基于状态的智能制造系统实时生产控制

Feifan Wang, Yan Lu, Feng Ju
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引用次数: 7

摘要

在本文中,我们提出了基于状态的智能制造实时生产控制,旨在通过自动评估生产系统的状态并动态配置智能产品和零件的加工路线来提高系统性能。将机器的退化条件定义为离散状态,并将其建模为马尔可夫链。在考虑机器退化和缓冲区占用的情况下,利用马尔可夫决策过程建立了一个最大化生产率的优化问题。在一个三机柔性生产系统中验证了该方法的有效性。传统上,状态监测和生产控制是由不同领域的专家分别设计、开发、安装和管理的。因此,本文还讨论了基于条件的生产控制的实施挑战,并确定和分析了现有和缺失的使能标准。
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Condition-based Real-time Production Control for Smart Manufacturing Systems
In this paper, we present condition-based real-time production control for smart manufacturing which is aimed at improving system performance by automatically assessing a production system's condition and dynamically configuring the processing routes for smart products and parts. A ma-chine's degradation condition is defined in discrete states and modeled as a Markov chain. By taking into account machines' degradation and buffers' occupancy, an optimization problem is formulated to maximize the production rate using Markov Decision Processes. The effectiveness of the method has been demonstrated on a three-machine flexible production system. Traditionally, condition monitoring and production control are designed, developed, installed and managed separately by different domain experts. Hence, in this paper, the implementation challenges of condition-based production control are also discussed, with the existing and missing enabling standards identified and analyzed.
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