A Data-Driven Framework to Predict Fused Filament Fabrication Part Properties Using Surrogate Models and Multi-Objective Optimisation

A Data-Driven Framework to Predict Fused Filament Fabrication Part Properties Using Surrogate Models and Multi-Objective Optimisation | 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 A Data-Driven Framework to Predict Fused Filament Fabrication Part Properties Using Surrogate Models and Multi-Objective Optimisation Yongjie Zhang, Joon Phil Choi, Seung Ki Moon This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1159839/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract In additive manufacturing (AM), due to large number of process parameters and multiple responses of interest, it is hard for AM designers to attain optimal part performance without a systematic approach. In this research, a data-driven framework is proposed to achieve the desired AM part performance and quality by predicting part properties and optimizing AM process parameters effectively and efficiently. The proposed framework encompasses efficient sampling of design space and establishing the initial experiment points. Based on established empirical data, surrogate models, are used to characterise influence of critical process parameters on responses on interest. Further, process maps can be generated for enhancing understanding on the influence of process parameters on responses of interests and AM process characteristics. Subsequently, multi-objective optimisation coupled with a multi criteria decision making technique is applied to determine an optimal design point, which maximises the identified responses of interest to meet the part functional requirements. A case study is used to validate the proposed framework for optimising an ULTEM™ 9085 fused filament fabrication part to meet its functional requirements of surface roughness and mechanical strength. From the case study, results indicate that the proposed approach is able to achieve good predictive results for responses of interest with a relatively small dataset. Further, process maps generated from the surrogate model provide a visual representation of the influence between responses of interest and critical process parameters for FFF process, which traditionally requires multiple investigations to arrive at similar conclusions. Mechanical Engineering Additive manufacturing Fused Filament Fabrication Mechanical properties Multi-objective optimisation Surrogate models Full Text Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Minor Revisions Needed 11 Mar, 2022 Reviews received at journal 24 Dec, 2021 Reviewers invited by journal 16 Dec, 2021 Editor assigned by journal 16 Dec, 2021 First submitted to journal 10 Dec, 2021 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. 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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-1159839","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":70757028,"identity":"10df5136-cb6a-4a52-b7c3-08bec6c7bd3e","order_by":0,"name":"Yongjie Zhang","email":"","orcid":"","institution":"Nanyang Technological University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yongjie","middleName":"","lastName":"Zhang","suffix":""},{"id":70757029,"identity":"9f7c327c-0281-48ce-b6c5-c05bad5bde7e","order_by":1,"name":"Joon Phil Choi","email":"","orcid":"","institution":"Nanyang Technological University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Joon","middleName":"Phil","lastName":"Choi","suffix":""},{"id":70757030,"identity":"b334e7c3-c428-4730-8011-1ed1b348690a","order_by":2,"name":"Seung Ki Moon","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxElEQVRIiWNgGAWjYDACHjBpg8whTksahCZFy2EStMj3nE588HHH+Tx7iQTGB2/bGOzlHQhoMTjbu9lw5pnbxTwSCcyGc9sYEjceIKSFn3ebNG/b7cQeiQQ2IIMhwbCBkMP6wVrOgbSw/wZqsSeoheFsL0jLAbAtzEAtjPMJ6TA4cxbol7bkxJ4zD5sl55yTSNxASIt8T+7GBx/b7BLb25MPfnhTZmMvT9BhCMAIUivBYHCAeC0we0mwZRSMglEwCkYGAADYTz0C7vRlPQAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-2249-7500","institution":"Nanyang Technological University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Seung","middleName":"Ki","lastName":"Moon","suffix":""}],"badges":[],"createdAt":"2021-12-10 15:18:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1159839/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1159839/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":16620229,"identity":"fb892d5f-39f1-4523-90fc-f37288e682a0","added_by":"auto","created_at":"2021-12-20 16:18:45","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":826474,"visible":true,"origin":"","legend":"","description":"","filename":"JAMTdraftZhangetal.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1159839/v1_covered.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003eA Data-Driven Framework to Predict Fused Filament Fabrication Part Properties Using Surrogate Models and Multi-Objective Optimisation\u003c/p\u003e","fulltext":[{"header":"Full Text","content":"This preprint is available for \u003ca href='/article/rs-1159839/latest.pdf' target='_blank'\u003edownload as a PDF\u003c/a\u003e."}],"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":false,"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":"Additive manufacturing, Fused Filament Fabrication, Mechanical properties, Multi-objective optimisation, Surrogate models","lastPublishedDoi":"10.21203/rs.3.rs-1159839/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1159839/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn additive manufacturing (AM), due to large number of process parameters and multiple responses of interest, it is hard for AM designers to attain optimal part performance without a systematic approach. 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