Research on Temperature Field Prediction Model of Electric Spindle Based on Improved BP Neural Network

The motorized spindle is the core component of CNC machine tools. In order to ensure its processing performance and processing safety, the temperature field of motorized spindle is studied. The three-dimensional model of the motorized spindle is established, and the convective heat transfer coefficient of the internal heat load and the simulation boundary condition are calculated by combining the heat transfer theory. The simulation is carried out by the finite element analysis software, and the internal temperature distribution of the motorized spindle under thermal steady state is calculated. Based on the numerical simulation analysis method and the thermal balance test method, the data basis for the prediction model of the motorized spindle temperature field is provided. The traditional BP neural network algorithm and PSO-BP neural network algorithm are used to predict the temperature of the motorized spindle measuring point under specific working conditions, and the temperature field prediction results are compared and analyzed. The results show that the PSO-BP neural network prediction model has good compatibility for variable data input, and the prediction results show little difference, which has high prediction accuracy and robustness.

Turbine Blade Temperature Field Prediction Using the Numerical Methods

Nowadays, material science and stress characteristics are crucial in the field of jet engines. There are methods for fatigue life, stress, and temperature prediction; however, the conventional methods are ineffective and time-consuming. The article is devoted to the research in the field of application of the numerical methods in order to develop an innovative methodology for the temperature fields prediction based on the integration of the finite element methods and artificial neural networks, which leads to the creation of the novel methodology for the temperature field prediction. The proposed methodology was applied to the temperature field prediction on the surface blades of the experimental iSTC-21v jet engine turbine. The results confirmed the correctness of the new methodology, which is able to predict temperatures at the specific points on the surface of a turbine blade immediately. Moreover, the proposed methodology is able to predict temperatures at specific points on the turbine blade during the engine runs, even for the multiple operational regimes of the jet engine. Thanks to this new unique methodology, it is possible to increase the reliability and lifetime of turbines and hot parts of any jet engine and to reduce not only the maintenance but also the research and development costs due to the significantly lower time demands. The main advantage is to predict temperature fields much faster in comparison to the methods available today (computational fluid dynamics (CFD), etc.), and the major aim of the proposed article is to predict temperatures using a neural network. Apart from the above-mentioned advantages, the article’s main purpose is devoted to the artificial neural networks, which have been until now used for many applications, but in our case, the neural network was for the first time applied for the temperature field prediction on the turbine blade.