Modern manufacturing systems are becoming increasingly complex, dynamic, and connected, and their performance is being affected by not only their constituent processes but also their system-level interactions. This paper presents an integrated modelling method based on a graph neural network (GNN) and multi-agent reinforcement learning (MARL) collaborative control for adjusting individual machining process parameters in response to system- and process-level conditions. The structural and operational dependencies among process machines are captured with a GNN. Iteratively trained with MARL, machines learn to adaptively control local process parameters, e.g., machining speed and depth of cut, while achieving the global goal of improving production yield.

现代制造系统正变得越来越复杂、动态和互联，它们的性能不仅受其组成过程的影响，还受其系统级交互的影响。本文提出了一种基于图神经网络 (GNN) 和多智能体强化学习 (MARL) 协同控制的集成建模方法，用于根据系统级和工艺级条件调整单个加工工艺参数。流程机器之间的结构和操作依赖关系由 GNN 捕获。通过 MARL 的迭代训练，机器学习自适应地控制局部工艺参数，例如加工速度和切削深度，同时实现提高产量的全球目标。