Enhancing Prediction of Magnetic Properties in Additive Manufacturing Products through a 3D Convolutional Vision Transformer Model

Enhancing Prediction of Magnetic Properties in Additive Manufacturing Products through a 3D Convolutional Vision Transformer Model | 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 Enhancing Prediction of Magnetic Properties in Additive Manufacturing Products through a 3D Convolutional Vision Transformer Model Ming-Huwi Horng, Lien-Kai Chang, Po-Chun Chen, Mi-Ching Tsai, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5317796/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 25 Mar, 2025 Read the published version in The International Journal of Advanced Manufacturing Technology → Version 1 posted 5 You are reading this latest preprint version Abstract With the advancement of metal additive manufacturing technology, selective laser melting (SLM) has gained significant prominence in industrial manufacturing. However, traditional methods for measuring magnetic properties need to improve efficiency and accuracy for modern manufacturing demands. This study employs a 3D convolutional vision transformer (3D-CvT) model to rapidly and accurately predict magnetic properties in products created through the SLM process. The 3D-CvT model merges the advantages of convolutional neural networks and vision transformer, enhancing the understanding of spatial and feature information. As a result, it achieves higher accuracy and efficiency in predicting magnetic properties compared to traditional machine learning methods. Utilizing heatmap technology, this model visually displays areas of the image that significantly impact prediction outcomes. These heat maps facilitate an effective understanding of how image features influence the magnetic properties of the products. Experiments were conducted on 200 specimens, with the results indicating that the 3D-CvT model's predictions showed low mean square error (MSE), low mean absolute error (MAE), and high R-squared (R 2 ) values compared to the actual measured values (ground truth). This indicates strong consistency between the predicted and measured magnetic properties. Additionally, a Student's t-test was performed, and the corresponding p-values exceeded 0.05, suggesting no statistically significant difference between the predicted and actual measured values for the target population. elected laser melting Magnetic property prediction Deep learning Convolutional vision transformer Student's t-test Heat map. Full Text Cite Share Download PDF Status: Published Journal Publication published 25 Mar, 2025 Read the published version in The International Journal of Advanced Manufacturing Technology → Version 1 posted Editorial decision: Major Revisions Needed 15 Jan, 2025 Reviewers agreed at journal 27 Oct, 2024 Reviewers invited by journal 25 Oct, 2024 Editor assigned by journal 25 Oct, 2024 First submitted to journal 24 Oct, 2024 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. 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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-5317796","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":370522411,"identity":"7f6c581c-8156-49a2-a027-30ab87a4ad14","order_by":0,"name":"Ming-Huwi 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Technology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"elected laser melting, Magnetic property prediction, Deep learning, Convolutional vision transformer, Student's t-test, Heat map.","lastPublishedDoi":"10.21203/rs.3.rs-5317796/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5317796/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWith the advancement of metal additive manufacturing technology, selective laser melting (SLM) has gained significant prominence in industrial manufacturing. However, traditional methods for measuring magnetic properties need to improve efficiency and accuracy for modern manufacturing demands. This study employs a 3D convolutional vision transformer (3D-CvT) model to rapidly and accurately predict magnetic properties in products created through the SLM process.\u003c/p\u003e\n\u003cp\u003eThe 3D-CvT model merges the advantages of convolutional neural networks and vision transformer, enhancing the understanding of spatial and feature information. As a result, it achieves higher accuracy and efficiency in predicting magnetic properties compared to traditional machine learning methods. Utilizing heatmap technology, this model visually displays areas of the image that significantly impact prediction outcomes. 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