• Transactions of the Korean Society of Machine Tool Engineers
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• v.12 no.5
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• pp.15-23
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• 2003

A feature-based inspection planning system is proposed in this research to develop more efficient measuring methodology for the OMM(On-Machine Measurement) or CMM(coordinate Measuring Machine) for complicated workpiece having many primitive form features. This paper is proposed solution that optimum inspection sequence of the objective features. The sequences are determined by analyzing the feature information such as the nearest relationship and the possible probe-approach direction(PAD) of the features, and forming feature groups. A series of heuristic rules are developed to accomplish it. Also, each feature is decomposed into its constituent geometric elements for inspection process, and then the number of sampling points, location of the measuring points, optimum probing path are determined.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2002.10a
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• pp.353-358
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• 2002

A feature-based inspection planning system is proposed in this research to develop more efficient measuring methodology for the OMM (On-machine measurement) for complicated workpiece having many primitive form features. This paper focuses on the development of the CAIP (computer-aided inspection system) methodologies. The optimum inspection sequences for the features are determined by analyzing the feature information such as the nested relations and the possible probe approaching directions of the features, and forming feature groups. A series of heuristic rules are developed to accomplish it. Also, each feature is decomposed into its constituent geometric elements, and then the number of sampling points, the locations of the measuring point, the optimum probing path are determined by applying the fuzzy logic, Hammersley's method, and the TSP algorithm. To verify the proposed methodologies, simulations are carried out and the results are analyzed.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.16 no.6
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• pp.62-70
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• 2007

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.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.17 no.4
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• pp.140-145
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• 2008

Recently more complex geometric shapes, including freeform surfaces, are adopted for the design of products to emphasize style or beauty. Modeling of these products is extremely difficult or often impossible. Reverse engineering is the latest technology that can solve the problem by generating CAD models from the physical mockups or prototype models. Reverse engineering uses the coordinate measuring machine(CMM) to get the shape data of products. CMM is limited by the size of the product; therefore it must need the feature to solve it. The tooling-ball which is generally used for feature has difficulty in being used for soft products. Besides, the higher the accuracy of the tooling-ball is, the more expensive its cost is. This study will develop the feature of high accuracy without additional tools and compare the difference of accuracy by it.

• Journal of Korean Institute of Industrial Engineers
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• v.40 no.3
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• pp.257-266
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• 2014

The semiconductor manufacturing industry is managed by a number of parameters from the FAB which is the initial step of production to package test which is the final step of production. Various methods for prediction for the quality and yield are required to reduce the production costs caused by a complicated manufacturing process. In order to increase the accuracy of quality prediction, we have to extract the significant features from the large amount of data. In this study, we propose the method for extracting feature from the cell level data of probe test process using OPTICS which is one of the density-based clustering to improve the prediction accuracy of the quality of the assembled chips that will be placed in a package test. Two features extracted by using OPTICS are used as input variables of quality prediction model because of having position information of the cell defect. The package test progress for chips classified to the correct quality grade by performing the improved prediction method is expected to bring the effect of reducing production costs.

• Journal of Korean Institute of Industrial Engineers
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• v.21 no.3
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• pp.441-461
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• 1995

This research proposes an Integrated Product Information Model (IPIM) using STEP (Standard for the Exchange of product model data) for Computer Integrated Manufacturing (CIM) of Concurrent Engineering (CE). IPIM is based on Geometry and Topology (STEP Part 42), Form Feature (STEP Part 48), and Tolerance (STEP Part 48) for representing the integrated information of mechanical parts. For the IPIM, 1) new entities are developed for integration of existing entities, and 2) the existing entities are restructured and modified for a special application protocol. In CIM or CE, the advantages of using IPIM having integrated form of geometry, feature and tolerance are 1) integration of product design, process design and manufacturing sequentially or concurrently. 2) keep the product data consistency, modified by different domain, and 3) automatic data exchange between different application software and different hardware. The prototype system is composed of CAD, Data Probe, DBMS and SDAI (Standard Data Access Interface), and the generated STEP data is stored in a step file of DBMS for other applications.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.9 no.2
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• pp.45-51
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• 2000

This study proposes that analysis and evaluation method of time series ultrasonic signal using the chaotic feature extrac-tion for degradation extent. Features extracted from time series data using the chaotic time series signal analyze quantitatively material degradation extent. For this purpose analysis objective in this study if fractal dimension lyapunov exponent and strange attractor on hyperspace. The lyapunov exponent is a measure of the rate at which nearby trajectories in phase space diverge. Chaotic trajectories have at least one positive lyapunov exponent. The fractal dimension appears as a metric space such as the phase space trajectory of a dynamical syste, In experiment fractal(correlation) dimensions and lyapunov experiments showed values of mean 3.837-4.211 and 0.054-0.078 in case of degradation material The proposed chaotic feature extraction in this study can enhances ultrasonic pattern recognition results from degrada-tion signals.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.9 no.2
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• pp.144-150
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• 2000

Surface roughness is one of the most important parameters to estimate quality of products. As this reason so many studies were car-ried out through various attempts that were contact or non-contact using computer vision. Even through these efforts there were few good results in this research., however texture analysis making a important role to solve these problems in various fields including universe aviation living thing and fibers. In this study feature value of co-occurrence matrix was calculated by statistic method and roughness value of worked surface was classified, of it. Experiment was carried out using input vector of neural network with characteristic value of texture calculated from worked surface image. It's found that recognition rate of 74% was obtained when adapting texture features. In order to enhance recogni-tion rate combination type in characteristics value of texture was changed into input vector. As a result high recognition rate of 92.6% was obtained through these processes.

• Journal of Korean Institute of Industrial Engineers
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• v.19 no.2
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• pp.119-132
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• 1993

This paper deals with the development of an expert process design system interfaced with CAD for porismatic parts in injection mold manufacture. The developed CAD/CAPP system consists of two modules such as CAD interface module and process design module. Parts are represented using AutoCAD system on the IBM PC/AT. CAD interface module recognizes form features and manufacturing features of the part using form feature recognition algorithm and manufacturing feature recognition rule base. Process design module selects operations and determines machine tools, cutting tools and operation sequencing by using knowledge base which is acquired from expert process planners. A case study is implemented to evaluate feasibilities of the function of the proposed system. The CAD/CAPP system can improve the efficiency of process design activities and reduce the time required for process design.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.04a
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• pp.410-415
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• 1997

Recently, The manufacturing companies are introducing the CAD/CAM systems to solve problems for the lack of experts, the higher cost of manufacturing and the difficulties of process. Knowledge engineering approach makes it possible to change a know-how of experts to computerized information effectivly. The proposal of this paper is the development of an interactive knowledge-based CAM system to disign and manufacture the mold with non-expert engineers used easily. This system is composed of two functional parts. One is the geometric modeler that used the technique of a feature modeling. The other is the expert system module that composed inference engine and databas which contains characteristics of materials and cutting tools setc.