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.

面对一个多变的市场，越来越多的产品和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工艺链和生产资源，集中生产控制方法已经达到了极限。而分散控制方法具有灵活性高、适应性快的特点。随着信息物理生产系统（CPPS）的发展，现在首次可以实现与订单相关的全面数据采集、生产组件之间的通信和使用本地计算能力的技术。这是采用分散控制方法的必要先决条件。然而，从方法论的角度来看，算法的局部优化倾向是一个进一步的障碍。在局部决策中基于经验的全局最优考虑可以消除这一缺点。拟议项目的主要目标是对分散生产控制方法的基础研究，该方法允许自主和可变决策，同时根据生产系统的全球系统性能进行基于经验的自我优化。该方法应特别考虑到车间生产的要求。为了实现项目目标，首先在CPPS框架内研究适合分散生产控制的系统架构。随后，设想了一种方法，允许分散和可变的决策制定，并基于经验考虑全局系统性能。为此所需的估值基础是由一套待审查的关键数字组成的。为后续的研究搭建了测试环境。为此，首先建立了具有代表性的车间生产仿真模型。控制仿真模型内物料流的软件实现控制方法与此相关联。最后，在结构化的测试计划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。