Application and Study of Fuzzy Neural Network Theory Based on Takagi-Sugeno Model to Thermal Error Modeling on NC Machine Tool

Abstract:

For the degree of thermal deformation nonlinear is high and difficult to predict, fuzzy neural network modeling (FNN) based on Takagi-Sugeno model was applied to the NC machine tool thermal error modeling thus the complete thermal error fuzzy neural network mathematical model on NC machine tool was established and network parameters initialization and learning method were discussed. Thermal error experiment was conducted on large NC gantry rail grinder spindle box system and two independent groups of spindle thermal error data were collected, one was used to establish thermal error fuzzy neural network prediction model and another one was used to verify the prediction accuracy of this model. The test results show that fuzzy neural network model has high prediction accuracy.

• Periodical: Advanced Materials Research (Volume 744)
• Pages: 147-152
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.744.147
• Online since: August 2013
• Authors: Zi Jian Liu, Zhi Min Yu, Si Ming Li, Yan Di Ai
• Keywords: Fuzzy Neural Network (FNN), Fuzzy Segmentation, Membership Function, Takagi-Sugeno Fuzzy Inference, Thermal Error Prediction Model

References

[1] Ni Jun. The review and prospect of the research of error compensation of CNC machine tool [J]. China Mechanical Engineering, 1997, 8(1) : 29- 33.

[2] Cao S G, Rees N W, Feng G. Stability Analysis of Fuzzy Control Systems[J], Transactions on Fuzzy Control, 1996, 26(1): 2001-204.

[3] Hao Ying. Sufficient conditions on uniform approximation of multivariate functions by general Takagi-Sugeno fuzzy inference systems with linear rule consequent[J]. IEEE Transactions on System, Man, And Cybernetics, Part A: Systems and Humans, 1998, 28(4): 515-520.

DOI: 10.1109/3468.686713

[4] Huang Jinjie, LI Shiyong. A T-S Type of Rough Fuzzy Control System and its Implementation[A]. Proceedings of the 9th Intennational Conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing[C]. Chongqing: May, 2003. 325-328.

DOI: 10.1007/3-540-39205-x_49

[5] Dong Jingrong, Yang Xutai. Predicting of the economic development for the circumjacent reservoir region of the Longtan hydropower project using fuzzy neural network[J]. Journal of Forecasting, 1998, 17(4): 54-58.