Analysis and prediction on the cutting process of constrained damping boring bars based on PSO-BP neural network model

Firstly, this paper computed the static and dynamic characteristics of common boring bars and constrained damping boring bars respectively, and the correctness of the computational model in time-frequency domain was also validated by experiments. Modal frequencies of constrained damping boring bars were obviously more than those of common boring bars, which could effectively avoid structural resonance in low frequency and had an obvious advantage in improving anti-vibration performance of boring bars. The absolute value of the maximum vibration acceleration of common boring bars was 13.1 m/s(2), while the absolute value of the maximum vibration acceleration of constrained damping boring bars was 9.1 m/s(2). The maximum vibration acceleration decreased by 30.5 %. The maximum vibration displacement of common boring bars was 5.2 mm and corresponding frequency was 201 Hz. The maximum vibration displacement of constrained damping boring bars was 2.3 mm and corresponding frequency was 235 Hz. When the analyzed frequency was lower than the frequency with the maximum vibration displacement, the displacement spectrum of common boring bars had more peak values. Thus, it was clear that constrained damping boring bars had an obvious advantage in improving vibration characteristics. The impact of cutting speed, feed rate and back cutting depth on vibration characteristics was studied respectively. Results showed that the vibration of constrained damping boring bars gradually decreased with the increase of cutting speed and gradually increased with the increase of feed rate and back cutting depth. In addition, the amplitude and frequency of vibration displacement spectrum of boring bars were basically unchanged no matter how cutting parameters changed. In order to quickly predict the vibration characteristic, BP neural network and PSO-BP neural network were respectively used to predict the cutting process of boring bars. When the iteration number of BP neural network was 300, iterative error was 0.00015 which was far more than the set target error. When the iteration number of PSO-BP neural network was 215, iterative error was converged to the set target error. Therefore, PSO-BP neural network had an obvious advantage in predicting the cutting process of boring bars. In addition, the predicted result of PSO-BP neural network was consistent with the experimental result, which showed that the neural network model in this paper was effective.