The application of ANFIS prediction models for thermal error compensation on CNC machine tools

This paper first reviews different methods of designing thermal error models, before concentrating on employing ANFIS models.The GM(1, N) model and fuzzy c-means clustering are used for variable selection, which is capable of simplifying the system prediction model.The results of the study show that the ANFIS-FCM model is superior in terms of the accuracy of its predictive ability with the benefit of fewer rules. Thermal errors can have significant effects on CNC machine tool accuracy. The errors come from thermal deformations of the machine elements caused by heat sources within the machine structure or from ambient temperature change. The effect of temperature can be reduced by error avoidance or numerical compensation. The performance of a thermal error compensation system essentially depends upon the accuracy and robustness of the thermal error model and its input measurements. This paper first reviews different methods of designing thermal error models, before concentrating on employing an adaptive neuro fuzzy inference system (ANFIS) to design two thermal prediction models: ANFIS by dividing the data space into rectangular sub-spaces (ANFIS-Grid model) and ANFIS by using the fuzzy c-means clustering method (ANFIS-FCM model). Grey system theory is used to obtain the influence ranking of all possible temperature sensors on the thermal response of the machine structure. All the influence weightings of the thermal sensors are clustered into groups using the fuzzy c-means (FCM) clustering method, the groups then being further reduced by correlation analysis.A study of a small CNC milling machine is used to provide training data for the proposed models and then to provide independent testing data sets. The results of the study show that the ANFIS-FCM model is superior in terms of the accuracy of its predictive ability with the benefit of fewer rules. The residual value of the proposed model is smaller than ?4µm. This combined methodology can provide improved accuracy and robustness of a thermal error compensation system.

[1]  Che Hassan Che Haron,et al.  Online tool wear prediction system in the turning process using an adaptive neuro-fuzzy inference system , 2013, Appl. Soft Comput..

[2]  Yi Lin,et al.  Grey Systems: Theory and Applications , 2010 .

[3]  James B. Bryan,et al.  International Status of Thermal Error Research (1990) , 1990 .

[4]  Oscar Castillo,et al.  Time series prediction using ensembles of ANFIS models with genetic optimization of interval type-2 and type-1 fuzzy integrators , 2014, Int. J. Hybrid Intell. Syst..

[5]  S. M. N. Arosha Senanayake,et al.  Article in Press G Model Applied Soft Computing a Knowledge-based Intelligent Framework for Anterior Cruciate Ligament Rehabilitation Monitoring , 2022 .

[6]  J. C. Dunn,et al.  A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters , 1973 .

[7]  Oscar Castillo,et al.  A new approach for time series prediction using ensembles of ANFIS models , 2012, Expert Syst. Appl..

[8]  Anil K. Jain.  Data clustering: 50 years beyond K-means , 2008, Pattern Recognit. Lett..

[9]  Derek G. Ford,et al.  The use of thermal imaging, temperature and distortion models for machine tool thermal error reduction , 1998 .

[10]  Ali M. Abdulshahed,et al.  Application of GNNMCI(1, N) to environmental thermal error modelling of CNC machine tools , 2013 .

[11]  KarahocaAdem,et al.  GSM churn management by using fuzzy c-means clustering and adaptive neuro fuzzy inference system , 2011 .

[12]  Adem Karahoca,et al.  GSM churn management by using fuzzy c-means clustering and adaptive neuro fuzzy inference system , 2011, Expert Syst. Appl..

[13]  Oscar Castillo,et al.  A new approach for time series prediction using ensembles of ANFIS models with interval type-2 and type-1 fuzzy integrators , 2013, 2013 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr).

[14]  Samarjit Kar,et al.  Applications of neuro fuzzy systems: A brief review and future outline , 2014, Appl. Soft Comput..

[15]  Serge Guillaume,et al.  Designing fuzzy inference systems from data: An interpretability-oriented review , 2001, IEEE Trans. Fuzzy Syst..

[16]  T. Velmurugan,et al.  Performance based analysis between k-Means and Fuzzy C-Means clustering algorithms for connection oriented telecommunication data , 2014, Appl. Soft Comput..

[17]  Kee-Joe Lim,et al.  Comparison of recognition rates between BP and ANFIS with FCM clustering method on off-line PD diagnosis of defect models of traction motor stator coil , 2005, Proceedings of 2005 International Symposium on Electrical Insulating Materials, 2005. (ISEIM 2005)..

[18]  James C. Bezdek,et al.  Pattern Recognition with Fuzzy Objective Function Algorithms , 1981, Advanced Applications in Pattern Recognition.

[19]  Aun-Neow Poo,et al.  Error compensation in machine tools — a review: Part II: thermal errors , 2000 .

[20]  Jian Han,et al.  Thermal error modeling of machine tool based on fuzzy c-means cluster analysis and minimal-resource allocating networks , 2012 .

[21]  Qingzhong Liu,et al.  Predicting injection profiles using ANFIS , 2007, Inf. Sci..

[22]  Kun-Chieh Wang.  Thermal Error Modeling of a Machining Center using Grey System Theory and Adaptive Network-Based Fuzzy Inference System , 2006, 2006 IEEE Conference on Cybernetics and Intelligent Systems.

[23]  Christian Brecher,et al.  Thermal issues in machine tools , 2012 .

[24]  Jian Han,et al.  A new thermal error modeling method for CNC machine tools , 2012 .

[25]  Wen-Jun Zhang,et al.  Thermal-error modeling for complex physical systems: the-state-of-arts review , 2009 .

[26]  Bin Li,et al.  A thermal error model for large machine tools that considers environmental thermal hysteresis effects , 2014 .

[27]  J. G. Yang,et al.  Application of synthetic grey correlation theory on thermal point optimization for machine tool thermal error compensation , 2009 .

[28]  Ali M. Abdulshahed,et al.  Comparative study of ANN and ANFIS prediction models for thermal error compensation on CNC machine tools , 2013 .

[29]  Jun Ni,et al.  Thermal error modelling for real-time error compensation , 1996 .

[30]  Andrew P. Longstaff,et al.  Efficient estimation by FEA of machine tool distortion due to environmental temperature perturbations , 2013 .

[31]  Melih Inal,et al.  Comparison of neural network application for fuzzy and ANFIS approaches for multi-criteria decision making problems , 2014, Appl. Soft Comput..

[32]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[33]  Deng Ju-Long,et al.  Control problems of grey systems , 1982 .

[34]  Seung-Han Yang,et al.  Thermal Error Modeling of a Horizontal Machining Center Using the Fuzzy Logic Strategy , 2000 .