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<p>The reverse engineering and manufacturing of sculptured surfaces remain challenging due to the limitations of traditional CAD/CAM workflows in handling complex, free-form geometries. This paper presents a novel, integrated framework that reconceptualizes the surface reconstruction problem through the lens of signal processing. The methodology begins with the acquisition of 3D point cloud data via a Coordinate Measuring Machine (CMM). A key novelty lies in the application of a two-dimensional Fourier Transform (2D-FT) to the height map of the digitized surface, enabling a frequency-domain analysis that diagnostically informs data preprocessing and optimizes machining direction. The framework subsequently evaluates and applies advanced geometric modeling techniques, including triangle-based cubic interpolation and Non-Uniform Rational B-Splines (NURBS), to generate a high-fidelity CAD model. Finally, the CAM phase incorporates geometric and kinematic analyses, such as curvature-adaptive tool path generation using Voronoi diagrams and 3D velocity vector calculation, to ensure gouge-free machining and adherence to machine kinematic constraints. Experimental results on a complex sample surface demonstrate the framework’s efficacy: the 2D-FT successfully identified dominant surface frequencies, guiding the machining strategy; cubic interpolation provided a superior surface fit with a 60% reduction in visual artifacts compared to linear interpolation; and kinematic analysis preemptively identified potential machining instability zones, potentially reducing servo errors by adaptively controlling feed rates. This work establishes a foundational step towards intelligent, fully automated manufacturing systems by providing a diagnostic, data-driven pipeline from measurement to machining, directly applicable to industries like aerospace and automotive mold-making.</p>

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A signal-processing-driven framework for the reconstruction and machining of sculptured surfaces from coordinate measurement data

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月5日

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  • Laith Al-Juboori

摘要

The reverse engineering and manufacturing of sculptured surfaces remain challenging due to the limitations of traditional CAD/CAM workflows in handling complex, free-form geometries. This paper presents a novel, integrated framework that reconceptualizes the surface reconstruction problem through the lens of signal processing. The methodology begins with the acquisition of 3D point cloud data via a Coordinate Measuring Machine (CMM). A key novelty lies in the application of a two-dimensional Fourier Transform (2D-FT) to the height map of the digitized surface, enabling a frequency-domain analysis that diagnostically informs data preprocessing and optimizes machining direction. The framework subsequently evaluates and applies advanced geometric modeling techniques, including triangle-based cubic interpolation and Non-Uniform Rational B-Splines (NURBS), to generate a high-fidelity CAD model. Finally, the CAM phase incorporates geometric and kinematic analyses, such as curvature-adaptive tool path generation using Voronoi diagrams and 3D velocity vector calculation, to ensure gouge-free machining and adherence to machine kinematic constraints. Experimental results on a complex sample surface demonstrate the framework’s efficacy: the 2D-FT successfully identified dominant surface frequencies, guiding the machining strategy; cubic interpolation provided a superior surface fit with a 60% reduction in visual artifacts compared to linear interpolation; and kinematic analysis preemptively identified potential machining instability zones, potentially reducing servo errors by adaptively controlling feed rates. This work establishes a foundational step towards intelligent, fully automated manufacturing systems by providing a diagnostic, data-driven pipeline from measurement to machining, directly applicable to industries like aerospace and automotive mold-making.