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首页> 外文期刊>Applied Mechanics and Materials >Smart Melting: Increasing Efficiency in Non-Ferrous Melting and Die-Casting Plants through Incident Management
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Smart Melting: Increasing Efficiency in Non-Ferrous Melting and Die-Casting Plants through Incident Management

机译:智能熔炼:通过事件管理提高有色熔炼和压铸工厂的效率

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摘要

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 die-casting 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. 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:该研究是研究合作巴伐利亚绿色工厂(GFB)中的Smart Melting项目的一部分。该项目的主要目标是提高关键指标OEE(总体设备效率)和减少有色压铸行业的能耗。通过在工业合作伙伴的工厂中实施软件工具来收集必要的数据以改善维护,从而创建了事件管理的必要要求。本文重点介绍工业伙伴工厂中数据采集的当前状态以及事件管理系统估计的过程改进潜力。对收集的生产压铸机的事件数据进行处理和分析。在数据分析中,根据事件的原因对事件进行分类并分配给相应的机器。为了获得统计概率分布,研究了修复时间(TTR)和失效时间(TTF)。 TTR是指数分布的值,可以使用Weibull分布描述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指标OEE的影响。改进的维护减少了事件数量并增加了TTF。缩短停机时间的改进的事件管理可降低TTR。作为改进的结果,OEE的可用性因子显着增加。另外,可以预期OEE的品质因数(合格零件的比例)将会增加。

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