Evaluating surface roughness in powder bed fusion via singular value decomposition

Evaluating surface roughness in powder bed fusion via singular value decomposition | 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 Article Evaluating surface roughness in powder bed fusion via singular value decomposition Iason Sideris, Philippe Feser, Michael R Tucker, Markus Bambach, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8435666/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Surface roughness in laser-based metal powder bed fusion (PBF-LB/M) plays a critical role in determining both functional performance and the quality of downstream manufacturing steps. This characterization requires extracting roughness from an areal height map, typically obtained using optical microscopy or contact profilometry. However, accurately extracting roughness from areal topography remains challenging due to non-planar surfaces, pronounced waviness, and the computational cost of conventional post-processing algorithms. This article presents a simple and computationally efficient method that isolates surface roughness from microscopy depth fields using singular value decomposition (SVD). The approach requires no pre-training and, on the datasets studied, surpasses existing ISO 25178-compliant filtering workflows in both accuracy and runtime. Physical sciences/Engineering Physical sciences/Mathematics and computing Physical sciences/Optics and photonics additive manufacturing laser-based metal powder bed fusion surface roughness ISO singular value decomposition optical microscopy Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 23 Feb, 2026 Reviews received at journal 06 Feb, 2026 Reviews received at journal 05 Feb, 2026 Reviewers agreed at journal 26 Jan, 2026 Reviewers agreed at journal 18 Jan, 2026 Reviewers agreed at journal 17 Jan, 2026 Reviewers invited by journal 15 Jan, 2026 Editor assigned by journal 13 Jan, 2026 Submission checks completed at journal 30 Dec, 2025 First submitted to journal 23 Dec, 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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