Self-optimizing decentralized production control

自优化分散生产控制

• 批准号: 426187351
• 负责人: Professor Dr.-Ing. Berend Denkena
• 金额: --
• 依托单位: Institut für Fertigungstechnik und Werkzeugmaschinen (IFW)
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2019
• 资助国家: 德国
• 起止时间: 2018-12-31 至 2022-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/426187351?language=en
• 关键词: Self; optimizing; decentralized; production; control

Facing a volatile market, an increasing variety of products and consequently increasingly complex manufacturing systems, production control is of particular importance in manufacturing. Due to the need for more frequent adaptation of process chains and production resources, centralized production control approaches are reaching their limits. In contrast, decentralized control approaches are characterized by high flexibility and quick adaptability. With the development of cyber-physical production systems (CPPS), technologies are now available for the first time with which comprehensive order-related data acquisition, communication between production components and the use of local computing capacities can be realized. This is an essential prerequisite for the introduction of decentralized control approaches. From a methodological point of view, however, local optimization tendencies of the algorithms represent a further obstacle. Experience-based consideration of the global optimum in local decision-making can eliminate this disadvantage.The main objective of the proposed project is the fundamental research of a method for decentralized production control that allows autonomous and variable decision-making with simultaneous experience-based self-optimization with regard to the global system performance of the production system. The method should take special account of the requirements of shop-floor production. In order to achieve the project goals, a suitable system architecture for decentralized production control is first examined within the framework of a CPPS. Subsequently, a method is conceived that allows decentralized and variable decision making with experience-based consideration of global system performance. The valuation basis required for this is formed by a system of key figures to be examined. A test environment is set up for the subsequent research. For this purpose, a simulation model of representative shop-floor production is first created. The software-implemented control method that controls the material flow within the simulation model is linked to this. Finally, simulation experiments are carried out within the framework of a structured test plan, which provide in-depth and scientifically substantial knowledge of the potentials and limits of the autonomous and learning control method being researched.

面对动荡的市场，越来越多的产品，以及因此而日益复杂的制造系统，生产控制在制造业中尤为重要。由于需要更频繁地调整流程链和生产资源，集中式生产控制方法正在达到其极限。相比之下，分散控制方法具有灵活性高、适应性强的特点。随着网络物理生产系统(CPPS)的发展，现在首次出现了能够实现与订单有关的全面数据采集、生产组件之间的通信和使用本地计算能力的技术。这是采用分散控制办法的基本先决条件。然而，从方法论的角度来看，算法的局部最优化倾向是另一个障碍。在局部决策中基于经验的全局最优考虑可以克服这一缺点。本项目的主要目标是研究一种分布式生产控制方法，该方法允许自主和可变的决策，同时关于生产系统的全局系统性能进行基于经验的自我优化。该方法应特别考虑车间生产的要求。为了实现项目目标，首先在CPPS的框架内研究了适用于分散生产控制的系统架构。随后，构思了一种方法，该方法允许在基于经验的基础上考虑全局系统性能的分散和可变决策。为此所需的估值基础是由一套要审查的关键数字系统构成的。为后续研究搭建了测试环境。为此，首先建立了典型车间生产的仿真模型。控制模拟模型内物质流动的软件实现的控制方法与此相关联。最后，在结构化测试计划的框架内进行了仿真实验，为所研究的自主学习控制方法的潜力和局限性提供了深入和科学的实质性知识。