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<p>In modern manufacturing, precise measurement of components with complex curved geometries remains challenging, especially for quantitative size measurements at the micro scale of 5–500&#xa0;μm. Conventional two-dimensional imaging methods suffer from limited depth-measurement capabilities and cannot capture the complete three-dimensional surface topology with metrological accuracy. This paper presents an integrated intelligent measurement framework that combines multi-line 3D laser profilometry with a deep convolutional neural network for the automated quantitative characterization of micro-scale surface anomalies on non-planar surfaces. After compression and feature-preserving smoothing based on a multi-resolution octree, the raw point cloud data is fed into an enhanced U-Net architecture improved with an attention mechanism. The network simultaneously performs defect segmentation and dimensional measurement across five categories: micro-cracks, pinholes, scratches, contaminant particles, and surface roughness anomalies. Experimental validation on pharmaceutical capsule surfaces, precision optical components, and semiconductor wafers demonstrates a measurement accuracy of 97.3% for dimensional anomalies as small as 5&#xa0;μm. Within the critical 5–20&#xa0;μm measurement range, detection recall rates reach 78–91%, with false-positive rates below 2.1%. Measurement uncertainty in high-curvature regions (κ &gt; 0.25&#xa0;mm<sup>−1</sup>) contributes to the lower bound of this recall range, as quantified in our failure mode analysis. The average measurement throughput achieves 850 mm<sup>2</sup>/s, representing a 4–7 × improvement over commercial profilers, while the per-part measurement latency is maintained at 245&#xa0;ms, making the framework suitable for inline metrology applications.</p>

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Intelligent detection system for micro-nano defects on complex curved surfaces based on 3D laser scanning and deep learning

  • 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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  • Zhiwen Xiong,
  • Yuanchun Li

摘要

In modern manufacturing, precise measurement of components with complex curved geometries remains challenging, especially for quantitative size measurements at the micro scale of 5–500 μm. Conventional two-dimensional imaging methods suffer from limited depth-measurement capabilities and cannot capture the complete three-dimensional surface topology with metrological accuracy. This paper presents an integrated intelligent measurement framework that combines multi-line 3D laser profilometry with a deep convolutional neural network for the automated quantitative characterization of micro-scale surface anomalies on non-planar surfaces. After compression and feature-preserving smoothing based on a multi-resolution octree, the raw point cloud data is fed into an enhanced U-Net architecture improved with an attention mechanism. The network simultaneously performs defect segmentation and dimensional measurement across five categories: micro-cracks, pinholes, scratches, contaminant particles, and surface roughness anomalies. Experimental validation on pharmaceutical capsule surfaces, precision optical components, and semiconductor wafers demonstrates a measurement accuracy of 97.3% for dimensional anomalies as small as 5 μm. Within the critical 5–20 μm measurement range, detection recall rates reach 78–91%, with false-positive rates below 2.1%. Measurement uncertainty in high-curvature regions (κ > 0.25 mm−1) contributes to the lower bound of this recall range, as quantified in our failure mode analysis. The average measurement throughput achieves 850 mm2/s, representing a 4–7 × improvement over commercial profilers, while the per-part measurement latency is maintained at 245 ms, making the framework suitable for inline metrology applications.