Dynamic modeling of sliding joints based on transversely isotropic virtual material and deep neural network

Abstract

Aiming at the problem that the current isotropic virtual material-based modeling method for dynamic modeling of sliding joints can hardly reflect the difference between normal and tangential mechanical properties, which restricts the modeling quality, a transversely isotropic material model is introduced to comprehensively describe the mechanical properties of sliding joints. Firstly, a dynamic model based on transversely isotropic virtual material and Deep Neural Network (DNN) is constructed to reflect the relationship between the dynamic parameters of transversely isotropic virtual material ( E τ , E n , μ τ , μ n , G n , ρ ) and the natural frequencies. Then, using the cuckoo search algorithm, the transversely isotropic virtual material parameters are determined. Subsequently, as an application case, the flat and V-guide joints of the M7120D/H surface grinder are employed to validate the proposed modeling method. Finally, compared to the experimental modal test results, the error of natural frequencies is less than 1%, which achieves high accuracy. Additionally, the quantitative comparison based on the same application case shows that the proposed modeling method is superior to isotropic virtual material and spring damping method.