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Multi-agent evolution reinforcement learning method for machining parameters optimization based on bootstrap aggregating graph attention network simulated environment
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DOI：10.1016/j.jmsy.2023.02.015.png)
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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 evolu-tionary 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 pro-cess. Compared with the classical optimization method, the optimization effect is improved by 13% and a lot of optimization time is saved.
Keyword:
Surface roughness
Cutting efficiency
Machining parameters optimization
Graph attention network
Multi-agent reinforcement learning
Evolutionary learning
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14.2
论文数:
2.7K
被引数:
1.6W
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