Combined Optimization and Virtual Commissioning of Production Systems with a High Volume of Material Flow using Multiscale Network Models (OptiPlant)

使用多尺度网络模型对具有大量物料流的生产系统进行组合优化和虚拟调试 (OptiPlant)

• 批准号: 327964174
• 负责人: Professorin Dr. Simone Göttlich
• 金额: --
• 依托单位: Lehrstuhl für wissenschaftliches Rechnen
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2017
• 资助国家: 德国
• 起止时间: 2016-12-31 至 2021-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/327964174?language=en
• 关键词: Combined; Optimization; Virtual; Commissioning; Production

The time-deterministic hardware-in-the-loop simulation (HiLS) of machines in a material flow-intensive production system is promising, as for example in areas like beverage and packaging technology (special machinery). The individual design of each machine and the commissioning is error-prone, which often requires costly and time-consuming corrections of the control code on site. The integration of new machines into existing production systems leads to cost-intensive downtimes of the complete system. Further, the increasing demand for more extensive and rapidly changing product ranges results in a higher complexity of the control system. In the area of special machinery, where machines are designed and manufactured according to customer requirements, simulation-based methods could accelerate the engineering process, reduce costs and lower the error rate significantly. Nevertheless, simulation-based methods concerning individual machines as well as the material flow in connection with the real control system are not applied today in this area. The objective is to transfer the results collected with the HiLS of CNC machines for testing the service performance and control validation to the beverage technology with its specific requirements. However, the description of virtual machines with HiLS the implementation of time-deterministic and computationally efficient algorithms. At present, there is no material flow model that ensures a time-deterministic computation of the material flow dynamics with a large number of moving objects (in beverage technology approximately 20.000 - 50.000 bottles per hour). The use of a multi-scale network model for simulating the material flow within a HiLS is, therefore, promising and new. This will be investigated within this research project. Furhermore, a mathematical throughput optimization of the transport system for finding the optimal flow rate can be performed with the flow model. Currently, throughput optimizations are only conducted disregarding the feasibility in the implementation phase. In addition, here for the most part only event-discrete material flow simulations are found, which do not consider the system layout. In this research project, a mathematical throughput optimization preceding the HiLS and based on the flow model in combination with a service performance is to be developed during the virtual commissioning, using the multi-scale network model on the real control system.

在物流密集型生产系统中，机器的时间确定性硬件在环仿真（HiLS）很有前途，例如在饮料和包装技术（特殊机械）等领域。每台机器的单独设计和调试容易出错，这通常需要在现场对控制代码进行昂贵且耗时的校正。将新机器集成到现有生产系统中会导致整个系统的成本高昂的停机时间。此外，对更广泛和快速变化的产品范围的日益增长的需求导致控制系统的更高复杂性。在特殊机械领域，机器是根据客户要求设计和制造的，基于仿真的方法可以加快工程流程，降低成本并显着降低错误率。然而，基于仿真的方法涉及单个机器以及与真实的控制系统相关的物料流，目前在该领域中还没有应用。我们的目标是将HiLS数控机床收集的结果用于测试服务性能和控制验证，以满足饮料技术的特定要求。然而，用HiLS描述虚拟机需要时间确定性和计算效率高的算法的实现。目前，还没有材料流模型来确保大量移动物体（在饮料技术中大约每小时20.000 - 50.000瓶）的材料流动态的时间确定性计算。因此，使用多尺度网络模型来模拟HiLS内的物质流是有前途的和新的。这将在本研究项目中进行调查。此外，可以利用流动模型执行运输系统的数学吞吐量优化以找到最佳流速。目前，吞吐量优化只是在实施阶段进行，而不考虑其可行性。此外，这里大多数情况下只发现事件离散物料流模拟，不考虑系统布局。在这个研究项目中，一个数学吞吐量优化HiLS之前，并结合服务性能的流量模型的基础上开发的虚拟调试期间，使用多尺度网络模型上的真实的控制系统。