4.7 Article

Adaptive slicing based on efficient profile analysis

期刊

COMPUTER-AIDED DESIGN
卷 107, 期 -, 页码 89-101

出版社

ELSEVIER SCI LTD
DOI: 10.1016/j.cad.2018.09.006

关键词

Additive manufacturing; Adaptive slicing; Geometric profile; Sampling

资金

  1. National Science Foundation (NSF) [CMMI 1151191]
  2. Epstein Institute at University of Southern California

向作者/读者索取更多资源

Adaptive slicing is an important computational task required in the layer-based manufacturing process. Its purpose is to find an optimal trade-off between the fabrication time (number of layers) and the surface quality (geometric deviation error). Most of the traditional adaptive slicing algorithms are computationally expensive or only based on local evaluation of errors. To tackle these problems, we introduce a method to efficiently generate slicing plans by a new metric profile that can characterize the distribution of deviation errors along the building direction. By generalizing the conventional error metrics, the proposed metric profile is a density function of deviation errors, which measures the global deviation errors rather than the in-plane local geometry errors used in most prior methods. Slicing can be efficiently evaluated based on metric profiles in contrast to the expensive computation on models in boundary-representation. An efficient algorithm based on dynamic programming is proposed to find the best slicing plan. Our adaptive slicing method can also be applied to models with weighted features and can serve as the inner loop to search the best building direction. The performance of our approach is demonstrated by experimental tests on different examples. (C) 2018 Elsevier Ltd. All rights reserved.

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