.png){kind=icon}
返回
On setup level tool sequence selection for 2.5-D pocket machining
.png){kind=icon}
PRE
AI
DOI：10.1016/j.rcim.2005.06.001.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This paper describes algorithms for efficiently machining,in entire setup. Previously, the author developed a graph based algorithm to find the optimal tool sequence for machining a single 2.5-axis pocket. This paper extends this algorithm for finding all efficient tool sequence to machine an entire setup. A setup consists of a set of features with precedence constraints, that are machined when the stock is clamped in a particular orientation. The precedence constraints between the features primarily result from nesting of some features within others. Four extensions to the basic graph algorithm are investigated in this research. The first method finds optimal tool sequences on a feature by feature basis. This is a local optimization method that does not consider inter feature tool-path interactions. The second method uses a composite graph for finding an efficient tool sequence for the entire Setup. The constrained graph and subgraph approaches have been developed for situations where different features in the setup have distinct critical tools. It is found that the first two methods can produce erroneous results which can lead to machine crashes and incomplete machining. Illustrative examples have been generated for each method. (C) 2005 Published by Elsevier Ltd.
Keyword:
CAPP
tool sequence selection
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
R
IF:
11.4
论文数:
3.4K
被引数:
1.3W
机构
暂无机构信息
引用论文
.png){kind=icon}
分享收藏分享收藏分享收藏Al2O3/MC particles reinforced MoFeCrTiWNbx high-entropy-alloy coatings prepared by laser cladding
PREAI
分享收藏分享收藏
查看更多内容