Data-Driven Discovery of Process–Structure Relationships in Additive Manufacturing via Featurization from Kinetic Monte Carlo Simulations and Interpretable Machine Learning

Data-Driven Discovery of Process–Structure Relationships in Additive Manufacturing via Featurization from Kinetic Monte Carlo Simulations and Interpretable Machine Learning | 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 Data-Driven Discovery of Process–Structure Relationships in Additive Manufacturing via Featurization from Kinetic Monte Carlo Simulations and Interpretable Machine Learning Dipayan Sanpui, Henry Chan, Aditya Koneru, Suvo Banik, Sukriti Manna, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9163057/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 Understanding process–structure linkages is essential for accelerating microstructure design in additive manufacturing (AM). Here, we develop a computational framework that integrates automated feature extraction from kinetic Monte Carlo (kMC) simulations with interpretable machine learning (ML) models to predict grain-scale morphological features from processing conditions. A dataset of 1,524 simulated three-dimensional polycrystalline microstructures was analyzed to extract descriptors including grain size, surface-to-volume ratio, sphericity, and roundness. These were correlated with process parameters such as scan velocity and heat-affected zone (HAZ) dimensions. Four ML models—Random Forest, Gradient Boosting Machine, Extreme Gradient Boosting (XGBoost), and neural networks—were implemented to benchmark predictive accuracy and interpretability. The ensemble-based models were chosen for their robustness and efficiency on structured datasets, while neural networks were included to capture nonlinear and higher-order correlations. Among them, XGBoost achieved the highest predictive performance (R² = 0.977, MAE = 6.6 pixels). Model explainability was provided using Shapley Additive Explanations (SHAP), revealing that scan velocity and HAZ parameters strongly govern grain morphology. By coupling interpretability with physics-informed learning, our framework bridges high-fidelity simulation and rapid prediction, enabling scalable microstructure design in additive manufacturing. Mechanical Engineering Materials Theory and Modeling Artificial Intelligence and Machine Learning 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. 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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-9163057","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":608502756,"identity":"07635ad4-12aa-4997-a29a-2160f7865ba9","order_by":0,"name":"Dipayan Sanpui","email":"","orcid":"https://orcid.org/0000-0002-1354-2234","institution":"Department of Mechanical and Industrial Engineering, University of Illinois, Chicago, Illinois 60607, United 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manufacturing (AM). Here, we develop a computational framework that integrates automated feature extraction from kinetic Monte Carlo (kMC) simulations with interpretable machine learning (ML) models to predict grain-scale morphological features from processing conditions. A dataset of 1,524 simulated three-dimensional polycrystalline microstructures was analyzed to extract descriptors including grain size, surface-to-volume ratio, sphericity, and roundness. These were correlated with process parameters such as scan velocity and heat-affected zone (HAZ) dimensions. Four ML models—Random Forest, Gradient Boosting Machine, Extreme Gradient Boosting (XGBoost), and neural networks—were implemented to benchmark predictive accuracy and interpretability. The ensemble-based models were chosen for their robustness and efficiency on structured datasets, while neural networks were included to capture nonlinear and higher-order correlations. Among them, XGBoost achieved the highest predictive performance (R² = 0.977, MAE = 6.6 pixels). Model explainability was provided using Shapley Additive Explanations (SHAP), revealing that scan velocity and HAZ parameters strongly govern grain morphology. By coupling interpretability with physics-informed learning, our framework bridges high-fidelity simulation and rapid prediction, enabling scalable microstructure design in additive manufacturing.\u003c/p\u003e","manuscriptTitle":"Data-Driven Discovery of Process–Structure Relationships in Additive Manufacturing via Featurization from Kinetic Monte Carlo Simulations and Interpretable Machine Learning","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-20 05:43:50","doi":"10.21203/rs.3.rs-9163057/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"b4464a08-2080-4a08-b6fa-de7b1e751a90","owner":[],"postedDate":"March 20th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":64751935,"name":"Mechanical Engineering"},{"id":64751936,"name":"Materials Theory and Modeling"},{"id":64751937,"name":"Artificial Intelligence and Machine Learning"}],"tags":[],"updatedAt":"2026-03-20T05:43:50+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-20 05:43:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9163057","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9163057","identity":"rs-9163057","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}