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Efficient construction of the medial axis for a CAD model using parallel computing

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Abstract

As a simplified representation of a geometric model, the medial axis (MA) has been used in a wide range of engineering applications. While obtaining the true MA of a complicated CAD model is known to be a difficult task, current research is predominantly focused on computing its approximate MA instead. To improve its quality, this work develops a novel and efficient method for obtaining a high-quality MA composed of MA faces for a CAD model. Specifically, an MA point is computed using a dual-normal-tracing algorithm for each sample point. This algorithm can be implemented through GPU-enabled parallel computing and be executed in an iterative manner until MA points have been found for all sample points. After the iteration is completed, the MA points generated are then converted into the resultant MA by evaluating the topological connectivities of their corresponding sample points. Finally, the resultant MA is converted into MA faces using the information of boundary CAD faces. The proposed method is evaluated by analyzing its complexity and robustness, discussing its applicability and testing its performance in a couple of computational experiments. As shown in the evaluation, this method is easy to implement through exploiting parallel computing and can support effective and high-quality MA generation for a CAD model.

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Acknowledgements

The authors appreciate the support from the National Key Technology Support Program (Grant no. 2014BAD04B00), the National Science Foundation of China (Grant nos. 61702074, 61572427 and 61672247), the China Postdoctoral Science Foundation Funded Project (Grant no. 2017M621123) and the Fundamental Research Funds for the Central Universities (Grant nos. 3132017117).

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Correspondence to Yusheng Liu or Jianjun Zhao.

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Zhu, H., Liu, Y., Wang, H. et al. Efficient construction of the medial axis for a CAD model using parallel computing. Engineering with Computers 34, 413–429 (2018). https://doi.org/10.1007/s00366-017-0549-3

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  • DOI: https://doi.org/10.1007/s00366-017-0549-3

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