Augmented Go & See: An approach for improved bottleneck identification in production lines
Augmented Go & See: An approach for improved bottleneck identification in production lines
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DOI：
10.1016/j.promfg.2019.03.023
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
Constantin Hofmann;T. Staehr;Samuel Cohen;N. Stricker;B. Haefner;G. Lanza
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文献类型：
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作者：
Constantin Hofmann;T. Staehr;Samuel Cohen;N. Stricker;B. Haefner;G. Lanza
生产线上的瓶颈经常发生变化，因此很难识别。它们会导致产量下降、生产时间延长和工作进度增加。Go & See是一种成熟的精益实践，经理们到车间去亲眼看到问题。混合现实是一种很有前途的技术，可以提高复杂生产环境中的透明度。直到最近，混合现实应用程序在需要高性能硬件的计算能力方面一直要求很高。本文提出了一种基于自携设备原理，利用混合现实技术进行生产线瓶颈识别的实时KPI可视化方法。开发的应用程序使用图像识别来识别工作站，并在增强现实中可视化周期时间和正在进行的工作。有了这些额外的信息，就有可能发现瓶颈的不同根源，例如由于故障导致的系统更高或不同的周期时间。根据acatech工业4.0成熟度模型，该解决方案可以被归类为三级透明应用。可以看出，与标准Go & See相比，瓶颈和潜在原因的识别得到了改进。
Bottlenecks in production lines are often shifting and thus hard to identify. They lead to decreased output, longer throughput times and higher work in progress. Go & See is a well-established Lean practice where managers go to the shop floor to see the problems first hand. Mixed reality is a promising technology to improve transparency in complex production environments. Until recently, mixed reality applications have been very demanding in terms of computing power requiring high performance hardware. This paper presents an approach for real-time KPI visualization using mixed reality for bottleneck identification in production lines relying on the bring-your-own device principle. The developed application uses image recognition to identify work stations and visualizes cycle times and work in progress in augmented reality. With this additional information, it is possible to discern different root causes for bottlenecks, for example systematically higher or varying cycle times due to breakdowns. This solution can be classified according to the acatech industry 4.0 maturity model as a level 3 - transparency - application. It could be shown that the identification of bottlenecks and underlying reasons has been improved compared to standard Go & See.