Abstract

Improving machining quality and production efficiency is the focus of the manufacturing industry. How to obtain efficient machining parameters under multiple constraints such as machining quality is a severe challenge for manufacturing industry. In this paper, a multi-agent evolutionary reinforcement learning method (MAERL) is proposed to optimize the machining parameters for high quality and high efficiency machining by combining the graph neural network and reinforcement learning. Firstly, a bootstrap aggregating graph attention network (Bagging-GAT) based roughness estimation method for machined surface is proposed, which combines the structural knowledge between machining parameters and vibration features. Secondly, a mathematical model of machining parameters optimization problem is established, which is formalized into Markov decision process (MDP), and a multi-agent reinforcement learning method is proposed to solve the MDP problem, and evolutionary learning is introduced to improve the stability of multi-agent training. Finally, a series of experiments were carried out on the commutator production line, and the results show that the proposed Bagging-GAT-based method can improve the prediction effect by about 25% in the case of small samples, and the MAERL-based optimization method can better deal with the coupling problem of reward function in the optimization process. Compared with the classical optimization method, the optimization effect is improved by 13% and a lot of optimization time is saved.

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