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/s2, while the absolute value of the maximum vibration acceleration of constrained damping boring bars was 9.1 m/s2. 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.

[1]  Masoud Monjezi,et al.  Prediction of seismic slope stability through combination of particle swarm optimization and neural network , 2015, Engineering with Computers.

[2]  Jianqiang Yi,et al.  BP neural network prediction-based variable-period sampling approach for networked control systems , 2007, Appl. Math. Comput..

[3]  Chen Zichen.  Research on mechanism of cutting chatter suppression based on magnetorheological intelligent boring bar , 2008 .

[4]  Etsuo Marui,et al.  Suppression of chatter vibration of boring tools using impact dampers , 2000 .

[5]  Atsushi Matsubara,et al.  Vibration suppression of boring bar by piezoelectric actuators and LR circuit , 2014 .

[6]  Xiang Bai,et al.  An End-to-End Trainable Neural Network for Image-Based Sequence Recognition and Its Application to Scene Text Recognition , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  M. Sortino,et al.  Modeling the dynamic properties of conventional and high-damping boring bars , 2013 .

[8]  Jianbin Qiu,et al.  A Combined Adaptive Neural Network and Nonlinear Model Predictive Control for Multirate Networked Industrial Process Control , 2016, IEEE Transactions on Neural Networks and Learning Systems.

[9]  Jang-Kyo Kim,et al.  Vibration damping characteristics of carbon fiber-reinforced composites containing multi-walled carbon nanotubes , 2011 .

[10]  Tatiana Smirnova,et al.  On the Development of a Simple and Robust Active Control System for Boring Bar Vibration in Industry , 2007 .

[11]  José Fernández-Sáez,et al.  Optimization of passive vibration absorbers to reduce chatter in boring , 2013 .

[12]  Olivier Grunder,et al.  Multi-step ahead electricity price forecasting using a hybrid model based on two-layer decomposition technique and BP neural network optimized by firefly algorithm , 2017 .

[13]  J. Saffury,et al.  Optimized chatter resistance of viscoelastic turning bars , 2009 .

[14]  D. Lundin,et al.  Anti-vibration Engineering in Internal Turning Using a Carbon Nanocomposite Damping Coating Produced by PECVD Process , 2014, Journal of Materials Engineering and Performance.

[15]  M. Ringnér,et al.  Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks , 2001, Nature Medicine.

[16]  Ping Wang,et al.  Mechanical Property Prediction of Strip Model Based on PSO-BP Neural Network , 2008 .

[17]  Dai Gil Lee,et al.  Design and manufacture of a carbon fiber epoxy rotating boring bar , 2003 .

[18]  Penggao Wen,et al.  Fault Prediction of Elevator Door System Based on PSO-BP Neural Network , 2016 .

[19]  T. Schmitz,et al.  A tuned holder for increased boring bar dynamic stiffness , 2011 .

[20]  Abhishek Sharma,et al.  Artificial neural network-based prediction of performance and emission characteristics of CI engine using polanga as a biodiesel , 2016 .