Reconfiguration demand is increasing due to frequent requirement changes for manufacturing systems. Recent approaches aim at investigating feasible configuration alternatives from which they select the optimal one. This relies on processes whose behavior is not reliant on e.g. the production sequence. However, when machine learning is used, components' behavior depends on the process' specifics, requiring additional concepts to successfully conduct reconfiguration management. Therefore, we propose the enhancement of the comprehensive reconfiguration management with transfer learning. This provides the ability to assess the machine learning dependent behavior of the different CPPS configurations with reduced effort and further assists the recommissioning of the chosen one. A real cyber-physical production system from the discrete manufacturing domain is utilized to demonstrate the aforementioned proposal.
翻译:由于制造系统的需求频繁变化,重组需求正在增加。最近的做法旨在调查可行的配置替代方案,从而选择最佳的组合替代方案。这依赖于行为不依赖生产序列等过程。然而,在使用机器学习时,组件的行为取决于流程的具体情况,需要更多概念才能成功进行重组管理。因此,我们提议通过转让学习加强全面重组管理。这为评估不同CPPS配置的机器学习依附行为提供了能力,减少了工作,并进一步协助重新启用所选择的结构。一个来自离散制造领域的真正的网络物理生产系统被用来展示上述建议。