Research on Feature Extraction Curves and Surfaces in Reverse Engineering

An artificial neural network based feature extraction system for finding three dimensional features from physical objects is presented. As part of a geometric reverse engineering system, the feed-forward neural network allows for the efficient implementation of feature recognition. Reverse engineering of mechanical parts is the process of obtaining a geometric CAD model from the measurements of an existing artefact. Ideally, the reverse engineering system would automatically segment the cloud data into constituent surface patches and produce an accurate solid model. In order to accomplish this intent, a neural network is used to search and find the features in the initial scan data set. In this work, feature extraction for geometric reverse engineering has been accomplished. Work has also been done to extract features from the multiple shapes. The technique developed will reduce the time and effort required to extract features from scanned data of a physical object.

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Iterative Angular Feature Extraction (IAFE) Method for Reverse Engineering

The International Journal of Advanced Manufacturing Technology ◽

10.1007/s00170-002-1403-2 ◽

2003 ◽

Vol 21 (10-11) ◽

pp. 850-856

Author(s):

K. H. Lee ◽

W. Ning

Keyword(s):

Feature Extraction ◽

Reverse Engineering

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Feature Recognition in Geometric Reverse Engineering

10.32920/ryerson.14649579.v1 ◽

2021 ◽

Author(s):

Muhammad Arshad

Keyword(s):

Neural Network ◽

Feature Extraction ◽

Reverse Engineering ◽

Feature Recognition ◽

Three Dimensional ◽

Physical Object ◽

Engineering System ◽

Feed Forward Neural Network ◽

Data Set ◽

Cloud Data

An artificial neural network based feature extraction system for finding three dimensional features from physical objects is presented. As part of a geometric reverse engineering system, the feed-forward neural network allows for the efficient implementation of feature recognition. Reverse engineering of mechanical parts is the process of obtaining a geometric CAD model from the measurements of an existing artefact. Ideally, the reverse engineering system would automatically segment the cloud data into constituent surface patches and produce an accurate solid model. In order to accomplish this intent, a neural network is used to search and find the features in the initial scan data set. In this work, feature extraction for geometric reverse engineering has been accomplished. Work has also been done to extract features from the multiple shapes. The technique developed will reduce the time and effort required to extract features from scanned data of a physical object.

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Interactive Feature Modeling for Reverse Engineering

Journal of Computing and Information Science in Engineering ◽

10.1115/1.2364205 ◽

2006 ◽

Vol 6 (4) ◽

pp. 422-424 ◽

Cited By ~ 4

Author(s):

K. Schreve ◽

C. L. Goussard ◽

A. H. Basson ◽

D. Dimitrov

Keyword(s):

Feature Extraction ◽

Reverse Engineering ◽

Feature Modeling ◽

Free Form ◽

Modeling Tools ◽

Draft Angle ◽

Feature Based ◽

Nurbs Surfaces ◽

Point Data ◽

Interactive Feature

In feature based reverse engineering entities, or features, having higher level engineering meaning are used to approximate point data. This is in contrast to approximating the data with free form NURBS surfaces. Currently no such system is operationally available. Interactive feature based modeling tools for feature extraction, edge detection, and draft angle approximation are presented here. Several case studies demonstrate the application of these algorithms.

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Feature extraction from slice data for reverse engineering

Frontiers of Mechanical Engineering in China ◽

10.1007/s11465-007-0004-z ◽

2007 ◽

Vol 2 (1) ◽

pp. 25-31 ◽

Cited By ~ 1

Author(s):

Yingjie Zhang ◽

Shangning Lu

Keyword(s):

Feature Extraction ◽

Reverse Engineering

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An Improved Quantitative Image Analyser

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100078663 ◽

1977 ◽

Vol 35 ◽

pp. 226-227

Author(s):

J.P. Fallon ◽

P.J. Gregory ◽

C.J. Taylor

Keyword(s):

Feature Extraction ◽

Quantitative Analysis ◽

Frequency Content ◽

General Sense ◽

Physical Measurements ◽

Quantitative Image ◽

Image Analyser ◽

Automatic Methods ◽

Quantitative Results

Quantitative image analysis systems have been used for several years in research and quality control applications in various fields including metallurgy and medicine. The technique has been applied as an extension of subjective microscopy to problems requiring quantitative results and which are amenable to automatic methods of interpretation.Feature extraction. In the most general sense, a feature can be defined as a portion of the image which differs in some consistent way from the background. A feature may be characterized by the density difference between itself and the background, by an edge gradient, or by the spatial frequency content (texture) within its boundaries. The task of feature extraction includes recognition of features and encoding of the associated information for quantitative analysis.Quantitative Analysis. Quantitative analysis is the determination of one or more physical measurements of each feature. These measurements may be straightforward ones such as area, length, or perimeter, or more complex stereological measurements such as convex perimeter or Feret's diameter.

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