arrow
返回

Automatic Support Removal for Additive Manufacturing Post Processing

delete2019-10-01
delete33
PRE
AI
S
Saigopal Nelaturi
M
Morad Behandish *
A
Amir M. Mirzendehdel
J
Johan de Kleer
DOI:10.1016/j.cad.2019.05.030delete
delete原文链接
delete原文求助
delete分享
delete收藏
摘要

摘要

En 中文
An additive manufacturing (AM) process often produces a near-net shape that closely conforms to the intended design to be manufactured. It sometimes contains additional support structure (also called scaffolding), which has to be removed in post-processing. We describe an approach to automatically generate process plans for support removal using a multi-axis machining instrument. The goal is to fracture the contact regions between each support component and the part, and to do it in the most cost-effective order while avoiding collisions with evolving near-net shape, including the remaining support components. A recursive algorithm identifies a maximal collection of support components whose connection regions to the part are accessible as well as the orientations at which they can be removed at a given round. For every such region, the accessible orientations appear as a 'fiber' in the collision-free space of the evolving near-net shape and the tool assembly. To order the removal of accessible supports, the algorithm constructs a search graph whose edges are weighted by the Riemannian distance between the fibers. The least expensive process plan is obtained by solving a traveling salesman problem (TSP) over the search graph. The sequence of configurations obtained by solving TSP is used as the input to a motion planner that finds collision free paths to visit all accessible features. The resulting part without the support structure can then be finished using traditional machining to produce the intended design. The effectiveness of the method is demonstrated through benchmark examples in 3D. (C) 2019 Elsevier Ltd. All rights reserved.
Keyword:
Support removal
Additive manufacturing
Post-processing
Accessibility analysis
Configuration space
AI总结

AI总结

对已上传原文的论文进行重点信息的提取,主要内容包括:简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。

期刊

C
Computer-Aided Design
IF:
3.1
论文数:
3.1K
被引数:
6.4K

机构

暂无机构信息
引用论文

引用论文

err分享
err收藏
Trauma Quality Improvement Using Risk-Adjusted Outcomes
err2008-03-01
err0
PREAI
errShahid Shafi; Avery B. Nathens; Jennifer Parks; Henry M. Cryer; John J. Fildes; Larry M. Gentilello
err分享
err收藏
学者 查看更多内容