한국정밀공학회:학술대회논문집 (Proceedings of the Korean Society of Precision Engineering Conference)
- 한국정밀공학회 2000년도 추계학술대회 논문집
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- Pages.299-302
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- 2000
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- 2005-8446(pISSN)
Neural Network을 이용한 최적 측정장비 결정 시스템 개발
Development of an optimal measuring device selection system using neural networks
초록
Various types of measuring devices are used for reverse engineering and inspection in different fields of industry such as automotive, aerospace, computer graphics, and home appliance. In order to measure a part easily and efficiently, it is important to select appropriate measuring device considering the characteristics of each measuring machine and part information. In this research, an optimal measuring device selection system using neural networks is proposed. There are two major steps: Firstly, the measuring information such as curvature, normal, type of surface, edge, and facet approximation is extracted from the CAD model. Second, the best suitable measuring device is proposed using the neural network system based on the knowledge of the measuring parameters and the measuring resources. An example of machine selection is implemented to evaluate the performance of the system.