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Smart Melting: Increasing Efficiency in Non-Ferrous Melting and Die-Casting Plants through Incident Management

机译:智能熔化:通过事件管理提高有色金属熔融和压铸厂的效率

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This study is part of the project Smart Melting in the research cooperation Green Factory Bavaria (GFB). The main targets of the project are the improvement of the key figure OEE (Overall Equipment Effectiveness) and the reduction of the energy consumption in the non-ferrous diecasting industry. By implementing a software tool in the factory of an industrial partner, which collects the essential data to improve maintenance, a necessary requirement for an incident management is created. This article focuses on the current state of data acquisition in the plant of the industrial partner and the estimated potential of process improvement by an incident management system. The collected incident data of the producing die-casting machines are processed and analyzed. In the data analysis the incidents are classified according to their cause and assigned to the respective machine. The time to repair (TTR) and the time to failure (TTF) are investigated in order to obtain statistical probability distributions. The TTR is an exponentially distributed value and the TTF can be depicted using the Weibull distribution [1]. A simulation model based on statistically concentrated data is used to determine the effects of a better incident management on the key figure OEE. An improved maintenance reduces the number of incidents and increases the TTF. An improved incident management which shortens the downtime reduces the TTR. As a result of the improvements the availability factor of the OEE increases remarkably. Additionally an increase of the quality factor (proportion of good parts) of the OEE can be expected.
机译:本研究是该项目的一部分是研究合作绿色工厂巴伐利亚(GFB)的智能融化。该项目的主要目标是改善关键图OEE(整体设备效率)和有色金属工业中的能量消耗的降低。通过在工业伙伴的工厂实施软件工具,该工具收集基本数据以改善维护,创建了事件管理的必要要求。本文重点介绍了工业合作伙伴工厂的现状和事件管理系统的估计工艺改进潜力。处理和分析生产压铸机的收集的入射数据。在数据分析中,事件根据其原因进行分类并分配给各个机器。研究了修复(TTR)的时间和失败时间(TTF)以获得统计概率分布。 TTR是指数分布值,可以使用Weibull分布[1]来描绘TTF。基于统计集中数据的仿真模型用于确定更好的事件管理对关键图OEE的影响。改进的维护减少了事件的数量并增加了TTF。改进的事件管理,缩短停机时间减少了TTR。由于改进了OEE的可用性因子显着增加。另外,可以预期oee的质量因数(良好部分的比例)。

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