Integrates multi-head attention with SAC for faster convergence in optimizing additive manufacturing parameters to minimize porosity, outperforming DQN, PPO, TD3, and vanilla SAC.
Physics-informed neural networks: A step towards data-driven optimization of additive manufacturing,
3 Pith papers cite this work. Polarity classification is still indexing.
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A TPSR-based framework with four LLM roles integrates language model reasoning into industrial automation via digital twins, achieving high task executability in case studies.
A systematic literature review defines self-explainability, proposes a taxonomy and levels framework, and reports that most approaches are conceptual with no standard evaluation method.
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Multi-Head Attention-Based Feature Extractor Integration with Soft Actor-Critic for Porosity Prediction and Process Parameter Optimization in Additive Manufacturing
Integrates multi-head attention with SAC for faster convergence in optimizing additive manufacturing parameters to minimize porosity, outperforming DQN, PPO, TD3, and vanilla SAC.
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Self-Explainability in Self-Adaptive and Self-Organising Systems: Status and Research Directions
A systematic literature review defines self-explainability, proposes a taxonomy and levels framework, and reports that most approaches are conceptual with no standard evaluation method.