Modeling and Prediction of Laser Cladding Layer Morphology with Deep Learning

Modeling and Prediction of Laser Cladding Layer Morphology with Deep 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 Modeling and Prediction of Laser Cladding Layer Morphology with Deep Learning Juan Ma, Fanming Guo, Jihao Sang, Xining He, Jinguo Han, Yanhou Liu, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9286219/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract An ESFM network-based prediction model for cladding layers was developed using deep learning. A molten pool image dataset was constructed, followed by image preprocessing and augmentation. Using EfficientNetV2 as the baseline, the Fused-MBConv and MBConv blocks were optimized by integrating the Channel Shuffle mechanism from ShuffleNetV2, yielding the improved ESFM model through structural adjustment. The model was trained and validated on the molten pool dataset for classification prediction, with training time and accuracy compared quantitatively. Experimental results demonstrate that the improved ESFM model achieves fast convergence and 96% classification accuracy for cladding layer quality, confirming its effectiveness. This work provides a reliable approach for laser cladding quality prediction and supports further research toward stable deposition and process optimization. Laser cladding cladding layer quality prediction convolutional neural network EfficientNetV2 Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 03 May, 2026 Reviews received at journal 03 May, 2026 Reviews received at journal 01 May, 2026 Reviewers agreed at journal 30 Apr, 2026 Reviewers agreed at journal 28 Apr, 2026 Reviewers agreed at journal 11 Apr, 2026 Reviewers invited by journal 09 Apr, 2026 Editor assigned by journal 09 Apr, 2026 Submission checks completed at journal 07 Apr, 2026 First submitted to journal 31 Mar, 2026 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-9286219","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":623340865,"identity":"f20f8d42-78a4-4613-943d-18d7770520f2","order_by":0,"name":"Juan Ma","email":"","orcid":"","institution":"Shandong University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Juan","middleName":"","lastName":"Ma","suffix":""},{"id":623340866,"identity":"0d7a6848-6d6e-44ed-91ef-06e09fd362dc","order_by":1,"name":"Fanming Guo","email":"","orcid":"","institution":"Shandong University of 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