Volume decomposition and feature recognition: part 1—polyhedral objects

Abstract A new feature recognition method is presented that generates volumetric feature representations from conventional boundary representations of mechanical parts. Recognition is accomplished by decomposing the known total feature volume of a part into a set of smaller volumes through analytic face extension. The decomposed volumes are combined to generate an initial set of features. Alternative sets of features are generated by maintaining and evaluating information on intersections of the initial feature set. The capabilities of the method are demonstrated through both a hypothetical and a real world design example. The method’s ability to locate features despite interactions with other features, and its ability to generate alternative sets of features, distinguishes it from existing recognition techniques.

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Maximal Volume Decomposition and its Application to Feature Recognition

ASME 1995 15th International Computers in Engineering Conference and the ASME 1995 9th Annual Engineering Database Symposium ◽

10.1115/cie1995-0788 ◽

1995 ◽

Author(s):

Parag Dave ◽

Hiroshi Sakurai

Keyword(s):

Decomposition Method ◽

Feature Recognition ◽

Graph Matching ◽

Raw Material ◽

Maximal Volume ◽

Object A ◽

Finished Part ◽

Volume Decomposition ◽

Minimal Cells ◽

Volume Difference

Abstract A method has been developed that decomposes an object having both planar and curved faces into volumes, called maximal volumes, using the halfspaces of the object. A maximal volume has as few concave edges as possible without introducing additional halfspaces. The object is first decomposed into minimal cells by extending the faces of the object. These minimal cells are then composed to form maximal volumes. The combinations of such minimal cells that result in maximal volumes are searched efficiently by examining the relationships among those minimal cells. With this decomposition method, a delta volume, which is the volume difference between the raw material and the finished part, is decomposed into maximal volumes. By subtracting maximal volumes from each other in different orders and applying graph matching to the resulting volumes, multiple interpretations of features can be generated.

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Representation and Mapping of Geometric Dimensions From Design to Manufacturing

Volume 3: 22nd Design Automation Conference ◽

10.1115/96-detc/dac-1481 ◽

1996 ◽

Author(s):

Jami J. Shah ◽

Yong Yan

Keyword(s):

Process Planning ◽

Degrees Of Freedom ◽

Feature Recognition ◽

Machining Features ◽

Design Models ◽

Volume Decomposition ◽

Recognition Systems ◽

Part Dimension

Abstract This paper describes the development of a dimension model for use in both design and process planning. The model also facilitates the converting of dimensions and tolerances (D&T) from design models to machining features extracted automatically by feature recognition systems. The model is based on relative degrees of freedom of geometric entities, such as edges and faces of a part. Dimension graphs are created based on the degrees of freedom. The model allows dimension specification, dimension scheme modification, and dimension scheme validation. A methodology to automatically determine the dimensions of machining volumes obtained by volume decomposition is also described.

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Automatic feature recognition of regular features for symmetrical and non-symmetrical cylinder part using volume decomposition method

Engineering With Computers ◽

10.1007/s00366-018-0576-8 ◽

2018 ◽

Vol 34 (4) ◽

pp. 843-863 ◽

Cited By ~ 5

Author(s):

Ahmad Faiz Zubair ◽

Mohd Salman Abu Mansor

Keyword(s):

Decomposition Method ◽

Feature Recognition ◽

Volume Decomposition ◽

Automatic Feature Recognition

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A Convergent Convex Decomposition of Polyhedral Objects

Journal of Mechanical Design ◽

10.1115/1.2926575 ◽

1992 ◽

Vol 114 (3) ◽

pp. 468-476 ◽

Cited By ~ 32

Author(s):

Yong Se Kim ◽

D. J. Wilde

Keyword(s):

Decomposition Method ◽

Feature Recognition ◽

Current Method ◽

Convex Decomposition ◽

Object Based ◽

Geometric Objects ◽

Polyhedral Objects ◽

Volumetric Representation ◽

Boundary Information ◽

The Given

A convex decomposition method, called Alternating Sum of Volumes (ASV), uses convex hulls and set difference operations. ASV decomposition, however, may not converge, which severely limits the domain of geometric objects that the current method can handle. We investigate the cause of non-convergence and present a remedy; we propose a new convex decomposition called Alternating Sum of Volumes with Partitioning (ASVP) and prove its convergence. ASVP decomposition is a hierarchical volumetric representation which is obtained from the boundary information of the given object based on convexity. As an application, from feature recognition by ASVP decomposition if briefly discussed.

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Feature Recognition by Volume Decomposition Using Half-Space Partitioning

20th Design Automation Conference: Volume 1 — Dynamic Mechanical Systems; Geometric Modeling and Features; Concurrent Engineering ◽

10.1115/detc1994-0100 ◽

1994 ◽

Author(s):

Yan Shen ◽

Jami J. Shah

Keyword(s):

Half Space ◽

Decomposition Method ◽

Feature Recognition ◽

Boundary Representation ◽

Solid Model ◽

Space Partitioning ◽

Machining Features ◽

Convex Decomposition ◽

Volume Decomposition ◽

Convex Cell

Abstract A volume decomposition method called minimum convex decomposition by half space partitioning has been developed to recognize machining features from the boundary representation of the solid model. First, the total volume to be removed by machining is obtained by subtracting the part from the stock. This volume is decomposed into minimum convex cells by half space partitioning at every concave edge. A method called maximum convex cell composition is developed to generate all alternative volume decompositions. The composing sub volumes are classified based on degree of freedom analysis. This paper focuses on the first part of our system, i.e., the volume decomposition. The other part of the work will be submitted for publication at a leter date.

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Feature Recognition Using Combined Convex and Maximal Volume Decompositions

Volume 3: 25th Computers and Information in Engineering Conference, Parts A and B ◽

10.1115/detc2005-85522 ◽

2005 ◽

Cited By ~ 1

Author(s):

Eric Wang ◽

Yong Se Kim ◽

Yoonhwan Woo

Keyword(s):

Process Planning ◽

Feature Recognition ◽

Recognition System ◽

Feature Representation ◽

Planning System ◽

Feature Interaction ◽

Generation Process ◽

Maximal Volume ◽

Volume Decomposition ◽

Product Manufacturing

Next generation process planning systems should be capable of dealing with industrial demands of versatility, flexibility, and agility for product manufacturing. Development of process planning system is heavily dependent on feature recognition, but presently there is no satisfactory feature recognition system relying on a single method. In this paper, we describe a hybrid feature recognition method for machining features that combines three feature recognition technologies: graph-based, convex volume decomposition, and maximal volume decomposition. Based on an evaluation of the strengths and weaknesses of these methods, we integrate them in a sequential workflow, such that each method recognizes features according to its strengths, and successively simplifies the part model for the following methods. We identify two anomalous cases arising from the application of maximal volume decomposition, and discuss their cure by introducing limiting halfspaces. All recognized features are combined into a unified hierarchical feature representation, which captures feature interaction information, including geometry-based machining precedence relations.

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