Prediction of Turbomolecular Pump Performance Using Neural Networks

Turbomolecular pumps, as critical equipment for maintaining a clean high-vacuum environment, are widely utilized in various fields such as semiconductor manufacturing and space simulation. However, accurately and efficiently predicting the performance of turbomolecular pumps has been a challenging issue. Existing methods often suffer from a lack of generality and computational efficiency, making them less than satisfactory. This study focuses on developing a performance prediction method for turbomolecular pumps based on a neural network model to address these challenges. A neural network model is proposed, which can effectively learn and simulate the complex dynamic behavior of turbomolecular pumps. The model is trained using a combination of numerical simulation results and experimental test data, endowing it with excellent generalization and predictive accuracy. Through dual verification via numerical simulation and experimental testing, it is demonstrated that the neural network model can accurately predict the pumping performance of turbomolecular pumps. Additionally, a comprehensive analysis of the factors influencing pumping capacity is performed based on experimental test results. The results indicate that this model can serve as a tool for evaluating the structural design and performance assessment of turbomolecular pumps, aiding in the optimization of their design to enhance pumping efficiency and applicability.