Prediction and control of surface roughness for end milling process using ANFIS

In this paper, we applied an Adaptive Neuro-Fuzzy Inference System (ANFIS) approach for prediction of the workpiece surface roughness for the end milling process. A small number of fuzzy rules were used for building ANFIS models with the help of Subtractive clustering method (ANFIS-Subtractive clustering model). The predicted values are found to be in excellent agreement with the experimental data with average error values in the range of 3.47-3.49%. Also, we compared the proposed ANFIS models to other Artificial intelligence (AI) approaches. Results show that the proposed model has high accuracy in comparison to other AI approaches in literature. Therefore, we can use ANFIS model to predict the workpiece surface roughness for the end milling process.

[1]  Angelos P. Markopoulos,et al.  Artificial neural network models for the prediction of surface roughness in electrical discharge machining , 2008, J. Intell. Manuf..

[2]  Abdel Badie Sharkawy,et al.  Prediction of Surface Roughness in End Milling Process Using Intelligent Systems: A Comparative Study , 2011, Appl. Comput. Intell. Soft Comput..

[3]  Suresh Kumar Reddy Narala,et al.  Application of Artificial Neural Network and Response Surface Methodology in Modeling of Surface Roughness in WS2 Solid Lubricant Assisted MQL Turning of Inconel 718 , 2018 .

[4]  Stephen L. Chiu,et al.  Fuzzy Model Identification Based on Cluster Estimation , 1994, J. Intell. Fuzzy Syst..

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

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

[7]  Header Haddad,et al.  Optimization of the polymer concrete used for manufacturing bases for precision tool machines , 2012 .

[8]  Bor-Tsuen Lin,et al.  Adaptive network-based fuzzy inference system for prediction of surface roughness in end milling process using hybrid Taguchi-genetic learning algorithm , 2009, Expert Syst. Appl..

[9]  Ship-Peng Lo,et al.  An adaptive-network based fuzzy inference system for prediction of workpiece surface roughness in end milling , 2003 .

[10]  Mohammad Reza Soleymani Yazdi,et al.  Development of a dynamic surface roughness monitoring system based on artificial neural networks (ANN) in milling operation , 2015, The International Journal of Advanced Manufacturing Technology.

[11]  Ahmed A. D. Sarhan,et al.  Cutting force-based adaptive neuro-fuzzy approach for accurate surface roughness prediction in end milling operation for intelligent machining , 2015 .

[12]  Vladimir Pucovsky,et al.  Application of fuzzy logic and regression analysis for modeling surface roughness in face milliing , 2013, J. Intell. Manuf..

[13]  Ning Wang,et al.  Adaptive network-based fuzzy inference system with leave-one-out cross-validation approach for prediction of surface roughness , 2011 .

[14]  Uday S. Dixit,et al.  Application of soft computing techniques in machining performance prediction and optimization: a literature review , 2010 .

[15]  Somkiat Tangjitsitcharoen,et al.  Prediction of surface roughness in ball-end milling process by utilizing dynamic cutting force ratio , 2017, J. Intell. Manuf..