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<p>Accurate detection of junction lines is critical in automated footwear manufacturing, as the junction line determines the processing path for roughening and adhesive application during sole–upper assembly. However, material flexibility, geometric variability among shoe styles, and deformation during pressing make reliable detection difficult in industrial environments. This study proposes an adaptive junction line detection method for robotic footwear assembly based on contour sensing and shoe-last-based geometric matching. The method captures assembly contours using a contour sensor and matches them with a reference shoe-last model to identify junction line positions without relying on visual appearance features. Experimental results demonstrate that, under the nominal local-normal-aligned sensing condition, the proposed approach achieves a maximum detection error below 0.3&#xa0;mm, with most junction line recognition processes completed within 10&#xa0;s and all detections finished within 20&#xa0;s. In addition, one shoe-last model can be used for multiple shoe styles formed on the same last and size, thereby reducing modeling cost and improving adaptability in industrial footwear manufacturing.</p>

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Adaptive junction line detection for robotic footwear assembly using contour sensing and shoe-last-based geometric matching

  • 期刊论文

  • 发表日期：

    2026年5月16日

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  • Cheng-Kai Huang,
  • Meng-En Hsieh

摘要

Accurate detection of junction lines is critical in automated footwear manufacturing, as the junction line determines the processing path for roughening and adhesive application during sole–upper assembly. However, material flexibility, geometric variability among shoe styles, and deformation during pressing make reliable detection difficult in industrial environments. This study proposes an adaptive junction line detection method for robotic footwear assembly based on contour sensing and shoe-last-based geometric matching. The method captures assembly contours using a contour sensor and matches them with a reference shoe-last model to identify junction line positions without relying on visual appearance features. Experimental results demonstrate that, under the nominal local-normal-aligned sensing condition, the proposed approach achieves a maximum detection error below 0.3 mm, with most junction line recognition processes completed within 10 s and all detections finished within 20 s. In addition, one shoe-last model can be used for multiple shoe styles formed on the same last and size, thereby reducing modeling cost and improving adaptability in industrial footwear manufacturing.