Multi-Head Attention-Based Feature Extractor Integration with Soft Actor-Critic for Porosity Prediction and Process Parameter Optimization in Additive Manufacturing
Researchers developed a machine learning system combining multi-head attention mechanisms with Soft Actor-Critic reinforcement learning to optimize additive manufacturing processes and predict porosity defects. The approach demonstrates faster convergence and superior performance compared to existing RL algorithms, achieving a convergence value of 322.79 within 14 episodes.