Controlling milling distortion in die-forged structural components via causal reinforcement learning

The residual stress field in die-forged blanks exhibits complex distribution patterns and high magnitudes, serving as the primary cause of significant milling distortion in aluminum alloy structural components. To mitigate this issue, finishing allowances are introduced to control milling distortion. Variations in residual stress fields across blanks necessitate online decision-making for distortion control. Although data-driven methods theoretically enable such decision-making, traditional approaches are hampered by imbalanced training data and confounding factors, making it difficult to establish stable relationships among variables, and limiting the robustness of the model’s decisions. To overcome this challenge, this study explores the underlying mechanism of distortion control via finishing allowance. The proposed Causal Deep Q-Network (CDQN) method constructs an intelligent agent based on a causal graph. The residual stress field of the blank is used as an instrumental variable, and intervention operations are applied to block the backdoor path, enabling the identification of causal effects among variables. Verification tests were conducted on typical structural components (600 mm × 240 mm × 27 mm) using 7075-T6 die-forged blanks. The average distortion observed was 0.027 mm (variance: 9.5 × 10⁻⁶) in simulation, and 0.077 mm (variance: 7 × 10⁻³) in actual milling tests on three components. These results validate the proposed method’s effectiveness in precisely controlling milling distortion in structural components.