Form feature recognition using convex decomposition: results presented at the 1997 ASME CIE Feature Panel Session

1998 ◽  
Vol 30 (13) ◽  
pp. 983-989 ◽  
Author(s):  
Eric Wang ◽  
Yong Se Kim
Author(s):  
Sreekumar Menon ◽  
Yong Se Kim

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.


Author(s):  
Yong Se Kim ◽  
Kenneth D. Roe

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.


Author(s):  
Eric Wang ◽  
Yong Se Kim

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.


Author(s):  
Yong Se Kim

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.


Author(s):  
James K. Coles ◽  
Richard H. Crawford ◽  
Kristin L. Wood

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.


Author(s):  
Douglas L. Waco ◽  
Yong Se Kim

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.


Author(s):  
Yong Se Kim ◽  
Yong Hee Jung ◽  
Byung Gu Kang ◽  
Hyung Min Rho

Mechanical parts are often grouped into part families based on the similarity of their shapes, to support efficient manufacturing process planning and design modification. This paper presents a similarity assessment technique to support part family classification for machined parts. It exploits the multiple feature decompositions obtained by the feature recognition method using convex decomposition. Convex decomposition provides a hierarchical volumetric representation of a part, organized in an outside-in hierarchy. It provides local accessibility directions, which supports abstract and qualitative similarity assessment. It is converted to a Form Feature Decomposition (FFD), which represents a part using form features intrinsic to the shape of the part. This supports abstract and qualitative similarity assessment using positive feature volumes. FFD is converted to Negative Feature Decomposition (NFD), which represents a part as a base component and negative machining features. This supports a detailed, quantitative similarity assessment technique that measures the similarity between machined parts and associated machining processes implied by two parts’ NFDs. Features of the NFD are organized into branch groups to capture the NFD hierarchy and feature interrelations. Branch groups of two parts’ NFDs are matched to obtain pairs, and then features within each pair of branch groups are compared, exploiting feature type, size, machining direction, and other information relevant to machining processes.


1994 ◽  
Vol 26 (4) ◽  
pp. 689-707 ◽  
Author(s):  
Chia-Hwa Liu ◽  
Der-Baau Perng ◽  
Zen Chen

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