Adaptive slicing based on efficient profile analysis. (February 2019)

• Record Type: Journal Article
• Authors: Mao, Huachao; Kwok, Tsz-Ho; Chen, Yong; Wang, Charlie C.L.
• Is Part Of: Computer aided design. Volume 107(2019)
• Page Start: 89
• Page End: 101
• Publication Date: 2019-02
• Subjects: Additive manufacturing -- Adaptive slicing -- Geometric profile -- Sampling; Computer-aided design -- Periodicals; Engineering design -- Data processing -- Periodicals; Computer graphics -- Periodicals; Conception technique -- Informatique -- Périodiques; Infographie -- Périodiques; Computer graphics; Engineering design -- Data processing; Periodicals; Electronic journals; 620.00420285
• DOI: 10.1016/j.cad.2018.09.006
• Languages: English
• ISSNs: 0010-4485

Abstract

Abstract: 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. Highlights: A new profile-based adaptive slicing framework is presented with good properties of accurate and efficient. An optimization algorithm based on dynamic programming is presented to find the best slicing plan for an input CAD model. The analysis taken in our algorithm is based on a metric profile that can be generated by GPU-accelerated techniques within seconds. The formulation can be easily extended to integrate different commonly used error metrics, as well as the user specified salience.