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<p>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.</p>

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Evaluating surface roughness in powder bed fusion via singular value decomposition

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月26日

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  • Iason Sideris,
  • Philippe Feser,
  • Michael R. Tucker,
  • Markus Bambach,
  • Mohamadreza Afrasiabi

摘要

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.