An explainable data-driven framework for material processing: integrating design of experiment and machine learning in laser ablation

An explainable data-driven framework for material processing: integrating design of experiment and machine learning in laser ablation | 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 An explainable data-driven framework for material processing: integrating design of experiment and machine learning in laser ablation Mariachiara Grande, Tommaso Gallingani, Romolo Laurita, Matteo Gherardi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7857002/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 24 Jan, 2026 Read the published version in The International Journal of Advanced Manufacturing Technology → Version 1 posted 4 You are reading this latest preprint version Abstract In the Industry 4.0 era, digitalization and automation of large-scale data analysis create new opportunities to enhance productivity, quality, and process efficiency. This paper proposes an integrated framework combining Design of Experiments (DoE), Machine Learning (ML), and Explainable AI (XAI) in a single tool for structured and interpretable data planning, processing, and optimization. A key innovation is a biplot representation that visualizes interactions among process parameters, enabling the identification of optimal variable ranges for ideal process configurations. The framework’s predictions were experimentally validated in laser ablation of glass and polymethylmethacrylate, achieving errors between 0% and 5%. Its modular architecture ensures adaptability across diverse manufacturing processes, offering a generalizable and interpretable approach for intelligent manufacturing. This work demonstrates how combining DoE, ML, and XAI can support data-driven decision-making and advance process understanding within the Industry 4.0 paradigm. machine learning design of experiment pipeline biplot graph laser ablation Full Text Supplementary Files glass.xlsx pmma.xlsx rawdata.xlsx Cite Share Download PDF Status: Published Journal Publication published 24 Jan, 2026 Read the published version in The International Journal of Advanced Manufacturing Technology → Version 1 posted Reviewers agreed at journal 30 Oct, 2025 Reviewers invited by journal 30 Oct, 2025 Editor assigned by journal 17 Oct, 2025 First submitted to journal 15 Oct, 2025 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-7857002","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":537480483,"identity":"5b344307-f977-4b2c-a1cf-d9979f5b4de9","order_by":0,"name":"Mariachiara 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