The contact mode of a joint interface based on improved deep neural networks and its application in vibration analysis

Abstract

The precision of the contact model for a joint interface strongly depends on the fractal dimension and fractal roughness coefficient. In this paper, an improved deep neural network method was adopted to predict the surface appearance parameters. In order to meet the high accuracy requirements for the prediction results of the contact model, a novel surface appearance prediction model was established utilizing a regularized deep belief network. The Bayesian regularization strategy was used to reduce the network weights during unsupervised training, which can effectively restrain the contribution of unimportant neurons. This allows to limit the occurrence of overfitting, and the layer-by-layer training was performed for each hidden layer based on a continuous transfer function. Meanwhile, the surface appearance parameters of the joint interface could be obtained by plugging arbitrary machining parameters into the training model. The specific contact model was then established based on fractal theory by applying the above-mentioned prediction results. The parameters of the joint interface were used to simulate the frequencies and vibration modes of frame-shaped structural parts. The contact model was validated by comparing the simulation results with experimental data. The proposed model is expected to provide a theoretical basis for optimizing the structure and improving the accuracy of computerized numerical control machines.