Multi-Objective Optimization of Injection Molding Process Parameters for Thin-Walled Shell Parts Based on a RIME–RF– MOGWO Framework

Multi-Objective Optimization of Injection Molding Process Parameters for Thin-Walled Shell Parts Based on a RIME–RF– MOGWO Framework | 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 Article Multi-Objective Optimization of Injection Molding Process Parameters for Thin-Walled Shell Parts Based on a RIME–RF– MOGWO Framework Jiaxu Zhao, Jinghao Zhang, Liuyu Zhu, Xiying Fan, Yonghuan Guo, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9032277/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 Thin-walled components are widely used in automotive interior parts. However, during injection molding, thin-walled shell structures are highly sensitive to process parameters, which often leads to warpage and volumetric shrinkage, thereby significantly reducing their service life. To address this issue, a multi-objective optimization method for injection molding process parameters of thin-walled plastic parts is proposed based on an integrated RIME–RF–MOGWO framework. Simulation-generated samples are employed as the research data, with volumetric shrinkage and warpage deformation selected as the optimization objectives. First, the synthetic minority oversampling technique (SMOTE) is adopted to alleviate data imbalance and enhance the representativeness of minority samples. Subsequently, a random forest (RF) regression model is constructed to capture the nonlinear relationship between process parameters and quality responses. To further improve predictive accuracy, the frost-inspired RIME optimization algorithm is introduced to optimize the hyperparameters of the RF model, thereby strengthening its global search capability. Furthermore, the optimized RF surrogate model is coupled with the multi-objective grey wolf optimizer (MOGWO) to achieve multi-objective optimization of injection molding process parameters. A Pareto-optimal solution set is obtained through non-dominated sorting and crowding-distance-based diversity control. Multi-round optimization and simulation-based validation demonstrate that the proposed method effectively identifies optimal process parameter combinations, resulting in a 19.02% reduction in volumetric shrinkage and a 50.63% reduction in warpage deformation. These results indicate that the proposed approach can significantly improve the molding quality of thin-walled plastic parts used in automotive interior applications. Physical sciences/Engineering Physical sciences/Materials science Physical sciences/Mathematics and computing Thin-walled plastic part SMOTE Random forest regression RIME optimization algorithm Multi-objective grey wolf optimizer Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 28 Apr, 2026 Reviews received at journal 25 Apr, 2026 Reviews received at journal 23 Apr, 2026 Reviews received at journal 21 Apr, 2026 Reviewers agreed at journal 17 Apr, 2026 Reviewers agreed at journal 12 Apr, 2026 Reviewers agreed at journal 12 Apr, 2026 Reviews received at journal 31 Mar, 2026 Reviewers agreed at journal 29 Mar, 2026 Reviewers invited by journal 29 Mar, 2026 Editor invited by journal 17 Mar, 2026 Editor assigned by journal 14 Mar, 2026 Submission checks completed at journal 14 Mar, 2026 First submitted to journal 04 Mar, 2026 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-9032277","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":615095344,"identity":"4ba41603-6357-4c61-bbbc-cccb3f9b7fd8","order_by":0,"name":"Jiaxu Zhao","email":"","orcid":"","institution":"Jiangsu Normal University","correspondingAuthor":false,"prefix":"","firstName":"Jiaxu","middleName":"","lastName":"Zhao","suffix":""},{"id":615095345,"identity":"931cc83c-692a-4a6a-8cf6-035ca54e6449","order_by":1,"name":"Jinghao Zhang","email":"","orcid":"","institution":"Jiangsu Normal University","correspondingAuthor":false,"prefix":"","firstName":"Jinghao","middleName":"","lastName":"Zhang","suffix":""},{"id":615095346,"identity":"18c1be3d-a065-4c16-a50b-c2e6f52781f7","order_by":2,"name":"Liuyu Zhu","email":"","orcid":"","institution":"Jiangsu Normal University","correspondingAuthor":false,"prefix":"","firstName":"Liuyu","middleName":"","lastName":"Zhu","suffix":""},{"id":615095347,"identity":"e4ecdec5-5f6e-4b5c-bd93-86b52801052f","order_by":3,"name":"Xiying Fan","email":"","orcid":"","institution":"Jiangsu Normal University","correspondingAuthor":false,"prefix":"","firstName":"Xiying","middleName":"","lastName":"Fan","suffix":""},{"id":615095348,"identity":"bf1263fe-ad23-4b3e-bb6c-a026473fb663","order_by":4,"name":"Yonghuan Guo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwklEQVRIiWNgGAWjYFACHsYHEhUScmzszQeI1sJsYHHGwpiP51gC0VrYJCrbKhLnSeQoEKdBPiL3sMENNon0NoYcBoYfFdsIazG8kZf4cAaPRG4bw9kDjD1nbhOhZUaOsbGEBFALY18CM2MbcVrMpP8YSKSzMfMYEKdFXiLHTEIiQSKBjY1YLQY8b4wNJA5IGLbxsCUcJMov8u05hg8k/9XJy89/fPDBjwpibLmQgOAcIKweZEs/cepGwSgYBaNgJAMAjzA4yP3D1sMAAAAASUVORK5CYII=","orcid":"","institution":"Jiangsu Normal University","correspondingAuthor":true,"prefix":"","firstName":"Yonghuan","middleName":"","lastName":"Guo","suffix":""},{"id":615095349,"identity":"b8981414-3f99-4113-9ee0-26080d10d03b","order_by":5,"name":"Lie Li","email":"","orcid":"","institution":"Jiangsu Normal University","correspondingAuthor":false,"prefix":"","firstName":"Lie","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2026-03-04 16:09:32","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9032277/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9032277/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106094779,"identity":"0d1a6391-3fa5-4d26-ae4e-9c66492c541a","added_by":"auto","created_at":"2026-04-03 11:43:15","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":723278,"visible":true,"origin":"","legend":"","description":"","filename":"file.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9032277/v1_covered_1a2fd28d-2296-48e4-8ab9-3721a8f0132d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Multi-Objective Optimization of Injection Molding Process Parameters for Thin-Walled Shell Parts Based on a RIME–RF– MOGWO Framework","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Thin-walled plastic part, SMOTE, Random forest regression, RIME optimization algorithm, Multi-objective grey wolf optimizer","lastPublishedDoi":"10.21203/rs.3.rs-9032277/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9032277/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThin-walled components are widely used in automotive interior parts. However, during injection molding, thin-walled shell structures are highly sensitive to process parameters, which often leads to warpage and volumetric shrinkage, thereby significantly reducing their service life. To address this issue, a multi-objective optimization method for injection molding process parameters of thin-walled plastic parts is proposed based on an integrated RIME\u0026ndash;RF\u0026ndash;MOGWO framework.\u003c/p\u003e \u003cp\u003eSimulation-generated samples are employed as the research data, with volumetric shrinkage and warpage deformation selected as the optimization objectives. First, the synthetic minority oversampling technique (SMOTE) is adopted to alleviate data imbalance and enhance the representativeness of minority samples. Subsequently, a random forest (RF) regression model is constructed to capture the nonlinear relationship between process parameters and quality responses. To further improve predictive accuracy, the frost-inspired RIME optimization algorithm is introduced to optimize the hyperparameters of the RF model, thereby strengthening its global search capability.\u003c/p\u003e \u003cp\u003eFurthermore, the optimized RF surrogate model is coupled with the multi-objective grey wolf optimizer (MOGWO) to achieve multi-objective optimization of injection molding process parameters. A Pareto-optimal solution set is obtained through non-dominated sorting and crowding-distance-based diversity control. Multi-round optimization and simulation-based validation demonstrate that the proposed method effectively identifies optimal process parameter combinations, resulting in a 19.02% reduction in volumetric shrinkage and a 50.63% reduction in warpage deformation. These results indicate that the proposed approach can significantly improve the molding quality of thin-walled plastic parts used in automotive interior applications.\u003c/p\u003e","manuscriptTitle":"Multi-Objective Optimization of Injection Molding Process Parameters for Thin-Walled Shell Parts Based on a RIME–RF– MOGWO Framework","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-02 23:40:53","doi":"10.21203/rs.3.rs-9032277/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-29T03:41:31+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-25T09:42:40+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-24T02:19:26+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-21T21:42:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"336236309589611420839298169901813821539","date":"2026-04-18T01:11:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"296567616348516697942525411579067562571","date":"2026-04-13T02:47:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"243708124087752140565567898486901148333","date":"2026-04-12T15:06:03+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-31T07:54:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"56388641167667038041446084139041415193","date":"2026-03-29T15:08:13+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-29T11:07:12+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-18T03:25:34+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-14T11:52:16+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-14T11:51:21+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-03-04T16:04:19+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1c7ae2f0-493c-4f1d-af2c-4187d02e4ed8","owner":[],"postedDate":"April 2nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":65449144,"name":"Physical sciences/Engineering"},{"id":65449145,"name":"Physical sciences/Materials science"},{"id":65449146,"name":"Physical sciences/Mathematics and computing"}],"tags":[],"updatedAt":"2026-05-07T17:24:32+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-02 23:40:53","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9032277","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9032277","identity":"rs-9032277","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}