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<p>To address the challenges of detail feature loss or deviation in additive manufacturing (AM) of complex parts, this study proposes a five-axis support-free adaptive slicing optimization method specifically designed for STL model detail features (SMDF). This method establishes a dual-threshold decision mechanism based on angle variation threshold and area variation threshold to accurately identify model features. The average cross-sectional area difference is used as the evaluation metric for model accuracy. Based on the nonlinear mapping relationship between the skeleton curve curvature, model contour curvature, and the average cross-sectional area difference, a dual-dimensional curvature coupling model for the average cross-sectional area difference was established, and a calculation formula for adaptive slicing thickness was derived. Furthermore, based on the feature discrimination criterion, a five-axis support-free adaptive slicing thickness optimization formula is derived, incorporating the dual-threshold mechanism to enhance printing fidelity. The results demonstrate that: compared to conventional five-axis uniform-thickness slicing (FUS) and five-axis support-free adaptive slicing algorithms (FSAS), this method improves geometric accuracy by 52.23% and 11.86%. Crucially, the method effectively mitigates detail feature loss or deviation, ensuring high-fidelity reproduction of complex geometries. The proposed five-axis support-free adaptive slicing method successfully enhances geometric accuracy while preserving printing efficiency, significantly reducing detail feature loss or deviation in parts.</p>

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Five-axis support-free adaptive slicing optimization for STL model detail features

  • 期刊论文

  • 发表日期：

    2025年9月23日

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  • Yan Wu,
  • Jiale Hu,
  • Hongyu Gao,
  • Xiaoshuai Chen,
  • Muchun Fan,
  • Feng Hong

摘要

To address the challenges of detail feature loss or deviation in additive manufacturing (AM) of complex parts, this study proposes a five-axis support-free adaptive slicing optimization method specifically designed for STL model detail features (SMDF). This method establishes a dual-threshold decision mechanism based on angle variation threshold and area variation threshold to accurately identify model features. The average cross-sectional area difference is used as the evaluation metric for model accuracy. Based on the nonlinear mapping relationship between the skeleton curve curvature, model contour curvature, and the average cross-sectional area difference, a dual-dimensional curvature coupling model for the average cross-sectional area difference was established, and a calculation formula for adaptive slicing thickness was derived. Furthermore, based on the feature discrimination criterion, a five-axis support-free adaptive slicing thickness optimization formula is derived, incorporating the dual-threshold mechanism to enhance printing fidelity. The results demonstrate that: compared to conventional five-axis uniform-thickness slicing (FUS) and five-axis support-free adaptive slicing algorithms (FSAS), this method improves geometric accuracy by 52.23% and 11.86%. Crucially, the method effectively mitigates detail feature loss or deviation, ensuring high-fidelity reproduction of complex geometries. The proposed five-axis support-free adaptive slicing method successfully enhances geometric accuracy while preserving printing efficiency, significantly reducing detail feature loss or deviation in parts.