Multi-Head Attention-Based Feature Extractor Integration with Soft Actor-Critic for Porosity Prediction and Process Parameter Optimization in Additive Manufacturing
arXiv:2606.20087v1 Announce Type: new Abstract: 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-b
The increasing sophistication of AI algorithms and computational power is enabling more precise control over complex manufacturing processes, making this integration timely.
This development allows for more efficient and robust optimization of additive manufacturing, reducing defects and improving product quality through advanced AI techniques.
The ability to use continuous action spaces with attention mechanisms in reinforcement learning makes AI-driven process optimization more effective for high-precision manufacturing.
- · Additive manufacturing industry
- · High-precision engineering
- · AI algorithm developers
- · Aerospace and medical device sectors
- · Traditional process control methods
- · Manufacturers unable to adopt AI
Improved quality and cost efficiency in additive manufacturing for critical components.
Increased adoption of AI and reinforcement learning in other complex industrial processes.
Acceleration of customized and on-demand manufacturing capabilities across various industries.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI