Transactions of Nanjing University of Aeronautics & Astronautics
A Reinforcement Learning Method Based on Hybrid Proximal Policy Optimization for Deformation Control in Machining Titanium Alloy Components
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Abstract:
A hybrid action deformation control method based on hybrid proximal policy optimization （HPPO） is proposed for titanium alloy structural components. Existing reinforcement learning algorithms are generally confined to either discrete or continuous action spaces， and thus cannot simultaneously optimize machining sequence and allowance. The proposed method unifies both decision variables—machining sequence as discrete actions and machining allowance as continuous parameters—into a single parameterized hybrid action space. Online deformation force monitoring data serve as state feedback to enable adaptive control under dynamic machining conditions. A dual-layer reward mechanism combining process-level deformation force uniformity with terminal deformation convergence is designed to guide the agent toward synchronized suppression of both local and global deformations. Experimental validation on a Ti6Al4V aviation structural component demonstrates that the proposed method reduces average machining deformation from 0.103 mm to 0.054 mm， with RMSE decreasing from 0.119 mm to 0.071 mm， representing a 47.57% reduction relative to the uncontrolled case. These results confirm the accuracy and effectiveness of the proposed method in real manufacturing environments.
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Project Supported:
This work was financially supported by the Key Projects of the Basic Scientific Research Program （No.JCKY2024605B003） and National Natural Science Foundation of China （No.52575579）.
Clc Number:
V262.3
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