A Neural Network and Fuzzy-Based Methodology for the Prediction of Work Roll Surface Roughness in a Grinding Process

REFERENCES

• Sakakura , M. and Inasaki , I. 1992 . A Neural Network Approach to the Decision-Making Process for Grinding Operations . Annals of the CIRP , vol. 41 ( 1 ) : 353 – 356 .
   Google Scholar
• Warren Liao , T. and Chen , L. J. 1994 . A Neural Network Approach for Grinding Process: Modelling and Optimization . Int. Jnl. Machine Tool Manufacturing , vol. 34 ( 7 ) : 919 – 937 .
   Web of Science ®Google Scholar
• Inasaki , I. Sensor Fusion for Monitoring and Controlling Grinding Processes . Int. Jnl. of Adv. Manufacturing Technology , vol. 15 730 – 736 .
   Web of Science ®Google Scholar
• Sukthomya , W. and Tannock , J. 2005 . The Training of Neural Networks to Model Manufacturing Process . Int. Jnl of Intelligent Manufacturing , vol. 16 ( 1 ) : 39 – 51 .
   Web of Science ®Google Scholar
• Zhou , X. and Xi , F. 2002 . Modeling and Predicting Surface Roughness of the Grinding Process . Int. Jnl. of Machine Tools & Manufacture , vol. 42 ( 8 ) : 969 – 977 .
   Web of Science ®Google Scholar
• Hecker , R. L. and Liang , S. Y. 2003 . Predictive Modeling of Surface Roughness in Grinding . Int. Jnl. of Machine Tools and Manufacture , vol. 43 ( 8 ) : 755 – 761 .
   Web of Science ®Google Scholar
• Samhouri , M. and Surgenor , B. 2005 . Surface Roughness in Grinding: Offline Identification with an Adaptive Neuro-fuzzy Inference System Int. Conference NAMRAC-33
   Google Scholar
• Nandi , A. K. and Banerjee , M. K. 2005 . FBF-NN Based Modeling of Cylindrical Plunge Grinding Process using Genetic Algorithm . Int. Jnl. of Materials Processing Technology , vol. 162–163 : 655 – 664 .
   Google Scholar
• Kohli , A. and Dixit , U. S. 2005 . A Neural Network Based Methodology for the Prediction of Surface Roughness in a Turning Process . Int. Jnl. of Advanced Manufacturing Technology , vol. 25 ( 15 ) : 118 – 129 .
   Google Scholar
• Dixit , U. S. and Chandra , S. 2003 . A Neural Network Based Methodology for the Prediction of Roll Force and Roll Torque in Fuzzy Form for Cold Rolling Process . Int Jnl. Adv. Manufacturing Technology , vol. 22 ( 11–12 ) : 883 – 889 .
   Web of Science ®Google Scholar
• Hsiao , F.-H. , Chiang , W.-L. and Chen , C.-W. 2005 . Fuzzy Control for Nonlinear Systems via Neural-Network-Based Approach . International Journal for Computational Methods in Engineering Science and Mechanics , vol. 6 ( 3 ) : 145 – 152 .
   Google Scholar
• Tsoukalas , L. H. and Uhrig , R. E. 1997 . Fuzzy and Neural Approach in Engineering , New York : Wiley .
   Google Scholar
• Montgomery , D. C. 1991 . Design and Analysis of Experiments , New York : John Wiley & Sons .
   Google Scholar
• Ishibuchi , H. and Tanaka , H. Regression Analysis with Interval Model by Neural Networks . Proc. of the IEEE Int. Joint Conference on Neural Networks . Singapore . pp. 1594 – 1599 .
   Google Scholar