Robust surrogate modeling for glass forming process

Robust surrogate modeling for glass forming process | 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 Robust surrogate modeling for glass forming process Kuo-I Chang, Aabhash Dhakal, Torsten Kraft This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6283098/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 19 Jul, 2025 Read the published version in The International Journal of Advanced Manufacturing Technology → Version 1 posted 5 You are reading this latest preprint version Abstract A critical challenge in manufacturing process optimization is that simulation-based surrogate predictive models often fail when confronted with real-world measurement uncertainties. In this study, we present a robust surrogate modeling approach applicable to simulation-based manufacturing process optimization, while accounting for real-world measurement uncertainties. Unlike previous predictive methods that focus solely on tuning prediction accuracy or incorporate robustness through model-specific techniques, our methodology simultaneously optimizes for both accuracy and robustness, requiring only simulation data for training. Using glass forming as a case study, we quantitatively evaluate six machine learning algorithms under temperature measurement uncertainties of \((\pm)\) 3°C. In our experiments, Multi-layer perceptrons achieve the best overall performance with mean squared error of nodal deviation \((<)\) 0.2 while maintaining high robustness (0.6). Our approach generates a diverse set of Pareto-optimal solutions that allows post-training-and-optimization selection of the ideal model based on specific manufacturing requirements, eliminating the need to predefine the exact balance between accuracy and robustness before model development. This work represents a significant advancement in bridging the gap between idealized simulations and practical industrial applications by systematically accounting for measurement uncertainties in a model-agnostic manner. Surrogate model Robustness Finite element method simulation Machine learning Glass forming process Multi-objective optimization Natural perturbation Full Text Cite Share Download PDF Status: Published Journal Publication published 19 Jul, 2025 Read the published version in The International Journal of Advanced Manufacturing Technology → Version 1 posted Editorial decision: Major Revisions Needed 04 Jun, 2025 Reviewers agreed at journal 17 May, 2025 Reviewers invited by journal 12 Apr, 2025 Editor assigned by journal 31 Mar, 2025 First submitted to journal 28 Mar, 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-6283098","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":442144984,"identity":"cbcf5c7f-f377-48f6-967d-5ec4d0ab4b0c","order_by":0,"name":"Kuo-I Chang","email":"data:image/png;base64,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","orcid":"https://orcid.org/0009-0006-0582-2873","institution":"Fraunhofer Institute for Mechanics of Materials IWM: Fraunhofer-Institut fur Werkstoffmechanik IWM","correspondingAuthor":true,"prefix":"","firstName":"Kuo-I","middleName":"","lastName":"Chang","suffix":""},{"id":442144985,"identity":"7a42b81c-00ea-4579-a56e-44c0096cfe68","order_by":1,"name":"Aabhash Dhakal","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Aabhash","middleName":"","lastName":"Dhakal","suffix":""},{"id":442144986,"identity":"3109d9f5-5b40-4a30-aba6-15482495f70d","order_by":2,"name":"Torsten Kraft","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Torsten","middleName":"","lastName":"Kraft","suffix":""}],"badges":[],"createdAt":"2025-03-22 10:50:40","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6283098/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6283098/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00170-025-16123-4","type":"published","date":"2025-07-19T16:05:28+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":87220376,"identity":"e0045488-7f00-4734-a6d9-259d7e8c7c95","added_by":"auto","created_at":"2025-07-21 16:12:16","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3040942,"visible":true,"origin":"","legend":"","description":"","filename":"JAMTD2501514reviewer.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6283098/v1_covered_3ab10eee-d81d-4cb9-8194-3991d50faad0.pdf"}],"financialInterests":"","formattedTitle":"Robust surrogate modeling for glass forming process","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":true,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"the-international-journal-of-advanced-manufacturing-technology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jamt","sideBox":"Learn more about [The International Journal of Advanced Manufacturing Technology](https://www.springer.com/journal/170)","snPcode":"170","submissionUrl":"https://submission.nature.com/new-submission/170/3","title":"The International Journal of Advanced Manufacturing Technology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Surrogate model, Robustness, Finite element method simulation, Machine learning, Glass forming process, Multi-objective optimization, Natural perturbation","lastPublishedDoi":"10.21203/rs.3.rs-6283098/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6283098/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eA critical challenge in manufacturing process optimization is that simulation-based surrogate predictive models often fail when confronted with real-world measurement uncertainties. In this study, we present a robust surrogate modeling approach applicable to simulation-based manufacturing process optimization, while accounting for real-world measurement uncertainties. Unlike previous predictive methods that focus solely on tuning prediction accuracy or incorporate robustness through model-specific techniques, our methodology simultaneously optimizes for both accuracy and robustness, requiring only simulation data for training. Using glass forming as a case study, we quantitatively evaluate six machine learning algorithms under temperature measurement uncertainties of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\((\\pm)\\)\u003c/span\u003e\u003c/span\u003e3\u0026deg;C. 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This work represents a significant advancement in bridging the gap between idealized simulations and practical industrial applications by systematically accounting for measurement uncertainties in a model-agnostic manner.\u003c/p\u003e","manuscriptTitle":"Robust surrogate modeling for glass forming process","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-16 18:57:46","doi":"10.21203/rs.3.rs-6283098/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major Revisions Needed","date":"2025-06-04T06:12:35+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2025-05-17T16:23:24+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-12T23:32:50+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-03-31T04:50:28+00:00","index":"","fulltext":""},{"type":"submitted","content":"The International Journal of Advanced Manufacturing Technology","date":"2025-03-28T06:38:19+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"the-international-journal-of-advanced-manufacturing-technology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jamt","sideBox":"Learn more about [The International Journal of Advanced Manufacturing Technology](https://www.springer.com/journal/170)","snPcode":"170","submissionUrl":"https://submission.nature.com/new-submission/170/3","title":"The International Journal of Advanced Manufacturing Technology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"63fbfbe6-bd1d-4e8a-aeb3-ea3b646fa6ad","owner":[],"postedDate":"April 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-07-21T16:09:58+00:00","versionOfRecord":{"articleIdentity":"rs-6283098","link":"https://doi.org/10.1007/s00170-025-16123-4","journal":{"identity":"the-international-journal-of-advanced-manufacturing-technology","isVorOnly":false,"title":"The International Journal of Advanced Manufacturing Technology"},"publishedOn":"2025-07-19 16:05:28","publishedOnDateReadable":"July 19th, 2025"},"versionCreatedAt":"2025-04-16 18:57:46","video":"","vorDoi":"10.1007/s00170-025-16123-4","vorDoiUrl":"https://doi.org/10.1007/s00170-025-16123-4","workflowStages":[]},"version":"v1","identity":"rs-6283098","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6283098","identity":"rs-6283098","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}