 2025 Editor assigned by journal 09 May, 2025 Submission checks completed at journal 09 May, 2025 First submitted to journal 07 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. 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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-6613611","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":455732408,"identity":"a62dc6bd-89bc-4204-8c08-21f845bd26a8","order_by":0,"name":"Anand Kumar Mandal","email":"","orcid":"","institution":"Dr B R Ambedkar National Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Anand","middleName":"Kumar","lastName":"Mandal","suffix":""},{"id":455732415,"identity":"f2c3f315-9791-4ec6-bdbf-7de3f45c4dc5","order_by":1,"name":"Rakesh Kumar","email":"","orcid":"","institution":"Dr B R Ambedkar National Institute 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Implementation","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":"multiscale-and-multidisciplinary-modeling-experiments-and-design","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mmed","sideBox":"Learn more about [Multiscale and Multidisciplinary Modeling, Experiments and Design](https://link.springer.com/journal/41939)","snPcode":"41939","submissionUrl":"https://submission.nature.com/new-submission/41939/3","title":"Multiscale and Multidisciplinary Modeling, Experiments and Design","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Resistance spot welding (RSW), Design of Experiment (DOE), D-optimal, Response Surface Methodology (RSM), Genetic Algorithm (GA), Material Type (Z)","lastPublishedDoi":"10.21203/rs.3.rs-6613611/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6613611/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study quantitatively assesses the efficacy of Genetic Algorithm in the optimization of input process parameters of resistance spot welding. Two joints were fabricated (a) IFHS-IFHS of thickness 1.2 mm and (b) Mild steel- Mild steel of thickness 1.5 mm in a lap-shear joint arrangement using a single pulse welding. A D-optimal, Response surface methodology was used for designing and conducting the experiment. Regression models comprising of three continuous input variables (~\u0026thinsp;Weld-Time, Weld Current, Pressure) and one discrete variable (~\u0026thinsp;material type), with two responses (~\u0026thinsp;Failure load and Elongation) were used to formulate the input-output relationship. Henceforth, the developed regression models were used as fitness function in genetic algorithm optimization. The input variables were used as genetic codes for the chromosomes, which go through the process of selection, cross-over, mutation for the optimization process to complete. The sensitivity analysis of the Pareto-optimal scatter plots, (~\u0026thinsp;decision variables vs responses) were carried out. It was found that there was an increase of 5% and 6.6% in failure loads, with no substantial increase in elongation for both the IFHS and Mild steel joints respectively. The upper boundary limits of weld-time (~\u0026thinsp;25 cycles), weld current (~\u0026thinsp;15 KA) and pressure varying in the range [IFHS (~\u0026thinsp;4.8\u0026ndash;5.5 bar), Mild steel (~\u0026thinsp;4.2\u0026ndash;5.2 bar)] were found to be optimal input parameters for fabricating robust joints. This research provides a descriptive methodology of the application of genetic algorithm in the optimisation of process parameters for resistance spot welding process.\u003c/p\u003e","manuscriptTitle":"Optimization of Resistance Spot Welding parameters in steel sheets via Genetic Algorithm Implementation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-14 05:35:54","doi":"10.21203/rs.3.rs-6613611/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-06-09T15:05:03+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-07T09:36:51+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-13T06:04:52+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-12T06:31:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"124927126230823122745946141598799211733","date":"2025-05-12T04:36:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"292505090393722756934254750797076711801","date":"2025-05-11T15:18:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"308939568376797113803491706009226774071","date":"2025-05-09T15:49:23+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-09T14:57:25+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-09T04:21:08+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-09T04:17:09+00:00","index":"","fulltext":""},{"type":"submitted","content":"Multiscale and Multidisciplinary Modeling, Experiments and Design","date":"2025-05-07T15:49:54+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"multiscale-and-multidisciplinary-modeling-experiments-and-design","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mmed","sideBox":"Learn more about [Multiscale and Multidisciplinary Modeling, Experiments and Design](https://link.springer.com/journal/41939)","snPcode":"41939","submissionUrl":"https://submission.nature.com/new-submission/41939/3","title":"Multiscale and Multidisciplinary Modeling, Experiments and Design","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"3dda73c8-0ad1-4150-a1c0-42d4f824fc78","owner":[],"postedDate":"May 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-08-11T16:06:31+00:00","versionOfRecord":{"articleIdentity":"rs-6613611","link":"https://doi.org/10.1007/s41939-025-00996-9","journal":{"identity":"multiscale-and-multidisciplinary-modeling-experiments-and-design","isVorOnly":false,"title":"Multiscale and Multidisciplinary Modeling, Experiments and Design"},"publishedOn":"2025-08-06 15:57:58","publishedOnDateReadable":"August 6th, 2025"},"versionCreatedAt":"2025-05-14 05:35:54","video":"","vorDoi":"10.1007/s41939-025-00996-9","vorDoiUrl":"https://doi.org/10.1007/s41939-025-00996-9","workflowStages":[]},"version":"v1","identity":"rs-6613611","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6613611","identity":"rs-6613611","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}