CFRP surface roughness prediction in machining by acoustic emission: a reliable machine learning study

CFRP surface roughness prediction in machining by acoustic emission: a reliable machine learning study | 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 CFRP surface roughness prediction in machining by acoustic emission: a reliable machine learning study Thiago Luiz Lara Oliveira, Marlon Mendes de Oliveira, Matheus Brendon Francisco, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6844429/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 18 Sep, 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 Composite materials such as polymer-reinforced carbon fiber have been increasingly used in various sectors due to their reduced mass and high mechanical strength. The aeronautical sector, for example, has shown that manufacturing the Boeing 787 with 50% of its structure using this type of material led to fuel savings of 25%. However, when using these materials, machining is used as a secondary manufacturing process for geometry adjustments and can represent considerable costs for the manufacturing of components. Developing tools that can assist in real-time control of the surface quality of these machined parts is essential to understand, control, and optimize the machining process. Few studies have been found successfully associating some machine learning models with surface roughness, and the vast majority are focused on metals and using the own machining parameters. This study investigates whether considering also the acoustic emission signal emitted during the machining process would help or not to improve the surface roughness prediction. For that, this study applies a set of more than twenty machine learning models tuned by Bayesian optimization to a dataset constructed using Optimal Design of Experiments for the milling of polymer-reinforced carbon fiber under four machining variables (cutting speed, tool condition, milling direction, and carbon fiber) and fifteen input parameters from acoustic emission analysis. Our studies show that it is possible to successfully predict surface roughness with these parameters, show the best machine learning algorithm and its hyperparameters for this purpose, and the six most relevant features out of nineteen. Machining CFRP acoustic emission machine learning optimization Full Text Cite Share Download PDF Status: Published Journal Publication published 18 Sep, 2025 Read the published version in The International Journal of Advanced Manufacturing Technology → Version 1 posted Editorial decision: Accept as is for Publication 08 Sep, 2025 Reviewers agreed at journal 17 Jun, 2025 Reviewers invited by journal 17 Jun, 2025 Editor assigned by journal 17 Jun, 2025 First submitted to journal 14 Jun, 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. 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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-6844429","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":472569030,"identity":"ea4cd8a3-67f1-4d47-b74f-beb5999f9e43","order_by":0,"name":"Thiago Luiz Lara 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