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.

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.

Wavelet-Based Surface Analysis for Form Feature Extraction

2000 ◽  
Author(s):  
Lei Sun ◽  
Abir Qamhiyah
Keyword(s):  
Feature Extraction ◽  
Wavelet Transforms ◽  
Three Dimensional ◽  
Feature Representation ◽  
Cad Model ◽  
Vanishing Moments ◽  
Planar Surfaces ◽  
Solid Models ◽  
Form Features ◽  
Form Feature

Abstract A new procedure for extracting form features from solid models with non-planar surfaces is presented in this paper. In the procedure, a surface is selected as the unit for feature representation, i.e. “feature primitive.” Three-dimensional wavelet transforms are applied to code and classify surfaces in a CAD model. Form features are then extracted by clustering the coded surfaces. Two wavelet bases, Harr and Daubechies with different vanishing moments, have been implemented. An example is presented to demonstrate the proposed procedure.

Feature Extraction Techniques by Optimal Volume Decomposition

1996 ◽  
Author(s):  
Jian Dong ◽  
Sreedharan Vijayan
Keyword(s):  
Feature Extraction ◽  
Process Planning ◽  
Feature Recognition ◽  
Optimization Methods ◽  
Manufacturing Cost ◽  
Extraction Techniques ◽  
Manufacturing Planning ◽  
Cad Cam ◽  
Feature Based ◽  
Manufacturing Feature

Abstract The elements of Computer-Aided Manufacturing, do not make full use of the part description stored in a CAD model because it exists in terms of low-level faces, edges and vertices or primitive volumes related to the manufacturing planning task. Consequently manufacturing planning still depends upon human expertise and input to interpret the part definition according to manufacturing needs. Feature-based technology is becoming an important tool to resolve this and other related problems. One approach is to design the part using Features directly. Another approach is Manufacturing Feature Extraction and Recognition. Manufacturing Feature Extraction consists of searching for the part description, recognizing cavity features, extracting those features as solid volumes of material to be removed. Feature Recognition involves raising this information to the level of part features which can be read by a process planning program. The feature extraction can be called optimal if the manufacturing cost of the component using those features can be minimized. An optimized feature extraction technique using two powerful optimization methods viz., Simulated Annealing and Genetic Algorithm is presented in this paper. This work has relevance in the areas of CAD/CAM linking, process planning and manufacturability assessment.

A Discourse on Geometric Feature Recognition From CAD Models

2000 ◽  
Vol 1 (1) ◽  
pp. 41-51 ◽  
Author(s):  
Jami J. Shah ◽  
David Anderson ◽  
Yong Se Kim ◽  
Sanjay Joshi
Keyword(s):  
Feature Recognition ◽  
Free Form ◽  
Geometric Feature ◽  
Surface Geometry ◽  
The Past ◽  
Feature Interactions ◽  
Cad Models ◽  
Form Features ◽  
Matching Criteria ◽  
Domain Dependence

This paper discusses the past 25 years of research in feature recognition. Although a great variety of feature recognition techniques have been developed, the discussion here focuses on the more successful ones. These include graph based and “hint” based methods, convex hull decomposition, and volume decomposition-recomposition techniques. Recent advances in recognizing features with free form features are also presented. In order to benchmark these methods, a frame of reference is created based on topological generality, feature interactions handled, surface geometry supported, pattern matching criteria used, and computational complexity. This framework is used to compare each of the recognition techniques. Problems related to domain dependence and multiple interpretations are also addressed. Finally, some current research challenges are discussed.

scholarly journals A Survey on Classification algorithms of Brain Images in Alzheimer’s disease based on Feature Extraction techniques

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Ruhul Amin Hazarika ◽  
Arnab Kumar Maji ◽  
Samarendra Nath Sur ◽  
Babu Sena Paul ◽  
Debdatta Kandar
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Feature Extraction ◽  
Classification Algorithms ◽  
Extraction Techniques ◽  
Brain Images

scholarly journals Instrumental Odour Monitoring System Classification Performance Optimization by Analysis of Different Pattern-Recognition and Feature Extraction Techniques

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 114
Author(s):  
Tiziano Zarra ◽  
Mark Gino K. Galang ◽  
Florencio C. Ballesteros ◽  
Vincenzo Belgiorno ◽  
Vincenzo Naddeo
Keyword(s):  
Pattern Recognition ◽  
Feature Extraction ◽  
Performance Optimization ◽  
Industrial Plant ◽  
Mathematical Treatment ◽  
Extraction Techniques ◽  
Peak Period ◽  
Linear Discriminant ◽  
Artificial Neural

Instrumental odour monitoring systems (IOMS) are intelligent electronic sensing tools for which the primary application is the generation of odour metrics that are indicators of odour as perceived by human observers. The quality of the odour sensor signal, the mathematical treatment of the acquired data, and the validation of the correlation of the odour metric are key topics to control in order to ensure a robust and reliable measurement. The research presents and discusses the use of different pattern recognition and feature extraction techniques in the elaboration and effectiveness of the odour classification monitoring model (OCMM). The effect of the rise, intermediate, and peak period from the original response curve, in collaboration with Linear Discriminant Analysis (LDA) and Artificial Neural Networks (ANN) as a pattern recognition algorithm, were investigated. Laboratory analyses were performed with real odour samples collected in a complex industrial plant, using an advanced smart IOMS. The results demonstrate the influence of the choice of method on the quality of the OCMM produced. The peak period in combination with the Artificial Neural Network (ANN) highlighted the best combination on the basis of high classification rates. The paper provides information to develop a solution to optimize the performance of IOMS.

scholarly journals HRCNet: High-Resolution Context Extraction Network for Semantic Segmentation of Remote Sensing Images

2020 ◽  
Vol 13 (1) ◽  
pp. 71
Author(s):  
Zhiyong Xu ◽  
Weicun Zhang ◽  
Tianxiang Zhang ◽  
Jiangyun Li
Keyword(s):  
Remote Sensing ◽  
Feature Extraction ◽  
High Resolution ◽  
Spatial Information ◽  
Semantic Segmentation ◽  
Context Information ◽  
Remote Sensing Images ◽  
Global Context ◽  
Boundary Information ◽  
Extraction Stage

Semantic segmentation is a significant method in remote sensing image (RSIs) processing and has been widely used in various applications. Conventional convolutional neural network (CNN)-based semantic segmentation methods are likely to lose the spatial information in the feature extraction stage and usually pay little attention to global context information. Moreover, the imbalance of category scale and uncertain boundary information meanwhile exists in RSIs, which also brings a challenging problem to the semantic segmentation task. To overcome these problems, a high-resolution context extraction network (HRCNet) based on a high-resolution network (HRNet) is proposed in this paper. In this approach, the HRNet structure is adopted to keep the spatial information. Moreover, the light-weight dual attention (LDA) module is designed to obtain global context information in the feature extraction stage and the feature enhancement feature pyramid (FEFP) structure is promoted and employed to fuse the contextual information of different scales. In addition, to achieve the boundary information, we design the boundary aware (BA) module combined with the boundary aware loss (BAloss) function. The experimental results evaluated on Potsdam and Vaihingen datasets show that the proposed approach can significantly improve the boundary and segmentation performance up to 92.0% and 92.3% on overall accuracy scores, respectively. As a consequence, it is envisaged that the proposed HRCNet model will be an advantage in remote sensing images segmentation.

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.

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