Optimization design and reality of the virtual cutting process for the boring bar based on PSO-BP neural networks

摘要

Abstract Based on the traditional boring bar, a boring bar with friction damper is proposed in the paper. Firstly, the frequency response under different pressures is computed primarily based on the theory, which shows that the proposed boring bar has a certain vibration reduction effect. Secondly, the finite element model of the boring bar is built, and the first 6-order modes are computed, whose results are compared with the experimental value. As a result, the virtual reality of the boring bar is achieved. They are consistent with each other, which show that the finite element model is reliable. Then, the experimental cutting process of the boring bar is researched, which is compared with the simulation model with good coincidence. It is found from the result that the cutting simulation model of the boring bar is effective. Later, based on the verified simulation model, the positive pressure between the friction vibrator and boring bar, cutting speed, feed rate, back cutting depth and other parameters are changed to study the vibration reduction effects of the boring bar with friction damper. PSO (particle swarm optimization)-BP (backpropagation) neural network is then used to optimize the cutting process of the boring bar, and the optimal cutting parameters can be obtained. Finally, these optimized parameters are applied in the boring bar, the vibration reduction effect of the boring bar is verified by means of experiments, and the corresponding result shows that the proposed optimization in this paper is feasible. We can obtain higher quality work piece when we use this boring bar in the actual engineering.