Abstract
This paper presents an approach to predict dimensional errors in end milling by using a hybrid radial basis function (RBF) neural network. First, the results of end milling experiments are discussed and the effects of the cutting parameters on dimensional errors of machined surfaces are analyzed. The results showed the dimensional errors are affected by the spindle speed, the feed rate, the radial and axial depths of cut. Then, a hybrid RBF neural network is applied. This neural network combines regression tree and an RBF neural network to rapidly determine the center values and its number, and the radial values of the radial basis function. Finally, the prediction models of dimensional errors are established by using the RBF neural network, the ANFIS (adaptive-network-based fuzzy inference system), and the hybrid RBF neural network for end milling. Compared with the predicted results of the above three models, the performance of the hybrid RBF neural network-based method is shown to be the best.
Keywords
- Feed Rate
- Radial Basis Function
- Fuzzy Inference System
- Spindle Speed
- Radial Basis Function Neural Network
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Kline, W.A., DeVor, R.E., Lindberg, J.R.: The Prediction of Cutting Forces in End Milling with Application to Cornering Cuts. Int. J. Mach Tool Des. Res. 22, 7–22 (1982)
Budak, E., Altintas, Y.: Peripheral Milling Conditions for Improved Dimensional Accuracy. Int. J. Mach. Tools Manufact. 34, 907–918 (1994)
Matsubara, T., Yamamoto, H., Mizumoto, H.: Study on Accuracy in End Mill Operations. Bull. Japan Soc. Prec. Eng. 21, 95–100 (1987)
Byrne, G., Dornfled, D., Inasaki, I., Ketteler, G., Konig, W., Teti, R.: Tool Condition Monitoring (TCM) - The Statue of Research and Industrial Application. Annals of the CIRP 44, 541–567 (1995)
Hanna, M.M., Buck, A., Smith, R.: Fuzzy Petri Nets with Neural Networks to Model Products Quality from a CNC-milling Machining Centre. IEEE T. Syst. Man. Cy. A 26, 638–645 (1996)
Cho, S., Cho, Y., Yoon, S.: Reliable Roll Force Prediction in Cold Mill Using Multiple Neural Networks. IEEE T. Neural Networks 8, 874–882 (1997)
Li, X., Djordjevich, A., Patri, K.V.: Current Sensor-based Feed Cutting Force Intelligent Estimation and Tool Wear Condition Monitoring. IEEE T. Ind. Electron. 47, 697–702 (2000)
Orr, M.J.L.: Regularisation in the Selection of RBF Centres. Neural Comput. 7, 606–623 (1995)
Chen, S., Cowan, C.F.N., Grant, P.M.: Orthogonal Least Squares Learning Algorithm for Radial Basis Function Networks. IEEE T. Neural Networks 2, 302–309 (1991)
Cheng, Y.H., Lin, C.S.: A Learning Algorithm for Radial Basis Function Networks: With the Capability of Adding and Pruning Neurons. Proc. IEEE, 797–801 (1994)
Poggio, T., Girosi, F.: Regularization Algorithms for Learning that are Equivalent to Multilayer Networks. Science 247, 987–982 (1990)
Haykin, S.: Neural Networks: A Comprehensive Foundation. Maxmillan, New York (1994)
Lowe, D.: Adaptive Radial Basis Function Nonlinearities and the Problem of Generalization. In: 1th Int. Conf. Artificial Neural Networks, London, UK, pp. 171–175 (1989)
Kubat, M.: Decision Trees Can Initialize Radial-Basis Function Networks. IEEE T. Neural Networks 9, 813–824 (1998)
Orr, M.J.L.: Recent Advances in Radial Basis Function Networks. Technical Report, Institute for Adaptive and Neural computation, Edinburgh University (1999)
Jang, J.S.R.: ANFIS: Adaptive-Network-Based Fuzzy Inference System. IEEE Transactions on Systems, Man and Cybernetics 23, 665–685 (1993)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Li, X., Guan, X., Li, Y. (2004). A Hybrid Radial Basis Function Neural Network for Dimensional Error Prediction in End Milling. In: Yin, FL., Wang, J., Guo, C. (eds) Advances in Neural Networks - ISNN 2004. ISNN 2004. Lecture Notes in Computer Science, vol 3174. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28648-6_119
Download citation
DOI: https://doi.org/10.1007/978-3-540-28648-6_119
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22843-1
Online ISBN: 978-3-540-28648-6
eBook Packages: Springer Book Archive