Digital twin and reinforcement learning-based additive manufacturing optimization

With the rapid development of additive manufacturing technology, its potential in complex structural manufacturing has gained widespread attention. However, there are many challenges in the additive manufacturing process, such as printing accuracy control and process parameter optimization. These challenges directly affect the performance and reliability of the final product. In this paper, we combined reinforcement learning with digital twin, and proposed an additive manufacturing optimization method to enhance the intelligence and adaptability of the manufacturing process. Firstly, we established a virtual simulation platform for additive manufacturing processes with digital twin technology based on physical models and real-time sensor data fusion. The manufacturing process was monitored and predicted through realtime simulation and data-driven methods. Next, we use Deep Q-Network (DQN) to adaptively adjust key process parameters such as printing speed, layer thickness, temperature, etc., to optimize the printing quality and efficiency. The experimental results show that this method can significantly improve the stability of the manufacturing process and product quality.