Leveraging Large Language Models for Process Parameter Optimization in 3D-Printed ABS Polymer Specimens

Leveraging Large Language Models for Process Parameter Optimization in 3D-Printed ABS Polymer Specimens | 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 Leveraging Large Language Models for Process Parameter Optimization in 3D-Printed ABS Polymer Specimens Tanvir Ahmed Shanto This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7437594/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The optimization of process parameters in Fused Deposition Modeling (FDM) remains a critical challenge in predicting the mechanical strength of 3D-printed parts. Conventional methods, such as the Design of Experiments (DOE), are often costly and time-consuming, necessitating the exploration of Artificial Intelligence (AI)-based predictive models. This paper investigates the potential of Large Language Models (LLMs), including Microsoft Phi-2, Qwen2.5-Math-1.5B, DeepSeek-R1-Distill-Qwen-1.5B, and StableLM-3B-4e1t to optimize FDM process parameters for maximizing the mechanical strength of 3D-printed Acrylonitrile Butadiene Styrene (ABS) polymer specimens. We aim to enhance the prediction and optimization of critical mechanical property by applying few-shot inference with these advanced LLMs using tensile test data with varying print parameters. Model performance is analyzed using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R-squared (R²), highlighting each model’s effectiveness in identifying optimal settings for enhanced mechanical performance. Our results demonstrate the potential of LLMs in data-driven parameter optimization, providing a novel perspective on applying large-scale language models to predictive tasks in additive manufacturing. This approach opens new avenues for leveraging state-of-the-art AI to refine material properties in 3D printing processes. Mechanical Engineering Additive Manufacturing Fused Deposition Modeling Large Language Models Few-Shot Inference Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted 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-7437594","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":504403031,"identity":"31a0dff9-45d7-4de4-8719-456ae5e8d21c","order_by":0,"name":"Tanvir Ahmed Shanto","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5ElEQVRIiWNgGAWjYHAC9g8SBhJyyCIGeNXzMDCwMVhU2BgjqyZCS8WZtMQGorXYS6Q/e3Cz7XD62vYzxh8Y2/7IM7A3b5PAawvPGXPDmW2Hc7edyTGTYGwzMGzgOVaGXwt7D4O0JEjLgbQ0BqAWxgYJoF68WpjZH0j/BTrM7PyzZKDDDOwb5N8Q0MLeYCYhcSYtwexG8gGQwxIbJHgIaDlzxthAosLGcNuNx8ckEs4ZJ7fxpBVb4NPCPiP94QNgVMqbnU9s/vChTM62n/3wxhv4tKCCBAZQNI2CUTAKRsEooBgAAJVdRl1yYWMNAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0009-0006-8243-5690","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Tanvir","middleName":"Ahmed","lastName":"Shanto","suffix":""}],"badges":[],"createdAt":"2025-08-22 22:37:49","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-7437594/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7437594/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89922954,"identity":"a36185be-58bf-4e28-b20d-9628e1ab37a9","added_by":"auto","created_at":"2025-08-26 13:06:37","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":410571,"visible":true,"origin":"","legend":"","description":"","filename":"FinalDraft.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7437594/v1_covered_0433114d-af5d-498f-b5b5-2969cd1f5502.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eLeveraging Large Language Models for Process Parameter Optimization in 3D-Printed ABS Polymer Specimens\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"The University of Texas at Arlington","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Additive Manufacturing, Fused Deposition Modeling, Large Language Models, Few-Shot Inference","lastPublishedDoi":"10.21203/rs.3.rs-7437594/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7437594/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe optimization of process parameters in Fused Deposition Modeling (FDM) remains a critical challenge in predicting the mechanical strength of 3D-printed parts. Conventional methods, such as the Design of Experiments (DOE), are often costly and time-consuming, necessitating the exploration of Artificial Intelligence (AI)-based predictive models. This paper investigates the potential of Large Language Models (LLMs), including Microsoft Phi-2, Qwen2.5-Math-1.5B, DeepSeek-R1-Distill-Qwen-1.5B, and StableLM-3B-4e1t to optimize FDM process parameters for maximizing the mechanical strength of 3D-printed Acrylonitrile Butadiene Styrene (ABS) polymer specimens. We aim to enhance the prediction and optimization of critical mechanical property by applying few-shot inference with these advanced LLMs using tensile test data with varying print parameters. Model performance is analyzed using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R-squared (R\u0026sup2;), highlighting each model\u0026rsquo;s effectiveness in identifying optimal settings for enhanced mechanical performance. Our results demonstrate the potential of LLMs in data-driven parameter optimization, providing a novel perspective on applying large-scale language models to predictive tasks in additive manufacturing. 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