{"paper_id":"1b002ed6-9c1a-4a33-9e7b-dba96b4cc2ce","body_text":"Innovative Tool Condition Classification: Utilizing Time-Frequency Moments as Inputs for BiLSTM Networks in Milling Processes | 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 Innovative Tool Condition Classification: Utilizing Time-Frequency Moments as Inputs for BiLSTM Networks in Milling Processes Achmad Zaki Rahman, Khairul Jauhari, Mahfudz Al Huda, Rusnaldy Rusnaldy, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4017609/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Aug, 2024 Read the published version in Journal of the Brazilian Society of Mechanical Sciences and Engineering → Version 1 posted 4 You are reading this latest preprint version Abstract Milling is one of the most important processes in the manufacturing industry, and it uses rotating cutting tools to sculpt raw materials into intricate shapes and structures. However, tool wear and breakage present significant challenges influenced by various factors, such as machining parameters and tool fatigue, which directly impact surface quality, dimensional accuracy, and production costs. Therefore, monitoring cutter wear conditions is essential for ensuring milling process efficiency. This study proposes applying BiLSTM networks to classify end mill tool conditions based on vibration signals. Significant improvements in classification accuracy are achieved by extracting features and utilizing spectrogram analysis. Specifically, utilizing dual spectral vibration signals increases the BiLSTM's average accuracy from 84.5–96.3%. These findings demonstrate the effectiveness of the proposed method for real-time tool condition monitoring in milling operations, offering potential benefits for manufacturing processes. Tool Condition Monitoring (TCM) tool wear end mill tool breakage spectrogram analysis Full Text Cite Share Download PDF Status: Published Journal Publication published 02 Aug, 2024 Read the published version in Journal of the Brazilian Society of Mechanical Sciences and Engineering → Version 1 posted Reviewers agreed at journal 25 Apr, 2024 Reviewers invited by journal 22 Apr, 2024 Editor assigned by journal 07 Mar, 2024 First submitted to journal 05 Mar, 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-4017609\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":294055174,\"identity\":\"96faab78-a199-4e99-a08c-0bbd113ff213\",\"order_by\":0,\"name\":\"Achmad Zaki 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