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Energy efficiency optimization method for process parameters of machining system based on reinforcement learning

Process parameters are an important part of the CNC machining system, and the reasonable selection of process parameters can significantly affect the energy efficiency of the machine tool. In order to reduce the energy consumption of the processing system and improve the energy efficiency, this paper proposes a method to optimize the process parameters of the processing system based on reinforcement learning. First, construct an objective function with process parameters as variables and energy efficiency and process time as constraints. Then it analyzes its effectiveness through actual turning processing cases. Finally, through comparative experiments, it is concluded that the method proposed in this paper can reduce energy consumption by 20% and the consumption of cutting fluid by 35%, which verifies its effectiveness and practicability.