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.

Handling Blending Features in Form Feature Recognition Using Convex Decomposition

1994 ◽  
Author(s):  
Sreekumar Menon ◽  
Yong Se Kim
Keyword(s):  
Feature Recognition ◽  
Boundary Representation ◽  
Recognition Method ◽  
Geometric Information ◽  
Polyhedral Model ◽  
Curved Boundary ◽  
Convex Decomposition ◽  
Geometric Objects ◽  
Form Features ◽  
Form Feature

Abstract Form features intrinsic to the product shape can be recognized using a convex decomposition called Alternating Sum of Volumes with Partitioning (ASVP). However, the domain of geometric objects to which ASVP decomposition can be applied had been limited to polyhedral solids due to the difficulty of convex hull construction for solids with curved boundary faces. We develop an approach to extend the geometric domain to solids having cylindrical and blending features. Blending surfaces are identified and removed from the boundary representation of the solid, and a polyhedral model of the unblended solid is generated while storing the cylindrical geometric information. From the ASVP decomposition of the polyhedral model, polyhedral form features are recognized. Form feature decomposition of the original solid is then obtained by reattaching the stored blending and cylindrical information to the form feature components of its polyhedral model. In this way, a larger domain of solids can be covered by the feature recognition method using ASVP decomposition. In this paper, handling of blending features in this approach is described.

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.

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.

A Feature Recognition Approach with Fuzzy ART Neural Network

2011 ◽  
Vol 279 ◽  
pp. 406-411
Author(s):  
Cong Lu ◽  
Jun Zha
Keyword(s):  
Neural Network ◽  
Feature Recognition ◽  
Boundary Representation ◽  
Solid Model ◽  
Fuzzy Art ◽  
Fast Learning ◽  
Key Technologies ◽  
Learning Modes ◽  
Slow Learning

This paper proposes a feature recognition approach from a boundary representation solid model with Fuzzy ART neural network. To recognize the feature efficiently, some key technologies in Fuzzy ART neural network are used. The influence of the vigilance parameter on feature recognition is studied, and two learning modes, fast learning and slow learning are adopted and compared in feature recognition. Finally, a case study is given to verify the proposed approach.

Decomposition Based Feature Recognition Using Constrained and Aggregated Half-Space Partitioning

2000 ◽  
Author(s):  
Yong Lu ◽  
Rajit Gadh ◽  
Timothy J. Tautges
Keyword(s):  
Half Space ◽  
Pattern Matching ◽  
Feature Recognition ◽  
Combinatorial Problem ◽  
Space Partitioning ◽  
Decomposition Approach ◽  
Decomposition Stage ◽  
Two Stages ◽  
Minimal Cells ◽  
Recognition Technique

Abstract Decomposition based feature recognition (DBFR) has drawn attention over the years. It has two stages: decomposition and aggregation. At the decomposition stage, the CAD model is partitioned into minimal cells. At the aggregation stage, the decomposed individual cells are composed in different combinations and these combinations are matched with predefined feature patterns to retrieve features in the model. The DBFR technique shows promises to deal with interactive features. However, DBFR algorithms suffer from the combinatorial problem in both the partitioning and the composing stages. This paper proposes a novel decomposition based feature recognition technique using the constrained and aggregated half-space partitioning. The constrained and aggregated half-space is defined in the occupation of a volume in the Euclidean space, bounded by multiple surfaces. The decomposition approach based on this concept can largely avoid over-cuttings. It tends to produce partitions that can be directly matched with feature patterns. Different from other DBFR algorithms, pattern matching is also introduced in the decomposition stage. Thus it further shrinks the space of combination and feature determination. Some algorithms are also proposed to do efficient volume combinations at the aggregation stage.

Augmented Convex Decomposition Using Incremental Update for Recognition of Form Features

1996 ◽  
Author(s):  
Frédéric Parienté ◽  
Yong Se Kim
Keyword(s):  
Feature Recognition ◽  
Feature Representation ◽  
Boundary Representation ◽  
Central Feature ◽  
Decomposition Tree ◽  
Active Components ◽  
Convex Decomposition ◽  
Volumetric Representation ◽  
Incremental Update ◽  
Form Features

Abstract Alternating Sum of Volumes with Partitioning (ASVP) decomposition is a volumetric representation of a part obtained from its boundary representation that organizes faces of the part in an outside-in hierarchy. ASVP decomposition combines Alternating Sum of Volumes (ASV) decomposition, using convex hulls and set difference operations, and remedial partitioning, using cutting operations and concave edges. A Form Feature Decomposition (FFD) which can serve as a central feature representation for various applications is obtained from ASVP decomposition. The incremental update of convex decomposition is achieved by exploiting its hierarchical structure. For a connected incremental design change, the active components that only need to be updated are localized in a subtree of the decomposition tree called active subtree. Then, the new decomposition is obtained by only updating the active subtree in the old decomposition. In this paper, we present a new decomposition, called Augmented Alternating Sum of Volumes with Partitioning (AASVP) decomposition, that is incrementally constructed using ASV incremental update as a local operation on a decomposition tree. AASVP provides an improved feature recognition capability as it localizes the effect of the change in the decomposition tree, avoids excessive remedial partitioning and catches the designer’s intent in feature editing. AASVP differs from ASVP at the remedial-partitioning nodes by partitioning less. The current remedial partitioning method could be improved such that AASVP decomposition can be constructed directly from the solid model.

Representation and Mapping of Geometric Dimensions From Design to Manufacturing

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.

Conversions in Form Feature Recognition Using Convex Decomposition

1992 ◽  
Author(s):  
Yong Se Kim ◽  
Kenneth D. Roe
Keyword(s):  
Decomposition Method ◽  
Feature Recognition ◽  
Inductive Learning ◽  
The Other ◽  
New Combination ◽  
Convex Decomposition ◽  
Feature Information ◽  
Form Features ◽  
Primal And Dual ◽  
Form Feature

Abstract A convergent convex decomposition method called Alternating Sum of Volumes with Partitioning (ASVP) has been used to recognize volumetric form features intrinsic to the product shape. The recognition process is done by converting the ASVP decomposition into a form feature decomposition by successively applying combination operations on ASVP components. In this paper, we describe a method to generate new combination operations through inductive learning from conversion processes of primal and dual ASVP decompositions when one decomposition produces more desirable form feature information than the other.

A Convergent Convex Decomposition of Polyhedral Objects

1992 ◽  
Vol 114 (3) ◽  
pp. 468-476 ◽  
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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