Automatic Assembly Feature Recognition and Disassembly Sequence Generation

2001 ◽  
Vol 1 (4) ◽  
pp. 291-299 ◽  
Author(s):  
Raymond C. W. Sung ◽  
Jonathan R. Corney ◽  
Doug E. R. Clark

This paper describes a system for the automatic recognition of assembly features and the generation of 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: 1. Those portions of faces which are c-adjacent (i.e. they effectively touch within the tolerance of the octree). 2. 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 at 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

Author(s):  
Raymond C. W. Sung ◽  
Jonathan R. Corney ◽  
Doug E. R. Clark

Abstract This paper reviews 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, these are features which arise from these “spatial” and “contact” face adjacency relationships (known as s- 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. Once all the adjacent faces which form features have been located, they are used to partition the original faces of the assembly into adjacent and non-adjacent portions. The resulting system can locate and partition spatially adjacent faces in a wide range of situations and at different resolutions. By way of illustration, the algorithm is applied to a trial component.


2012 ◽  
Vol 580 ◽  
pp. 87-90
Author(s):  
Bin Fang ◽  
Liang Tian

No matter the bottom-up design mode is adopted or the top-down design is chosen, one-to-one dimension relation can be realized alone among assembly-components in three-dimensional CAD systems.In the paper, a method of automatic recognition is put forward based on assembly features in UG and various definitions as well as expressional methods of assembly features are analyzed. The interrelated dimension relation is established on account of the fit dimension chain. The converse parametric design is realized besides an independent module in UG is developed to achieve the function.


Author(s):  
Prabath Vemulapalli ◽  
Prashant Mohan ◽  
Jami J. Shah ◽  
Joseph K. Davidson

Tolerance allocation is important aspect in designing as well as manufacturing. Mating features in an assembly are important from the tolerance point of view and govern the tolerance schema. Presence of patterns within these features also plays an important role in the allocation of different tolerance classes. Identification of these assembly features and patterns are previously done manually. This research is aimed at automating these processes. The automation starts with the recognition of the assembly features in the assembly. The algorithms for feature recognition are designed such that they can handle any user defined assembly feature. The input for feature recognition is a STEP file containing information of the assembly. And the output file contains information of the recognized assembly features. Then patterns are identified from these assembly features. This paper discusses these two processes in detail. Also to facilitate the user, define new assembly features an alternate system called assembly feature tutor is developed. This paper also explains the working of this tutor.


Sadhana ◽  
2021 ◽  
Vol 46 (1) ◽  
Author(s):  
Bala Murali Gunji ◽  
Sai Krishna Pabba ◽  
Inder Raj Singh Rajaram ◽  
Paul Satwik Sorakayala ◽  
Arnav Dubey ◽  
...  

Author(s):  
William C. Regli ◽  
Satyandra K. Gupta ◽  
Dana S. Nau

Abstract While automated recognition of features has been attempted for a wide range of applications, no single existing approach possesses the functionality required to perform manufacturability analysis. In this paper, we present a methodology for taking a CAD model of a part and extracting a set of machinable features that contains the complete set of alternative interpretations of the part as collections of MRSEVs (Material Removal Shape Element Volumes, a STEP-based library of machining features). The approach handles a variety of features including those describing holes, pockets, slots, and chamfering and filleting operations. In addition, the approach considers accessibility constraints for these features, has an worst-case algorithmic time complexity quadratic in the number of solid modeling operations, and modifies features recognized to account for available tooling and produce more realistic volumes for manufacturability analysis.


2002 ◽  
Vol 40 (13) ◽  
pp. 3183-3198 ◽  
Author(s):  
Rahul Rai ◽  
Varun Rai ◽  
M. K. Tiwari ◽  
Venkat Allada

2007 ◽  
Vol 45 (18-19) ◽  
pp. 4537-4554 ◽  
Author(s):  
Yoshiaki Shimizu ◽  
Kyohei Tsuji ◽  
Masayuki Nomura

2000 ◽  
Vol 19 (2) ◽  
pp. 73-82 ◽  
Author(s):  
Hsin-Hao (Tom) Huang ◽  
Michael H. Wang ◽  
Michael R. Johnson

2019 ◽  
Vol 26 (2) ◽  
pp. 111-118
Author(s):  
Atsuko ENOMOTO ◽  
Hiroshi SEKI ◽  
Takuya YOSHIDA ◽  
Junya TAHATA ◽  
Mitsutaka IMAMURA ◽  
...  

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