Deep q-network based directed energy deposition laser power off-line planning method

Abstract

The application discloses a kind of based on deep Q network's directional energy deposition laser power offline planning method, belong to the field of additive manufacturing.The numerical model of macroscopic temperature field based on directional energy deposition process is constructed as interactive environment, the volume of molten pool is extracted as the state feature of heat process, and the target molten pool volume is determined by experiment calibration.The power planning problem is constructed as Markov decision process, the state vector consisting of molten pool volume sequence, discrete power adjustment action set and Gaussian reward function centered on target volume are defined, and the deep reinforcement learning framework is formed.Under this framework, the deep Q network agent is trained to learn the optimal power adjustment strategy.Finally, the laser power sequence of the deposition process is planned offline using the trained agent.The application can plan the optimal laser power sequence offline, stabilize the heat input of directional energy deposition process, effectively suppress defects such as incomplete fusion and cracks, and improve the quality of additive manufacturing.

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