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1、摩擦表面边界膜温度特性的神经网络模型Abstract：With the rapid development of friction surface boundary films, the study of temperature characteristics has become an important research field in the field of materials science. In this paper, a neural network model based on BP algorithm is proposed to study the temperatur
2、e characteristics of friction surface boundary films. The temperature characteristics of friction surface boundary films under different conditions are analyzed by using the established neural network model, and the simulation results are compared with the experimental results to verify the effectiv
3、eness of the model.Keywords：Friction surface boundary films; Temperature characteristics; Neural network model; BP algorithmIntroductionFriction surface boundary films refer to a kind of thin film formed on the surface of materials during friction or sliding. With the development of materials scienc
4、e, the research on friction surface boundary films has attracted more and more attention. On the one hand, friction surface boundary films can significantly reduce the friction coefficient and wear rate of materials, improve the efficiency and service life of mechanical parts, and play an important
5、role in energy conservation and environmental protection; on the other hand, with the continuous improvement of the working conditions of materials, the temperature of friction surface boundary films has become an important factor affecting their performance.In order to accurately predict the temper
6、ature characteristics of friction surface boundary films, it is necessary to establish a reasonable mathematical model. In recent years, with the rapid development of neural network technology, many researchers have used neural network methods to study the temperature characteristics of friction sur
7、face boundary films, and achieved good research results.MethodologyBP algorithm, as the most commonly used neural network algorithm, is widely used in various fields. In this paper, a neural network model based on BP algorithm is proposed to study the temperature characteristics of friction surface
8、boundary films. The model consists of an input layer, a hidden layer, and an output layer. The input layer contains the input variables of the model, including the material properties, the structure of the friction pair, and the friction conditions. The hidden layer contains a certain number of neur
9、ons that are not directly connected to the input or output layer, which are used to process the inputs and generate the output. The output layer contains the output variable of the model, that is, the temperature characteristics of the friction surface boundary film.The training of the neural networ
10、k model is divided into two steps. In the first step, the model is trained with actual experimental data to obtain the initial weights and biases of the network. In the second step, the trained model is used to simulate the temperature characteristics of the friction surface boundary film under diff
11、erent conditions. The simulation results are compared with the experimental results to verify the effectiveness and accuracy of the model.Results and DiscussionThe temperature characteristics of the friction surface boundary film under different conditions were simulated using the neural network mod
12、el established in this paper. The simulation results show that the temperature of the friction surface boundary film increases with the increase of friction force, sliding speed, and contact pressure, and decreases with the increase of film thickness and thermal conductivity. The simulation results
13、are in good agreement with the experimental results, indicating that the established neural network model has good accuracy and reliability in predicting the temperature characteristics of friction surface boundary films.ConclusionIn this paper, a neural network model based on BP algorithm is propos
14、ed to study the temperature characteristics of friction surface boundary films. The simulation results show that the temperature of the friction surface boundary film is affected by various factors such as friction force, sliding speed, contact pressure, film thickness, and thermal conductivity. The
15、 established neural network model has good accuracy and reliability in predicting the temperature characteristics of friction surface boundary films, which provides a theoretical basis for the design and optimization of friction surface boundary film materials.Moreover, the proposed neural network m
16、odel has several advantages over traditional models. Firstly, the neural network model can incorporate a large number of input variables, which can provide more accurate predictions than traditional mathematical models. Secondly, the neural network model has strong learning and adaptive ability, which can easily handle complex and nonlinear relationships between variables. Thirdly, the neural network model is able to generalize well to new data that
