A machine learning-based recommendation framework for material extrusion fabricated triply periodic minimal surface lattice structures

A machine learning-based recommendation framework for material extrusion fabricated triply periodic minimal surface lattice structures | 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 machine learning-based recommendation framework for material extrusion fabricated triply periodic minimal surface lattice structures Sajjad Hussain, Carman Lee, Yung Po Tsang, Saad Waqar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3723296/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 Triply Periodic Minimal Surface (TPMS) lattice structures are utilized in diverse fields such as engineering, material design, and biomedical. The use of appropriate TPMS lattice structures in 3D printing can obtain benefits in terms of production efficiency and material reduction towards a greener 3D printing process. However, there is a lack of an automated solution to suggest the appropriate TPMS lattice structure parameters, such that unnecessary material wastage cannot be neglected in the existing practices. To address the above challenges, this study proposes a machine learning-based recommendation framework for generating the TPMS lattice structures based on the engineering requirements. First, we compiled a dataset by producing 144 samples via the material extrusion (ME) technique and conducted compression tests on four TPMS lattice structures (Diamond, Gyroid, Schwarz, and split-P), each with varying parameters, fabricated using Polylactic acid (PLA) material. Second, we train four machine learning algorithms (K-Nearest Neighbors, Decision Tree, Random Forest, and Bayesian Regression) on this dataset to predict TPMS lattice structure (unit cell type, unit cell size, and wall thickness). Extensive experiments assess algorithm performance using R-squared values and Root Mean Square Error (RMSE) as evaluation measures. Our results indicate that the Random Forest and Decision Tree algorithms perform best, achieving R-squared scores of 0.9694 and 0.9689, along with RMSE values of 0.1180 and 0.0795, respectively. This work not only advances the field's understanding of automated selection for TPMS lattice structures but also holds noteworthy implications for eco-design and eco-innovation, particularly in the realm of sustainable and efficient green 3D printing applications. Material Extrusion (ME) Machine learning TPMS Lattice structures Polylactic Acid (PLA) Full Text 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-3723296","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":264387835,"identity":"cea07d5e-ab6b-4fb2-9ba5-17ad6bac363c","order_by":0,"name":"Sajjad Hussain","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Sajjad","middleName":"","lastName":"Hussain","suffix":""},{"id":264387836,"identity":"15a1b482-54cb-41c1-b852-b9a7d7c9b1eb","order_by":1,"name":"Carman Lee","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA40lEQVRIiWNgGAWjYDACHiB+wMAgx8DA3ADiMzYw8DAT1pLAwGAMVkySlsQGorXw95xOYEiosEnfzt7YJs3DYCO74QDvYQN8WiTO9m5gSDiTlruz5yBIS5rxhgN8yQn4tBjw825gSGw7nLvhRiJIy+HEDQd4jA8Q1vLvf7oBRMt/IrTwAh2W2HAgAarlAFgLXodJnDm74UDCsWRDoF+aLecYJBvPPMxjjNf7/D25Gx98qLGTN2dvPnjjTYWdbN/xHmMJfFpAAOxyoMEsEiCSgUBEInkKqPYDsYpHwSgYBaNgZAEAPg1LRts69FsAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0001-8577-4547","institution":"The Hong Kong Polytechnic University","correspondingAuthor":true,"prefix":"","firstName":"Carman","middleName":"","lastName":"Lee","suffix":""},{"id":264387837,"identity":"08a36ecc-6f0f-4714-99d5-8399a3070d9d","order_by":2,"name":"Yung Po Tsang","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Yung","middleName":"Po","lastName":"Tsang","suffix":""},{"id":264387838,"identity":"66c075f1-a9ac-450a-a8dd-35f371db88b1","order_by":3,"name":"Saad Waqar","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Saad","middleName":"","lastName":"Waqar","suffix":""}],"badges":[],"createdAt":"2023-12-08 03:19:49","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3723296/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3723296/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":61264311,"identity":"b4173344-2a3f-4839-9c4f-beba504215e3","added_by":"auto","created_at":"2024-07-28 20:23:54","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1920258,"visible":true,"origin":"","legend":"","description":"","filename":"PaperIJAMT.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3723296/v1_covered_b0088afa-ea96-4451-a507-21c612532f81.pdf"}],"financialInterests":"","formattedTitle":"A machine learning-based recommendation framework for material extrusion fabricated triply periodic minimal surface lattice structures","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"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":"Material Extrusion (ME), Machine learning, TPMS Lattice structures, Polylactic Acid (PLA)","lastPublishedDoi":"10.21203/rs.3.rs-3723296/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3723296/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTriply Periodic Minimal Surface (TPMS) lattice structures are utilized in diverse fields such as engineering, material design, and biomedical. The use of appropriate TPMS lattice structures in 3D printing can obtain benefits in terms of production efficiency and material reduction towards a greener 3D printing process. However, there is a lack of an automated solution to suggest the appropriate TPMS lattice structure parameters, such that unnecessary material wastage cannot be neglected in the existing practices. To address the above challenges, this study proposes a machine learning-based recommendation framework for generating the TPMS lattice structures based on the engineering requirements. First, we compiled a dataset by producing 144 samples via the material extrusion (ME) technique and conducted compression tests on four TPMS lattice structures (Diamond, Gyroid, Schwarz, and split-P), each with varying parameters, fabricated using Polylactic acid (PLA) material. Second, we train four machine learning algorithms (K-Nearest Neighbors, Decision Tree, Random Forest, and Bayesian Regression) on this dataset to predict TPMS lattice structure (unit cell type, unit cell size, and wall thickness). Extensive experiments assess algorithm performance using R-squared values and Root Mean Square Error (RMSE) as evaluation measures. Our results indicate that the Random Forest and Decision Tree algorithms perform best, achieving R-squared scores of 0.9694 and 0.9689, along with RMSE values of 0.1180 and 0.0795, respectively. 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