• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 1997년도 추계학술대회 논문집
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• pp.230-234
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• 1997

Reverse engineering involves digitizing a three-dimensional model or part converting the data to a CAD database description and manufacturing by CNC. Currently, the digitization is done through measurements taken manually by a CMM or touch probe mounted on a CNC machinetool. Some reverse engineering techniques require close integration between the data collection method and the surface-fitting algorithms. Accurate surface data are collected by input to the surface fitting method. This study has been found that both the smoothness of surfaces and accuracy of surface fitting are related with the degree of the interpolated surfaces.

• 한국공작기계학회논문집
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• 제12권2호
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• pp.79-85
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• 2003

An algorithm for fitting with Least Square is a traditional and an effective method in processing with experimental data. Due to the lack of definite representation, it is difficult to fit measured data with free curves or surfaces. B-Spline is usefully utilized to express free curves and surfaces with a few parameters. This paper presents the combination of these two techniques to process the point data measured from CMM and other similar instruments. This research shows tests and comparison of the simulation results from two techniques.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회/대한산업공학회 2005년도 춘계공동학술대회 발표논문
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• pp.960-968
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• 2005

The high cost in maintaining complex manufacturing process makes it necessary to enhance an efficient maintenance system. For the effective maintenance of manufacturing process, precise fault diagnosis should be performed and an appropriate maintenance action should be executed. This paper suggests an intelligent fault diagnosis system using hybrid data mining. In this system, the rules for the fault diagnosis are generated by hybrid decision tree/genetic algorithm and the most effective maintenance action is selected by decision network and AHP. To verify the proposed intelligent fault diagnosis system, we compared the accuracy of the hybrid decision tree/genetic algorithm with one of the general decision tree learning algorithm(C4.5) by data collected from a coil-spring manufacturing process.

• 품질경영학회지
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• 제41권4호
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• pp.601-610
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• 2013

Purpose: The purpose of this paper is to analyze the relationship between R&D investment and growth rate of manufacturing firms in Incheon. Methods: The balanced panel data of 246 firms which have existed for the period 2001-2012 are constructed. As a method of analysis, fixed effects panel data model is used. Results: There is a one year lag in the relationship between R&D intensity and the subsequent sales growth of firms and its relation depends on the firms' characteristics. Conclusion: We suggest the emphasis on R&D investment for firms' growth and the differentiated R&D program based on firm size. This article has the limitation that various types of R&D investment cannot be included in this analysis.

• Journal of Mechanical Science and Technology
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• 제14권6호
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• pp.675-689
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• 2000

Rapid design and production techniques are indispensable for the custom-made production systems. For manufacturing custom-made shoes, the shoelast should be designed rapidly from the individual foot model. In this paper, we develop an integrated system for rapid design and manufacturing of custom-tailored shoes. The foot shape measurement sub-system allows scanning a standard shoelast and an individual foot and then extracts the three-dimensional crosssectional data of the shoelast and the human foot shape from the captured image data. The shoelast design sub-system uses the scanned data to design new customized shoelast curves or surfaces with the heeling and mixing algorithms built in this system. The pattern design subsystem provides a method, which transforms a shoe-upper surface designed by a stylist into a flat-pattern that can be manufactured. We also export the surface model to an NC machine to manufacture the physical shoelast model.

• 한국생산제조학회지
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• 제23권2호
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• pp.118-126
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• 2014

In this paper, a new method is proposed for the five-degree-of-freedom precision alignment and stitching of three-dimensional surface-profile data sets. The control parameters for correcting thealignment error are calculated from the surface profile data for overlapped areas among the adjacent measuring areas by using the "least squares method" and "maximum lag position of cross correlation function." To ensure the alignment and stitching reliability, the relationships betweenthe alignment uncertainty, overlapped area, and signal-to-noise level of the measured profile data are investigated. Based on the results of this uncertainty analysis, an appropriate size is proposed for the overlapped area according to the specimen's surface texture and noise level.

• 대한안전경영과학회지
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• 제8권2호
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• pp.51-59
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• 2006

So far, there is no technique of quantitative evaluation on danger related to industrial accidents. Therefore, as an endeavor for obtaining technique of quantitative evaluation, this study presents feature analysis of industrial accidents in manufacturing field using QUEST algorithm. In order to analyze feature of industrial accidents, a retrospective analysis was performed in 10,536 subjects (10,313 injured people, 223 deaths). The sample for this work chosen from data related to manufacturing businesses during three years $(2002\sim2004)$ in Korea. The analysis results were very informative since those enable us to know the most important variables such as occurrence type, company size, and occurrence time which can affect injured people. Also, it is found that classification using QUEST algorithm which was performed in this study is very reliable.

• 한국생산제조학회지
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• 제25권4호
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• pp.271-280
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• 2016

Predicting cutting forces in machine tools is essential to productivity improvement and process control in the manufacturing field. Furthermore, milling machining is more complicated than turning machining. Therefore, several studies have been conducted previously to simulate milling forces; this study aims to simulate the cutting forces in milling machines using multi-layered neural networks. In the experiments, the number of layers in these networks was 3 and 4 and the number of neurons in the hidden layers was varied from 20 to 200. The root mean square errors of simulated cutting force components were obtained from taught and untaught data for the various neural networks. Results show that the error trends for untaught data were non-uniform because of the complex nature of the cutting force components, which was caused by different cutting factors and nonlinear characteristics coming into play. However, trends for taught data showed a very good coincidence.

• 산업경영시스템학회지
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• 제34권4호
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• pp.57-65
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• 2011

Monitoring auto correlated processes is prevalent in recent manufacturing environments. As a proactive control for manufacturing processes is emphasized especially in the semiconductor industry, it is natural to monitor real-time status of equipment through sensor rather than resultant output status of the processes. Equipment's sensor data show various forms of correlation features. Among them, considerable amount of sensor data, statistically autocorrelated, is well represented by Box-Jenkins autoregressive moving average (ARMA) model. In this paper, we present a design method of statistical process control (SPC) used for monitoring processes represented by the ARMA model. The proposed method shows benefits in the power of detecting process changes, and considers robustness to ARMA modeling errors simultaneously. We prove benefits through Monte carlo simulation-based investigations.

• 산업공학
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• 제11권2호
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• pp.119-124
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• 1998

This paper proposes a new part family classification system (IPFACS: Image Processing and Fuzzy ART based Clustering System), which incorporates image processing techniques and a modified fuzzy ART neural network algorithm. IPFACS can classify parts based on geometrical shape and manufacturing attributes, simultaneously. With a proper reduction and normalization of an image data through the image processing methods and adding method in the modified Fuzzy ART, different types of geometrical shape data and manufacturing attribute data can be simultaneously classified in the same system. IPFACS has been tested for an example set of hypothetical parts. The results show that IPFACS provides a good feasible approach to form families based on both geometrical shape and manufacturing attributes.