Octree based assembly sequence generation

R. Sung, J. Corney, D. Clark
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引用次数: 2

Abstract

This paper describes a system for the automatic recognition of assembly features and the generation of assembly/disassembly sequences. The paper starts by reviewing the nature and use of assembly features. One of the conclusions drawn from this survey is that the majority of assembly features involve sets of spatially adjacent faces. Two principle types of adjacency relationships are identified and an algorithm is presented for identifying assembly features which arise from “spatial” and “contact” face adjacency relationships (known as s-adjacency and c-adjacency respectively). The algorithm uses an octree representation of a B-rep model to support the geometric reasoning required to locate assembly features on disjoint bodies. A pointerless octree representation is generated by recursively sub-dividing the assembly model's bounding box into octants which are used to locate:Those portions of faces which are c-adjacent (i.e. they effectively touch within the tolerance of the octree). Those portions of faces which are s-adjacent to a nominated face. The resulting system can locate and partition spatially adjacent faces in a wide range of situations and a different resolutions. The assembly features located are recorded as attributes in the B-rep model and are then used to generate a disassembly sequence plan for the assembly. This sequence plan is represented by a transition state tree which incorporates knowledge of the availability of feasible gripping features. By way of illustration, the algorithm is applied to several trial components
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基于八叉树的装配序列生成
介绍了一种装配特征自动识别和装配/拆卸序列生成系统。本文首先回顾了汇编特征的性质和使用。从这项调查中得出的结论之一是,大多数装配特征涉及空间上相邻的面孔集。识别了两种主要类型的邻接关系,并提出了一种算法来识别由“空间”和“接触”面部邻接关系(分别称为s邻接和c邻接)产生的装配特征。该算法使用B-rep模型的八叉树表示来支持定位不相交物体上的装配特征所需的几何推理。一个无指针的八叉树表示是通过递归地将装配模型的边界框细分为用于定位的八叉树来生成的:那些与c相邻的面部部分(即它们在八叉树的容差范围内有效接触)。那些与指定的脸s相邻的部分。由此产生的系统可以在广泛的情况下和不同的分辨率下对空间相邻的人脸进行定位和划分。所定位的装配特征被记录为B-rep模型中的属性,然后用于生成装配的拆卸顺序计划。该序列计划由过渡状态树表示,过渡状态树包含了可行夹持特征的可用性知识。通过实例说明,将该算法应用于若干试验分量
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