Prediction of surface roughness in aluminum alloy milling based on dynamic and static data fusion

Prediction of surface roughness in aluminum alloy milling based on dynamic and static data fusion | 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 Prediction of surface roughness in aluminum alloy milling based on dynamic and static data fusion Boyang Meng, Song Li, Zhongjie Li, Fuxing Xiang, Rui Ding, Caixu Yue, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9018628/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract In advanced manufacturing, surface roughness is a critical metric for evaluating the quality of machined workpieces, which directly impacts product quality and manufacturing efficiency. However, traditional roughness measurement methods, such as contact profilometers and optical inspection technologies, require precise calibration, are susceptible to environmental influences, and have limited data acquisition capabilities. These methods result in high costs, low efficiency, and an inability to meet the demands of real-time monitoring in actual production. To address these challenges, this study proposes a multi-source signal fusion model architecture that integrates parameter conditions, bidirectional cross-attention, and global encoding mechanisms. First, the collected sensor signals undergo preprocessing, feature extraction, and principal component analysis dimensionality reduction. The reduced-dimensional current and cutting force features serve as dynamic inputs, and process parameters serve as static conditions. This approach provides the model with high-quality data, thereby enhancing prediction accuracy. Second, the reduced-dimensional current and cutting force features, along with the process parameters, are uniformly encoded into token sequences. The FiLM linear modulation mechanism is then applied to adaptively scale and translate these features, improving prediction accuracy under varying parameter conditions. Finally, bidirectional cross-attention learns the relationship between the current and the cutting force. This relationship is then fused with the process parameters for encoding. A shallow neural network is used for regression prediction. The results validate the model's effectiveness in predicting surface roughness, offering a novel solution for this application. Dynamic and static data Surface roughness Linear modulation Bidirectional cross-attention fusion Full Text Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 29 Mar, 2026 Reviewers invited by journal 29 Mar, 2026 Editor assigned by journal 16 Mar, 2026 First submitted to journal 13 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. 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-9018628","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":613988397,"identity":"fb93c865-2450-4e6a-a744-af9ca2292201","order_by":0,"name":"Boyang 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