• Journal of the Korean Society for Precision Engineering
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• v.19 no.11
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• pp.103-111
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• 2002

In this study, the natural flaws in welding parts are classified using the signal pattern classification method. The storage digital oscilloscope including FFT function and enveloped waveform generator is used and the signal pattern recognition procedure is made up the digital signal processing, feature extraction, feature selection and classifier design. It is composed with and discussed using the distance classifier that is based on euclidean distance the empirical Bayesian classifier. feature extraction is performed using the class-mean scatter criteria. The signal pattern classification method is applied to the signal pattern recognition of natural flaws.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.828-831
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• 2005

In feature-based multi-resolution modeling, the features are rearranged according to a criterion for the levels of detail (LOD) of multi-resolution models. In this paper, two different LOD criteria are investigated and discussed. The one is the volumes of subtractive features, together with the precedence of additive features over subtractive features. The other is the volumes of features, regardless of whether the feature types are subtractive or additive. In addition, the algorithms to define and extract the LOD models based on the criteria are also described. The criterion of the volumes of features can be used for a wide range of applications in CAD and CAE in virtue of its generality.

• Journal of the Korean Society for Precision Engineering
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• v.17 no.6
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• pp.176-184
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• 2000

Reverse engineering (RE) is a process to create computer aided design (CAD) models from the scanned data of an existing part acquired using 3D position scanners. This paper proposes a novel methodology of extracting geometric features directly from a set of 3D scanned points, which utilizes the concepts of feature-based technology and artificial neural networks (ANNs). The use of ANN has enabled the development of a flexible feature-based RE application that can be trained to deal with various features. The following four main tasks were mainly investigated and implemented: (1) Data reduction; (2) edge detection; (3) ANN-based feature recognition; (4) feature extraction. This approach was validated with a variety of real industrial components. The test results show that the developed feature-based RE application proved to be suitable for reconstructing prismatic features such as block, pocket, step, slot, hole, and boss, which are very common and crucial in mechanical engineering products.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.2 no.2
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• pp.101-106
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• 2001

The integrated design system for injection molding has been studied. The current CAD system do not provide mold designers with necessary function for CAD/CAPP/CAE interface except the geometric modeling capability. This paper describes a feature-based CAD system for mold and moldbase design which enables the concurrent design and CIM, with integrated design procedure, at the initial design stage of injection molding A new design methodology and resulting feature data files for this design system are also discussed.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.9 no.1
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• pp.119-127
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• 2000

In this study the classified researches the artificial and natural flaws in welding parts are performed using the pattern recognition technology. For this purpose the signal pattern recognition package including the user defined function was developed and the total procedure including the digital signal processing feature extraction feature selection and classifi-er selection is teated by bulk,. Specially it is composed with and discussed using the statistical classifier such as the linear discriminant function the empirical Bayesian classifier. Also the pattern recognition technology is applied to classifica-tion problem of natural flaw(i.e multiple classification problem-crack lack of penetration lack of fusion porosity and slag inclusion the planar and volumetric flaw classification problem), According to this result it is possible to acquire the recognition rate of 83% above even through it is different a little according to domain extracting the feature and the classifier.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1994.04a
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• pp.198-207
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• 1994

공작기계부품 가공을 위한 공정표는 가공공정, 공정별 도면 분할, 가공기계 등을 결정하는 공정계획과 한 공정에 대하여 가공방법, 가공공구, 절삭조건, 공수등을 결정하는 작업계획을 통하여 발행된다. 작업계획에서 가공방법과 가공공구의 결정은 절삭조건과 공수에 영향을 주는 중요한 요소이다. 기존의 연구에서는 가공방법과 가공공구를 결정하기 위해 전문가 시스템 쉘(expert system shell)이용한 사례가 많았다. 이 경우, 지식 베이스(knowledge base) 의 구축에 많은 시간이 소요되고, 지식이 변했을 때 수정의 어려움이 있다. 본 연구에서는 표준화되지 않아 변경의 소지가 많은 가공방법과 가공공구 결정에 뉴럴 네트워크(neural network)의 한 종류인 백 프로퍼게이션 (back propagation) 학습 모델을 이용했다. 공정계획 후 분할된 공정별 도면으로부 터 크기 및 정밀도 등과 같은 특징형상(feature) 정보를 추출한 후, 특징형상 의 종류와 크기, 치수공차, 기하공차, 거칠기 등을 입력하여 가공방법 및 가 공공구가 출력되도록 학습패턴을 설정하여 학습시켰다. 학습패턴은 공정설계 전문가와 인터뷰하는 방법과 작업계획 과정을 분석하는 방법을 통하여 설정 했다. 백 프로퍼게이션 모델을 통하여 학습시킨 결과, 학습시킨대로 정확한 가공방법 및 가공공구를 결정할 수 있었다.

• Journal of Institute of Convergence Technology
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• v.4 no.2
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• pp.1-9
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• 2014

This paper examines feature-based reconstruction algorithm using feature-based modeling and based on topology optimization technology, which aims to achieve a minimal volume weight and to satisfy user-defined constraints such as stress, deformation related conditions. The finite element model after topology optimization allows us to remove some region of a solid model for predefined volume requirement. The stress or deformation distribution resulted from finite element analysis enables us to add some material to the solid model for a robust structure. For this purpose, we propose a feature-based redesign algorithm which inserts negative features to the solid model for material removal and positive features for material addition, and we introduce a bisection method which searches an optimal structure by iteratively applying the feature-based redesign algorithm. Several examples are considered to illustrate the proposed algorithms and to demonstrate the effectiveness of the present approach.

• Korean Journal of Computational Design and Engineering
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• v.4 no.2
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• pp.139-152
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• 1999

This paper presents a feature-based approach to extracting machining features fro a feature-based design model. In the approach, a design feature to machining feature conversion process incrementally converts each added design feature into a machining feature or a set of machining features. The proposed approach an efficiently handle protrusion features and interacting features since it takes advantage of design feature information, design intent, and functional requirements during feature extraction. Protrusion features cannot be directly mapped into machining features so that the removal volumes surrounding protrusion features are extracted and converted it no machining features. By utilizing feature information as well as geometry information during feature extraction, the proposed approach can easily overcome inherent problems relating to feature recognition such as feature interactions and loss of design intent. In addition, a feature extraction process can be simplified, and a large set of complex part can be handled with ease.

• Proceedings of the KSME Conference
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• 2003.04a
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• pp.304-309
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• 2003

In this study, natural flaws in welding parts are classified using the signal pattern classification method. The storage digital oscilloscope including FFT function and enveloped waveform generator is used and the signal pattern recognition procedure is made up the digital signal processing, feature extraction, feature selection and classifier design. It is composed with and discussed using the distance classifier that is based on euclidean distance the empirical Bayesian classifier. Feature extraction is performed using the class-mean scatter criteria. The signal pattern classification method is applied to the signal pattern recognition of natural flaws.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.04a
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• pp.426-431
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• 1997

Features are well recognized to play an important role for the integration of ACD and CAPP. Majority of pervious works for the feature recognition for prismatic part is based on 3D solid model. But in real factories, 2D drawing are used more than 3D drawings. In this paper, we develope an algorithm of the feature recognition on prismatic parts in 2D drawings, using by the graph method and the heuristic algorithm. Previous algorithms have some conflicts at feature interaction. In this paper, elements are grouped into connection by the graph method. Then features are recognized by using these grouped elements and their relationships of front and side-view. For resolving the problem of feature interaction, the element graphs are modified by an deloped algorithm. This algorithm is applied to a CAPP system for milling process planning.