A comparison of six voxel-based data structures for part geometry and cutter-workpiece engagement computations in multi-axis virtual milling

A comparison of six voxel-based data structures for part geometry and cutter-workpiece engagement computations in multi-axis virtual milling | 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 A comparison of six voxel-based data structures for part geometry and cutter-workpiece engagement computations in multi-axis virtual milling Zhengwen Nie, Jiabin Cao, Yiyang Zhao, Lin Zhang, Xun Liu, Yan Xu, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4922052/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract This paper presents an efficient method for computing machined part geometry and cutter-workpiece engagement in general multi-axis milling. The milling process in this work is simulated by multi-level voxel update and edge intersection point update in sub-voxel level. The computed intersection points are used not only for building the machined part mesh model via dynamic marching cubes algorithm, but also for determining the instantaneous cutter-workpiece engagement region. The multi-level representation of the workpiece enables batch processing of affected voxels and minimal intersection calculations for more rapid and accurate modeling results. Virtual prototyping of the milled part geometry is critical for tool path planning, while cutter-workpiece engagement is one of the most important inputs for cutting force prediction. In a series of test cases, the proposed method has shown satisfactory modeling accuracy and higher efficiency compared to the tri-dexel model. More importantly, this volumetric method is implemented on six different data structures which are obtained by combining the fundamental spatial decomposition strategies. The efficiency characteristics of the resulting data structures are studied quantitatively which will help select the most appropriate scheme according to the performance requirements and realize its full potential for improved efficiency by choosing the optimal branching factors. Milling Voxel Part modeling Cutter-workpiece engagement Efficiency Full Text Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Major Revisions Needed 24 Apr, 2025 Reviewers agreed at journal 17 Aug, 2024 Reviewers invited by journal 17 Aug, 2024 Editor assigned by journal 16 Aug, 2024 First submitted to journal 15 Aug, 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. 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-4922052","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":341385885,"identity":"de660c05-bc13-4974-a4d1-d253178a2fc6","order_by":0,"name":"Zhengwen 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