用注意力机制增强强化学习，更快更准优化3D打印参数减少孔隙率。
Multi-Head Attention-Based Feature Extractor Integration with Soft Actor-Critic for Porosity Prediction and Process Parameter Optimization in Additive Manufacturing
- 引入多头注意力特征提取器，提升对细微参数变化的感知能力。
- 14轮内收敛至322.79奖励值，优于DQN/PPO/TD3/SAC等方法。
- 适合需要高精度参数优化的增材制造场景，尤其关注缺陷控制。
增材制造过程优化需精确控制参数以减少孔隙等缺陷。传统基于离散动作空间的强化学习方法存在收敛慢、易陷入局部最优的问题，限制了其在高精度制造任务中的应用。本文提出一种结合连续动作空间与新型架构的方法，将多头注意力机制融入Soft Actor-Critic（SAC）算法。注意力特征提取器增强了智能体对低维输入特征中细微变化的捕捉能力，有助于在含局部极小值的价值空间中实现更优的探索-利用平衡。在激光粉末床熔融工艺中验证该方法，用于孔隙率预测与工艺参数优化，结果显示其收敛速度更快，最终奖励值更高，相比DQN、PPO、TD3及标准SAC表现更优。所提方法在14个训练周期内达到322.79的收敛奖励值，全程训练稳定。
原文摘要 · Abstract (English)
Additive manufacturing process optimization requires precise parameter control to minimize defects such as porosity. Traditional reinforcement learning (RL) approaches using discrete action spaces suffer from slow convergence and susceptibility to local optima, limiting their effectiveness for high-precision manufacturing tasks. This study addresses these limitations by employing a continuous action space combined with a novel architecture that integrates a multi-head attention mechanism with the Soft Actor-Critic (SAC) algorithm. The attention-based feature extractor enhances the agent's ability to capture subtle variations in low-dimensional input features, enabling more effective exploration-exploitation balance for navigating value spaces with local minima. We validate our approach on porosity prediction and process parameter optimization in laser powder bed fusion, demonstrating faster convergence and higher final reward values compared to standard RL methods including DQN, PPO, TD3, and vanilla SAC. The proposed methodology achieves a convergence value of 322.79 within 14 episodes, outperforming existing approaches while maintaining stability throughout training.
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