A Hybrid Approach to Feature Recognition Using Approximate and Partial Exact CAD Models

2002 ◽  
Author(s):  
Nagesh Belludi ◽  
Derek Yip-Hoi
Keyword(s):  
Feature Recognition ◽  
Hybrid Approach ◽  
Parameter Extraction ◽  
Feature Identification ◽  
Cad System ◽  
Processing Times ◽  
File Formats ◽  
Cad Models ◽  
Manufacturing Applications ◽  
Simple Surface

Several CAD system independent feature recognition techniques have been developed to drive manufacturing applications. Commercial implementations of these techniques require translating CAD models using STEP or other neutral file formats. With large CAD models found in some application domains; e.g., powertrain machining, corresponding STEP files are also large. This leads to large processing times. Another approach is to use lightweight formats such as STL or VRML. Here, complete & accurate parameter extraction is difficult because these formats approximate surfaces as tessellations. This paper discusses a new methodology for feature recognition, in which a VRML file is used for feature identification. To some extent, parameters of faces with simple surface-types are recovered from the tessellated model. If identified features consist of faces whose parameters are not recovered from the tessellated model, a partial STEP file translation is used for extracting exact parameters. This CAD system independent algorithmic development and implementation reduces the amount of data exported to neutral files, thus leading to more efficient feature recognition.

Geometric Reasoning for the Coding of CAD Models

1995 ◽  
Author(s):  
A. Z. Qamhiyah ◽  
B. Benhabib ◽  
R. D. Venter
Keyword(s):  
Computer Aided Design ◽  
Feature Recognition ◽  
Effective Means ◽  
Boundary Representation ◽  
Cad Model ◽  
Cad System ◽  
Cad Models ◽  
Form Features ◽  
Feature Based Design ◽  
Design Retrieval

Abstract Many of today’s concurrent product-development cycles depend on the utilization of intelligent Computer-Aided Design (CAD) systems. Thus, it would be essential to provide CAD users with effective means for interacting with the CAD system and its database. This paper addresses the development of a boundary-based coding procedure for CAD models. Coding the geometric and processing characteristics of objects, based on their CAD model representation, has been long recognized as an effective approach that allows convenient design retrieval on the one hand and process-planning automation on the other. Our work is based on the assumption that form features are recognizable and extractable from the CAD model by current feature-recognition, feature extraction and feature-based-design approaches. The coding procedure is applicable to the boundary representation of the object and its extracted form features.

Feature Recognition

2009 ◽  
pp. 90-108
Author(s):  
Xun Xu
Keyword(s):  
Feature Recognition ◽  
Geometric Model ◽  
Feature Model ◽  
Design System ◽  
Cad System ◽  
Domain Specific ◽  
Cad Models ◽  
Feature Based ◽  
High Level ◽  
Feature Based Design

Conventional CAD models only provide pure geometry and topology for mechanical designs such as vertices, edges, faces, simple primitives, and the relationship among them. Feature recognition is then required to interpret this low-level part information into high-level and domain-specific features such as machining features. Over the years, CAD has been undergoing fundamental changes toward the direction of feature-based design or design by features. Commercial implementations of FBD technique became available in the late 1980’s. One of the main benefits of adopting feature- based approach is the fact that features can convey and encapsulate designers’ intents in a natural way. In other words, the initial design can be synthesized quickly from the high-level entities and their relations, which a conventional CAD modeller is incapable of doing. However, such a feature-based design system, though capable of generating feature models as its end result, lacks the necessary link to a CAPP system, simply because the design features do not always carry the manufacturing information which is essential for process planning activities. This type of domain-dependent nature has been elaborated on in the previous chapter. In essence, feature recognition has become the first task of a CAPP system. It serves as an automatic and intelligent interpreter to link CAD with CAM, regardless of the CAD output being a pure geometric model or a feature model from a FBD system. To be specific, the goal of feature recognition systems is to bridge the gap between a CAD database and a CAPP system by automatically recognizing features of a part from the data stored in the CAD system, and based on the recognized features, to drive the CAPP system which produces process plans for manufacturing the part. Human interpretation of translating CAD data into technological information required by a CAPP system is thus minimized if not eliminated.

scholarly journals Practical method for the fast generation of a CAM model for jet engine parts

2021 ◽  
Vol 13 (3) ◽  
pp. 168781402110027
Author(s):  
Byung Chul Kim ◽  
Ilhwan Song ◽  
Duhwan Mun
Keyword(s):  
Computer Aided Design ◽  
Feature Recognition ◽  
Practical Method ◽  
Numerical Control ◽  
Cnc Machine Tools ◽  
Cnc Machine ◽  
Jet Engine ◽  
Computer Aided ◽  
Cad Models ◽  
Cam Model

Manufacturers of machine parts operate computerized numerical control (CNC) machine tools to produce parts precisely and accurately. They build computer-aided manufacturing (CAM) models using CAM software to generate code to control these machines from computer-aided design (CAD) models. However, creating a CAM model from CAD models is time-consuming, and is prone to errors because machining operations and their sequences are defined manually. To generate CAM models automatically, feature recognition methods have been studied for a long time. However, since the recognition range is limited, it is challenging to apply the feature recognition methods to parts having a complicated shape such as jet engine parts. Alternatively, this study proposes a practical method for the fast generation of a CAM model from CAD models using shape search. In the proposed method, when an operator selects one machining operation as a source machining operation, shapes having the same machining features are searched in the part, and the source machining operation is copied to the locations of the searched shapes. This is a semi-automatic method, but it can generate CAM models quickly and accurately when there are many identical shapes to be machined. In this study, we demonstrate the usefulness of the proposed method through experiments on an engine block and a jet engine compressor case.

Graph Representation of 3D CAD Models for Machining Feature Recognition With Deep Learning

2021 ◽  
Author(s):  
Weijuan Cao ◽  
Trevor Robinson ◽  
Hua Yang ◽  
Flavien Boussuge ◽  
Andrew Colligan ◽  
...  
Keyword(s):  
Deep Learning ◽  
Feature Recognition ◽  
Graph Representation ◽  
3D Cad ◽  
Machining Feature ◽  
Cad Models

Knowledge acquisition techniques for feature recognition in CAD models

2007 ◽  
Vol 19 (1) ◽  
pp. 21-32 ◽  
Author(s):  
Emmanuel Brousseau ◽  
Stefan Dimov ◽  
Rossitza Setchi
Keyword(s):  
Knowledge Acquisition ◽  
Feature Recognition ◽  
Cad Models

Heuristics Based Feature Recognition: A Graph Approach

1994 ◽  
Author(s):  
Shyam V. Narayan ◽  
Zhi-Kui Ling
Keyword(s):  
Engineering Design ◽  
Surface Finish ◽  
Feature Recognition ◽  
Graph Representation ◽  
Heuristic Rules ◽  
Feature Identification ◽  
Design And Manufacturing ◽  
Feature Based ◽  
Manufacturing Features

Abstract Feature based modeling has been used as a means to bridge the gap between engineering design and manufacturing. Features can represent an artifact with higher level entities which relate directly to its design functionalities and manufacturing characteristics, such as surface finish, manufacturability, fits, tolerance etc. In this study, a heuristic based feature recognition approach is proposed by using the graph representation of a design. The process consists of two steps: subgraph construction, and subgraph to feature identification. In this study, the subgraph construction is accomplished by using a set of heuristic rules. The process of subgraph to feature identification is carried out with a set of integers and characters which represent the geometric, topological, and semantic characteristics of the corresponding feature. This feature recognition scheme is used for the identification of machine features in a design.

A Generalized Method for the Classification and Extraction of Form Features

1996 ◽  
Author(s):  
A. Z. Qamhiyah ◽  
R. D. Venter ◽  
B. Benhabib
Keyword(s):  
Feature Extraction ◽  
Sequential Extraction ◽  
Feature Recognition ◽  
Boundary Elements ◽  
Geometric Reasoning ◽  
Extraction Techniques ◽  
Planar Surfaces ◽  
Cad Models ◽  
Extraction Stage ◽  
Form Features

Abstract Feature-extraction techniques address the primary limitation of feature-recognition approaches, namely their lack of generalization. This paper presents a boundary-based procedure for the classification and sequential extraction of form features from the CAD models of objects with planar surfaces. Form features are first classified based on their effect on the boundary elements of a basic shape. Geometric reasoning is then used to obtain generalized properties of the form-features’ classes. Finally, form-features’ classes are sequentially extracted based on the recognized properties. At the onset of each extraction stage, the object is viewed as an initial basic shape that has been iteratively altered through the introduction of form features.

A Hybrid Approach for 3D Lung Segmentation in CT Images Using Active Contour and Morphological Operation

2020 ◽  
pp. 163-175
Author(s):  
Satya Praksh Sahu ◽  
Bhawna Kamble
Keyword(s):  
Active Contour ◽  
Hybrid Approach ◽  
Similarity Index ◽  
Ct Images ◽  
Dice Similarity Coefficient ◽  
Morphological Operations ◽  
Lung Segmentation ◽  
Cad System ◽  
Detection And Diagnosis ◽  
Index Value

Lung segmentation is the initial step for detection and diagnosis for lung-related abnormalities and disease. In CAD system for lung cancer, this step traces the boundary for the pulmonary region from thorax in CT images. It decreases the overhead for a further step in CAD system by reducing the space for finding the ROIs. The major issue and challenging task for the segmentation is the inclusion of juxtapleural nodules in the segmented lungs. The chapter attempts 3D lung segmentation of CT images using active contour and morphological operations. The major steps in the proposed approach contain: preprocessing through various techniques, Otsu's thresholding for the binarizing the image; morphological operations are applied for elimination of undesired region and, finally, active contour for the segmentation of the lungs in 3D. For experiment, 10 subjects are taken from the public dataset of LIDC-IDRI. The proposed method achieved accuracies 0.979 Jaccard's similarity index value, 0.989 Dice similarity coefficient, and 0.073 volume overlap error when compared to ground truth.

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