动态项目规划与数字孪生

Silvan Zahno, J. Corre, Darko Petrovic, Gilles Mottiez, Loïc Fracheboud, Axel Amand, Steve Devènes, Gilbert Maître, F. Carrino
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引用次数: 0

摘要

数字孪生(DT)概念在工业4.0和制造过程的数字化中起着至关重要的作用。DT是物理对象、系统或过程的虚拟表示,旨在准确地反映其现实世界的对应物。在制造业中,现有的工艺数据通常是不完整的,不符合DT的标准。然而,在专门的通信框架和更便宜、更易于使用的传感器的帮助下,可以将现有的制造执行系统(MES)和企业资源规划(ERP)数据与从车间收集的缺失数据集成起来,以创建一个全面的DT。本文提出了一种用于非线性铝制造生产的数字化车间决策支持系统(DSS)。该系统分为五个主要部分:车间订单的数字化;合并和整理MES、ERP和车间数据;时效炉生产步骤的自定义和遗传优化算法炉膛内物料最优放置和堆垛的布置施工机制;以及用户友好的图形用户界面(GUI)。通过三次测试对系统的性能进行了评价。第一个测试测量了数字化的效率,第二个测试旨在量化在大厅寻找数据包所节省的时间,最后一个测试测量了优化器对炉子生产率的影响。结果显示,炉容量提高了23.5%,但由于可用性和人为干预,确定了局限性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Dynamic project planning with digital twin
The digital twin (DT) concept plays a crucial role in Industry 4.0 and the digitalization of manufacturing processes. A DT is a virtual representation of a physical object, system, or process, designed to accurately reflect its real-world counterpart. In manufacturing, existing process data are often incomplete and do not qualify as a DT. However, with the help of specialized communication frameworks and cheaper, easier-to-use sensors, it is possible to integrate the existing manufacturing execution system (MES) and enterprise resource planning (ERP) data with the missing data gathered from the shop floor to create a comprehensive DT. In this paper, we present a digital shop floor decision support system (DSS) for non-linear aluminum manufacturing production. The system is split into five main components: digitization of shop floor orders; merging and sorting of MES, ERP, and shop floor data; custom and genetic optimization algorithms for the aging furnace production step; layout construction mechanism for optimal placement and stacking of orders in the furnace; and a user-friendly graphical user interface (GUI). The system’s performance was evaluated through three tests. The first test measured the efficiency of digitization, the second aimed to quantify time saved in finding packets in the hall, and the last test measured the impact of the optimizer on furnace productivity. The results revealed a 23.5% improvement in furnace capacity, but limitations were identified due to usability and human intervention.
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