Login | Register

Adaptive slicing based on efficient profile analysis

Title:

Adaptive slicing based on efficient profile analysis

Mao, Huachao, Kwok, Tsz-Ho ORCID: https://orcid.org/0000-0001-7240-1426, Chen, Yong and Wang, Charlie C.L. (2019) Adaptive slicing based on efficient profile analysis. Computer-Aided Design, 107 . pp. 89-101. ISSN 00104485

[img]
Preview
Text (application/pdf)
AdapSlice18.pdf - Accepted Version
Available under License Creative Commons Attribution Non-commercial No Derivatives.
4MB

Official URL: http://dx.doi.org/10.1016/j.cad.2018.09.006

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.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Mechanical, Industrial and Aerospace Engineering
Item Type:Article
Refereed:Yes
Authors:Mao, Huachao and Kwok, Tsz-Ho and Chen, Yong and Wang, Charlie C.L.
Journal or Publication:Computer-Aided Design
Date:2019
Funders:
  • National Science Foundation (NSF)
  • Epstein Institute at University of Southern California
Digital Object Identifier (DOI):10.1016/j.cad.2018.09.006
Keywords:Additive manufacturing, Adaptive slicing, Geometric profile, Sampling
ID Code:985961
Deposited By: TSZ HO KWOK
Deposited On:11 Feb 2020 17:01
Last Modified:29 Sep 2020 00:00
All items in Spectrum are protected by copyright, with all rights reserved. The use of items is governed by Spectrum's terms of access.

Repository Staff Only: item control page

Downloads per month over past year

Research related to the current document (at the CORE website)
- Research related to the current document (at the CORE website)
Back to top Back to top