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재사용 클래스 구성을 위한 일반화 / 상세화 방법에 관한 연구
남덕희(Dughee Nam),김진수(Jinsoo Kim),이경환( Kyungwhan Lee) 한국정보과학회 1994 한국정보과학회 학술발표논문집 Vol.21 No.2B
일반화와 상세화는 재사용 클래스를 설계하는데 중요한 지침이 되고 있다. 이러한 일반화와 상세화를 이용하여 재사용성이 높은 클래스 라이브러리를 구축할 수 있는 방법을 제안하고 이 방법을 적용한 도구를 개발하였다. 제안된 방법은 클래스간의 관계를 분석하여 그 관계에 따라 상속관계를 갖도록 하였다. 제안된 일반화/상세화 방법은 기존의 클래스 라이브러리를 재구성하는데 유용할 뿐만 아니라 새로운 클래스의 등록시에도 적용할 수 있다.
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Predictive Maintenance in Injection Molding Process
ChulSoon Park,DugHee Moon 한국산업경영시스템학회 2014 한국산업경영시스템학회 학술대회 Vol.2014 No.춘계
In this research, we are developing a predictive maintenance model of the injection molding machines based on the prediction of trend of injection molding parameters. At first, we developed an interface method to directly monitor the real-time injection molding parameter data from injection molding machine controller. Second, we identified the principal injection parameters which mainly affect the quality of injection molding products and need to be monitored for maintenance. Third, based on the time series analysis, we developed the prediction models of the principal injection molding parameters, which are identified by previous statistical model to forecast its future patterns/trends and schedule its maintenance point in time. We adopted Nelson’s rules to identify abnormal patterns in predicted data. Finally, we used FTA (fault tree analysis) to relate the injection molding parameters to the parts of the injection molding machine, find out the equipment or parts to be corrected.
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A predictive maintenance approach based on real-time internal parameter monitoring
Park, Chulsoon,Moon, Dughee,Do, Namchul,Bae, Sung Moon Springer-Verlag 2016 INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TE Vol.85 No.1
<P>Since continuous real-time components or equipment condition monitoring is not available for injection molding machines, we propose a predictive maintenance approach that uses injection molding process parameters instead of machine components to evaluate the condition of equipment. In the proposed approach, maintenance decisions are made based on the statistical process control technique with real-time data monitoring of injection molding process parameters. First, machine components or equipment of injection molding machines, which require maintenance, is identified and then injection molding process parameters, which may be affected by malfunctioning of the previously identified components, are identified. Second, regression analysis is performed to select the process parameters that significantly affect the quality of the lens and require a high degree of attention. By analyzing the patterns of real-time monitored data series of process parameters, we can diagnose the status of the components or equipment because the process parameters are affected by machine components or equipment. Third, statistical predictive models for the selected process parameters are developed to apply statistical analysis techniques to the monitored data series of parameters, in order to identify abnormal trends. Fourth, when abnormal trends or patterns are found based on statistical process control techniques, maintenance information for related components or equipment is notified to maintenance workers. Finally, a prototype system is developed to show feasibility in a LabVIEWA (R) environment and an experiment is performed to validate the proposed approach.</P>
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