Synthetically Modeling of Thermal Error and Grinding Force Induced Error on a Precision NC Cylindrical Grinding Machine


Article Preview

Thermal errors and force-induced errors are two most significant sources of the NC grinding machine inaccuracy. And error compensation technique is an effective way to improve the manufacturing accuracy of the NC machine tools. Effective compensation relies on an accurate error model that can predict the errors exactly during machining. In this paper, a PSO–BP neural network modeling technique has been developed to build the model of the dynamic and highly nonlinear thermal errors and grinding force induced errors. The PSO–BP neural network modeling technique not only enhances the prediction accuracy of the model but also reduces the training time of the neural networks. The radial error of a grinding machine has been reduced from 27 to 8μmafter compensating its thermal error and force-induced error in this paper.



Advanced Materials Research (Volumes 24-25)

Edited by:

Hang Gao, Zhuji Jin and Yannian Rui




H. Wu et al., "Synthetically Modeling of Thermal Error and Grinding Force Induced Error on a Precision NC Cylindrical Grinding Machine", Advanced Materials Research, Vols. 24-25, pp. 243-248, 2007

Online since:

September 2007




[1] M. Weck, P. Mckeown, R. Bonse: Reduction and compensation of thermal error in machine tools. Annals of the CIRP, Vol. 44 (1995), pp.589-598.

DOI: 10.1016/s0007-8506(07)60506-x

[2] R. Eberhart, J. Kennedy: A New Optimizer Using Particle Swarm Theory. In: Proc of the Sixth International Symposium on Micro Machine and Human Science. Piscataway, NJ: IEEE Service Center (1995), pp.39-43.

DOI: 10.1109/mhs.1995.494215

[3] J. Kennedy, R. Eberhart: Particle Swarm Optimization. IEEE International Conference on Neural Networks, Perth, Australia (1995), p.1942-(1948).

[4] F.A. Guerra, L.S. Coelho: Radial Basis Neural Network Learning Based on Particle Swarm Optimization to Multistep Prediction of Chaotic Lorenz's System, IEEE Proceedings of the Fifth International Conference on Hybrid Intelligent Systems (HIS'05). (2005).

DOI: 10.1109/ichis.2005.91

[5] M. Sugisaka, X.J. Fan: An Effective Search Method for NN-Based Face Detection Using PSO. SICE Annual Conference in Sapporo (2004), pp.2742-2745.

[6] Y. Shi, R. Eberhart: Parameter Selection In Particle Swarm Optimization. Proceeding of the 7th Annual Conference on Evolutionary Programming, NY (1998), pp.591-600.

DOI: 10.1007/bfb0040810

[7] J.G. Yang, J.X. Yuan, J. Ni: Thermal error mode analysis and robust modeling for error compensation on a CNC turning center. International Journal of Machine Tools & Manufacture, Vol. 39 (1999), pp.1367-1381.

DOI: 10.1016/s0890-6955(99)00008-5

[8] Hecht-Nielsen, Robert: Kolmogorov's Mapping Neural Network Existence Theorem. IEEE First International Conference on Neural Networks, vol. 3, (1987), pp.11-14.

Fetching data from Crossref.
This may take some time to load.