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

2018 ◽  
Vol 34 (4) ◽  
pp. 843-863 ◽  
Author(s):  
Ahmad Faiz Zubair ◽  
Mohd Salman Abu Mansor
Keyword(s):  
Decomposition Method ◽  
Feature Recognition ◽  
Volume Decomposition ◽  
Automatic Feature Recognition

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.

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.

Using Automatic Feature Recognition to Interface CAD to CAPP

1992 ◽  
Author(s):  
Eric Wang
Keyword(s):  
Pattern Matching ◽  
Feature Recognition ◽  
Scale Up ◽  
Fundamental Difference ◽  
Computer Integrated Manufacturing ◽  
Semantic Gap ◽  
Recognition Method ◽  
Computer Aided Process Planning ◽  
Computer Aided ◽  
Automatic Feature Recognition

Abstract Interfacing CAD to CAPP (computer-aided process planning) is crucial to the eventual success of a fully-automated computer-integrated manufacturing (CIM) environment. Current CAD and CAPP systems are separated by a “semantic gap” that represents a fundamental difference in the ways in which they represent information. This semantic gap makes the interfacing of CAD to CAPP a non-trivial task. This paper argues that automatic feature recognition is an indispensable technique in interfacing CAD to CAPP. It then surveys the current literature on automatic feature recognition methods and systems, and analyzes their suitability as CAD/CAPP interfaces. It also describes a relatively recent automatic feature recognition method based on volumetric decomposition, using Kim’s alternating sum of volumes with partitioning (ASVP) algorithm. The paper’s main theses are: (1) that most previous automatic feature recognition approaches are ultimately based on pattern-matching; (2) that pattern-matching approaches are unlikely to scale up to the real world; and (3) that volumetric decomposition is an alternative to pattern-matching that avoids its shortcomings. The paper concludes that automatic feature recognition by volumetric decomposition is a promising approach to the interfacing of CAD to CAPP.

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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