Flexible Automation and Intelligent Manufacturing: Establishing Bridges for More Sustainable Manufacturing Systems - Proceedings of FAIM 2023, June 18-22, 2023, Porto, Portugal, Volume 1: Modern Manufacturing
Flexible Automation and Intelligent Manufacturing: Establishing Bridges for More Sustainable Manufacturing Systems - Proceedings of FAIM 2023, June 18-22, 2023, Porto, Portugal, Volume 1: Modern Manufacturing
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灵活自动化和智能制造：为更可持续的制造系统建立桥梁 - FAIM 2023 会议记录，2023 年 6 月 18-22 日，葡萄牙波尔图，第 1 卷：现代制造
DOI：
10.1007/978-3-031-38241-3_67
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发表时间：
2024
期刊：
影响因子：
--
通讯作者：
Aslan A
中科院分区：
文献类型：
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作者：
Aslan A
在没有完全自动化且涉及人类工人的复杂制造业中，重要的是要识别与计划生产进度的偏差，并找到瓶颈以提高效率。这不是一件容易的事，因为它需要工人如何实际执行制造活动的数据。超宽带（UWB）标签是跟踪运动的传感器，可用于收集这些数据。以前的研究主要集中在使用这些传感器来检测故障和异常，并确保工人的安全。然而，本文提出了一种方法，使用UWB数据发现过程模型的制造活动，使用过程挖掘技术。我们将我们的方法应用到具有UWB数据的真实的装配线，并发现装配线中规定的工艺步骤和瓶颈的偏差，这表明第一个装配步骤与其他步骤相比可能需要两倍的时间。
In complex manufacturing industries that are not fully automated and involve human workers it is important to identify deviations from the planned production schedule and locate bottlenecks for improved efficiency. This is not an easy task as it requires data on how workers are actually performing the manufacturing activities. Ultra-wideband (UWB) tags, which are sensors that track movement, can be used to collect this data. Previous research has mostly focused on using these sensors to detect faults and anomalies and to ensure worker safety. However, this paper presents a method for using UWB data to discover process models of manufacturing activities using process mining techniques. We applied our method to a real assembly line with UWB data and found deviations from the prescribed process steps and bottlenecks in the assembly line, which indicated that the first assembly step can take twice as much time compared to other steps.