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<p>Accurate surface profile evaluation of CFRP circular cell honeycomb structures is essential for ensuring the performance of composite sandwich components. However, their thin-walled geometry, periodic topology, and measurement noise pose challenges for conventional techniques. This study presents an integrated approach combining line-laser measurement, self-calibration, and geometry-guided measurement data processing. The self-calibration module compensates for sensor deflection using the intrinsic structure of the workpiece, eliminating the need for external references. A geometry-aware segmentation strategy is applied to isolate meaningful surface units, followed by bilateral filtering to suppress fine-scale noise while preserving edge features. Filtering performance is quantitatively assessed using local curvature heatmaps, root mean square error (RMSE), and signal-to-noise ratio (SNR). Experimental results demonstrate that the proposed method achieves profile deviations within 10&#xa0;μm compared to CMM measurements across both planar and curved surfaces. The results confirm the accuracy and practicality of proposed method for evaluating complex structures under real machining conditions.</p>

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A Machined Surface Profile Error Evaluation Method for CFRP Circular Cell Honeycomb Based on Grid-Shaped Surface Profile Extraction

  • 期刊论文

  • 发表日期：

    2025年6月26日

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  • Yan Wang,
  • Zhigang Dong,
  • Junchao Tian,
  • Renke Kang,
  • Yan Qin

摘要

Accurate surface profile evaluation of CFRP circular cell honeycomb structures is essential for ensuring the performance of composite sandwich components. However, their thin-walled geometry, periodic topology, and measurement noise pose challenges for conventional techniques. This study presents an integrated approach combining line-laser measurement, self-calibration, and geometry-guided measurement data processing. The self-calibration module compensates for sensor deflection using the intrinsic structure of the workpiece, eliminating the need for external references. A geometry-aware segmentation strategy is applied to isolate meaningful surface units, followed by bilateral filtering to suppress fine-scale noise while preserving edge features. Filtering performance is quantitatively assessed using local curvature heatmaps, root mean square error (RMSE), and signal-to-noise ratio (SNR). Experimental results demonstrate that the proposed method achieves profile deviations within 10 μm compared to CMM measurements across both planar and curved surfaces. The results confirm the accuracy and practicality of proposed method for evaluating complex structures under real machining conditions.