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.

Form Feature Recognition by Convex Decomposition

1991 ◽  
Author(s):  
Yong Se Kim
Keyword(s):  
Decomposition Method ◽  
Feature Recognition ◽  
Convex Hulls ◽  
Convex Decomposition ◽  
Geometric Objects ◽  
Volumetric Representation ◽  
Feature Information ◽  
Form Features ◽  
Form Feature

Abstract A convex decomposition method, called Alternating Sum of Volumes (ASV), uses convex hulls and set difference operations. ASV decomposition may not converge, which severely limits the domain of geometric objects that can be handled. By combining ASV decomposition and remedial partitioning for the non-convergence, we have proposed a convergent convex decomposition called Alternating Sum of Volumes with Partitioning (ASVP). In this article, we describe how ASVP decomposition is used for recognition of form features. ASVP decomposition can be viewed as a hierarchical volumetric representation of form features. Adjacency and interaction between form features are inherently represented in the decomposition in a hierarchical way. Several methods to enhance the feature information obtained by ASVP decomposition are also discussed.

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.

1997 Status of the Form Feature Recognition Method Using Convex Decomposition

1997 ◽  
Author(s):  
Eric Wang ◽  
Yong Se Kim
Keyword(s):  
Feature Recognition ◽  
Feature Representation ◽  
Boundary Representation ◽  
Current Status ◽  
Recognition Method ◽  
Convex Decomposition ◽  
Feature Representations ◽  
Negative Feature ◽  
Form Features ◽  
Form Feature

Abstract This paper describes the current status of our feature recognition method using a convex decomposition method called Alternating Sum of Volumes with Partitioning (ASVP). Volumetric form features are recognized from a part’s boundary representation by applying combination operations to the ASVP decomposition of the part to obtain a Form Feature Decomposition (FFD). The FFD can be post-processed to obtain application-specific feature representations; in particular, the conversion to the Negative Feature Decomposition (NFD), a machining feature representation, is described. Our domain of recognizable parts includes those having planar and cylindrical surfaces such that all cylindrical surfaces are bounded by straight edges and circular arcs, or are blending surfaces resulting from applying constant-radius blending to the part. We report the results of applying our method to the test parts for the 1997 Computers in Engineering Feature Panel Session.

Considerations in Positive to Negative Conversion for Machining Features Using Convex Decomposition

1993 ◽  
Author(s):  
Douglas L. Waco ◽  
Yong Se Kim
Keyword(s):  
Negative Component ◽  
Top Down ◽  
Machining Features ◽  
Convex Decomposition ◽  
Form Features ◽  
Common Parent ◽  
Parent Component ◽  
Form Feature ◽  
Negative Conversion

Abstract Form features intrinsic to the product shape can be recognized using a convex decomposition called Alternating Sum of Volumes with Partitioning (ASVP). Since the form feature decomposition is compact and faithful to the product shape, it includes both positive and negative components. For machining applications, the positive components are converted into corresponding negative components to represent the removal volume. The positive to negative conversion is done in top-down manner by abstracting the positive components using halfspaces determined by the original faces and combining with the parent negative component. In this paper, we describe the considerations in handling interacting sibling positive components which have a common parent component.

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.

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.

A Neutral Framework for Feature Definition and a Generic Algorithm for Feature Recognition

2014 ◽  
Vol 988 ◽  
pp. 530-539
Author(s):  
De Biao Zeng ◽  
Shi Ming Wan ◽  
Chun Ling Zeng ◽  
Guo Lei Zheng ◽  
Dong Ming Li
Keyword(s):  
Feature Recognition ◽  
Recognition Algorithm ◽  
Generic Algorithm ◽  
Wide Range ◽  
Form Features ◽  
Interacting Features ◽  
Form Feature

Traditional feature recognition approaches predefine features which can be recognized in systems. Feature definitions and recognitions are characterized by the domains which the approaches are applied to. Therefore, these approaches are not generic. In order to fulfill various feature recognition requirements of different users in different domains, this paper proposes an application independent framework for form feature definition and a generic algorithm for feature recognition based on the framework. Users in different domains can define their own features following the rules of the feature definition framework according to their tasks and user-defined features can be recognized by the uniform recognition algorithm. Hence, the system is flexible and can recognize a wide range of form features in different domains. The testing results show that this approach is effective in defining and recognizing isolated features as well as interacting features.

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.

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