Online Surface Roughness Prediction in Wire-EDM: Experimental Comparative Study of Multiple-Linear Regression and Fuzzy Logic

Online Surface Roughness Prediction in Wire-EDM: Experimental Comparative Study of Multiple-Linear Regression and Fuzzy Logic | 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 Online Surface Roughness Prediction in Wire-EDM: Experimental Comparative Study of Multiple-Linear Regression and Fuzzy Logic Sean Yu, Joseph C. Chen, Ye Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4917068/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Wire Electrical Discharge Machining (Wire-EDM) is renowned for its precise material removal and superior surface roughness (Ra). However, in-process variations can lead to defects in mass production, necessitating time-consuming and labor-intensive post-production quality checks. This research aims to develop an online surface roughness prediction model using both multi-linear regression and Fuzzy Logic. Duplex stainless steel 2507 was selected as the test material, and a design of experiment was conducted with three controllable parameters: Wire Tension, Supply Voltage and Table Feed Rate, to simulate the variance and uncertainties in Wire-EDM operations. The top three real-time data inputs retrieved from the in-machine monitoring system - maximum voltage, current difference, and maximum feed rate - were identified through regression analysis. Predictive models using both Multi-linear regression and Fuzzy Logic were developed, achieving prediction accuracy of 94.47% and 95.75%, respectively. The results show that Fuzzy Logic exhibits a stronger correlation between predicted and actual surface roughness. This system offers a fast and reliable means of predicting surface roughness during Wire-EDM operations, thereby enhancing production efficiency and product quality. Future research could explore the integration of additional sensors or optimizing the prediction model further with alternative methodologies. Wire-EDM Multi-linear Regression Fuzzy logic Online prediction Surface Roughness Full Text Cite Share Download PDF Status: Posted Version 1 posted 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-4917068","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":341253257,"identity":"0ba8c3d2-2342-4655-8d58-2c4f74e29215","order_by":0,"name":"Sean Yu","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Sean","middleName":"","lastName":"Yu","suffix":""},{"id":341253258,"identity":"fd5200ac-d278-4682-bc0b-e425b077e287","order_by":1,"name":"Joseph C. 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