Leveraging Machine Learning for Porosity Prediction in AM using FDM for Pretrained Models and Process Development

Leveraging Machine Learning for Porosity Prediction in AM using FDM for Pretrained Models and Process Development | 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 Machine Learning for Porosity Prediction in AM using FDM for Pretrained Models and Process Development Khadija Ouajjani, James Steck, Gerardo Olivares This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6049621/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 Due to the numerous independent parameters involved in additive manufacturing, 3D printing often delivers different quality of prints and requires an expensive trial-and-error approach before finding the optimal combination of the numerous and independent input variables. Machine learning is, therefore, an ideal solution to this nonlinear problem, and provides an informed guess on printing parameters based on a minimal set of experiments. Using the case example of Fused Deposition Modeling, and, as a proof of concept, examining the porosity defect, a machine learning powered process is developed to predict the porosity defect's occurrence. It also facilitates the determination of the types of combinations of printing variables to ensure minimal defects. Specimens were 3D printed and CT-scanned. Raw datasets were collected in the form of grayscale image files (around 7,300 images) from the CT-scan. A machine learning image classifier was developed and trained to sort exploitable images from defective ones. To preprocess information for the classifier, intelligent scripts were created to extract porosity features. A Multi-Layer Perceptron (MLP) was then developed and trained to predict porosity across the specimens’ height.. Given the size of the dataset and input features, the model's accuracy has proved to be optimal: The perceptron was able to predict reasonable porosity values for established and unknown combinations of input variables for two different sets of specimens. Finally, a scalability study was conducted to establish the impact of scaling on defect formation and the prediction of 3D printed parts. Porosity Defect Prediction Fused Deposition Modeling Machine Learning Convolutional Neural Networks Multi-layer Perceptron Additive Manufacturing 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. 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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-6049621","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":418311596,"identity":"0b59e5f1-80a7-4558-a489-9274051bfe3a","order_by":0,"name":"Khadija Ouajjani","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0003-1697-8698","institution":"National Institute of Aviation Research","correspondingAuthor":true,"prefix":"","firstName":"Khadija","middleName":"","lastName":"Ouajjani","suffix":""},{"id":418311597,"identity":"65e84d67-57fe-4063-bad7-b0260a72eba0","order_by":1,"name":"James Steck","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"James","middleName":"","lastName":"Steck","suffix":""},{"id":418311598,"identity":"e36dd51c-897e-4fb2-9836-fb0f71f1cd30","order_by":2,"name":"Gerardo Olivares","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Gerardo","middleName":"","lastName":"Olivares","suffix":""}],"badges":[],"createdAt":"2025-02-17 16:17:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6049621/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6049621/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":81477741,"identity":"63572fcb-459a-4ded-9f47-7346a6bd5063","added_by":"auto","created_at":"2025-04-27 13:05:04","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1370981,"visible":true,"origin":"","legend":"","description":"","filename":"KOIAMTLeveragingMachineLearningforPorosityPrediction.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6049621/v1_covered_6cc8873f-c995-482e-82a1-13ef83860fbf.pdf"}],"financialInterests":"","formattedTitle":"Leveraging Machine Learning for Porosity Prediction in AM using FDM for Pretrained Models and Process Development","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":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":"Porosity, Defect Prediction, Fused Deposition Modeling, Machine Learning, Convolutional Neural Networks, Multi-layer Perceptron, Additive Manufacturing","lastPublishedDoi":"10.21203/rs.3.rs-6049621/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6049621/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDue to the numerous independent parameters involved in additive manufacturing, 3D printing often delivers different quality of prints and requires an expensive trial-and-error approach before finding the optimal combination of the numerous and independent input variables.\u003c/p\u003e \u003cp\u003eMachine learning is, therefore, an ideal solution to this nonlinear problem, and provides an informed guess on printing parameters based on a minimal set of experiments. Using the case example of Fused Deposition Modeling, and, as a proof of concept, examining the porosity defect, a machine learning powered process is developed to predict the porosity defect's occurrence. It also facilitates the determination of the types of combinations of printing variables to ensure minimal defects.\u003c/p\u003e \u003cp\u003eSpecimens were 3D printed and CT-scanned. Raw datasets were collected in the form of grayscale image files (around 7,300 images) from the CT-scan. A machine learning image classifier was developed and trained to sort exploitable images from defective ones. To preprocess information for the classifier, intelligent scripts were created to extract porosity features. A Multi-Layer Perceptron (MLP) was then developed and trained to predict porosity across the specimens\u0026rsquo; height.. Given the size of the dataset and input features, the model's accuracy has proved to be optimal: The perceptron was able to predict reasonable porosity values for established and unknown combinations of input variables for two different sets of specimens. 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