Maximal Volume Decomposition and its Application to Feature Recognition

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.

Decomposing a Delta Volume Into Maximal Convex Volumes and Sequencing Them for Machining

1994 ◽  
Author(s):  
Hiroshi Sakurai
Keyword(s):  
Computer Program ◽  
Decomposition Method ◽  
Complex Shape ◽  
Raw Material ◽  
Small Computer ◽  
Machining Efficiency ◽  
Finished Part ◽  
Volume Difference

Abstract A method has been developed to decompose a polyhedral delta volume, which is the volume difference between the raw material and the finished part, into maximal convex volumes by intersecting the halfspaces of the faces of the delta volume. The hypothesis behind this effort is that in machining a delta volume of complex shape it is more efficient to divide it into volumes of simple shapes and remove volume by volume with large cutters than to remove it as a single volume with a single small cutter. The maximality of a maximal convex volume represents the possibility of using a large cutter and its convexity represents the simplicity of the shape of the volume. To prove the utility of maximal convex volumes, a small computer program was developed that sequences the maximal convex volumes based on a few heuristics on machining efficiency and tested it with a few objects. It generated good machining sequences. The basic idea of the decomposition method is to intersect a polyhedral delta volume with the halfspaces of its faces having concave edges. The combinations of such halfspaces that result in maximal convex volumes when they are intersected with the delta volume are determined efficiently by examining the relationships among the halfspaces. This basic idea works well for polyhedral delta volumes but does not work for delta volumes having curved faces since curved faces cannot always be extended infinitely. To cope with the delta volumes having cylindrical faces, a separate decomposition method has been developed. This method works only for the delta volumes that can be decomposed into 2½D machining volumes.

Feature Recognition by Volume Decomposition Using Half-Space Partitioning

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.

Feature Recognition Using Combined Convex and Maximal Volume Decompositions

2005 ◽  
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.

The Geometrical Feature Recognition Method of Part Drawing

2011 ◽  
Vol 415-417 ◽  
pp. 523-526
Author(s):  
Yan Dong ◽  
Mei Li
Keyword(s):  
Feature Recognition ◽  
Graph Matching ◽  
Geometric Feature ◽  
Recognition Method ◽  
Recognition Mechanism ◽  
Chemical Database ◽  
Geometry Feature ◽  
Constraint Structure ◽  
Geometric Elements ◽  
Fine Tune

This paper put forward a geometry feature recognition method of part drawing based on graph matching. Describe the constraints structure of geometric feature in geometric elements and those constraint relationships. Match sub-graph in contour closure graphics and those combination. Using linear symbol notation of chemical compounds in chemical database for reference, encode to constraint structure of geometry graphics, establish recognition mechanism of geometric characteristics by structure codes. Taking the fine-tune screw and fork parts for example, this method has been proved to be effective.

Volume decomposition and feature recognition: part 1—polyhedral objects

1995 ◽  
Vol 27 (11) ◽  
pp. 833-843 ◽  
Author(s):  
Hiroshi Sakurai
Keyword(s):  
Feature Recognition ◽  
Polyhedral Objects ◽  
Volume Decomposition

Volume decomposition and feature recognition, part II: curved objects

1996 ◽  
Vol 28 (6-7) ◽  
pp. 519-537 ◽  
Author(s):  
Hiroshi Sakurai ◽  
Parag Dave
Keyword(s):  
Feature Recognition ◽  
Volume Decomposition ◽  
Curved Objects

Form Feature Recognition Using Base Volume Decomposition

1994 ◽  
Author(s):  
James K. Coles ◽  
Richard H. Crawford ◽  
Kristin L. Wood
Keyword(s):  
Real World ◽  
Feature Recognition ◽  
Recognition Method ◽  
Mechanical Parts ◽  
Feature Representations ◽  
Volume Decomposition ◽  
Boundary Representations ◽  
New Feature ◽  
Form Feature

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.

Destructive Modeling by Volume Decomposition and Its Applications

2003 ◽  
Author(s):  
Yoonhwan Woo ◽  
Sang Hun Lee
Keyword(s):  
Decomposition Method ◽  
Solid Model ◽  
Solid Models ◽  
Volume Decomposition ◽  
Number Of Cells ◽  
Natural Way

Adding simple volumes, which are often called primitives, is a natural way to construct complex solid models. Conversely, cell-based volume decomposition is the existing method to decompose a complex solid model into simpler volumes that are often the primitives used to create the model. One problem of this volume decomposition is that it generates a large number of cells, many of which are unnecessary for the decomposition. In this paper, a volume decomposition method that improves the performance by avoiding generating the unnecessary cells is presented. Some possible applications are also presented to attest the usefulness of this volume decomposition method.

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