caa a22 4500 606158987 CHVBK 20210128100622.0 cr unu---uuuuu 210128e20150101xx s 000 0 eng 10.1007/s00521-014-1696-8 doi (NATIONALLICENCE)springer-10.1007/s00521-014-1696-8 Surface roughness prediction using Taguchi-fuzzy logic-neural network analysis for CNT nanofluids based grinding process [Elektronische Daten] [S. Prabhu, M. Uma, B. Vinayagam] The present study highlights the Taguchi design of experiment techniques proved to be an efficient tool for the design of neural networks' surface roughness to predict in the grinding process, where CNT mixed nanofluids are used as dielectric for machining AISI D3 Tool steel material. Empirical model for the prediction of output parameters has been developed using regression analysis and the results are compared for with and without using CNT nanofluids. Analysis of variance and F test is used to determine the significant parameter affecting the surface roughness which is the crucial parameter for any grinding process. Feedforward artificial neural networks are used to train the experimental values with the Levenberg-Marquardt algorithm; the most influencing factors are determined. The predicted surface roughness for without using CNT based cutting fluid is 11.3% and with CNT is 10.37%. Further, a fuzzy logic system is used to investigate the relationship between the machining process parameters' accuracy and to determining the efficiency of each parameter design with Taguchi design of experiments. The Natural Computing Applications Forum, 2014 Multiwall carbon nanotube nationallicence Grinding nationallicence Regression analysis nationallicence Fuzzy logic analysis nationallicence Neural network analysis nationallicence Prabhu S. School of Mechanical Engineering, SRM University, 603 203, Chennai, Tamilnadu, India aut Uma M. Department of Software Engineering, SRM University, 603 203, Chennai, Tamilnadu, India aut Vinayagam B. Department of Mechatronics, SRM University, 603 203, Chennai, Tamilnadu, India aut Neural Computing and Applications Springer London 26/1(2015-01-01), 41-55 0941-0643 26:1<41 2015 26 521 https://doi.org/10.1007/s00521-014-1696-8 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00521-014-1696-8 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Prabhu S. School of Mechanical Engineering, SRM University, 603 203, Chennai, Tamilnadu, India aut NATIONALLICENCE 700 1- Uma M. Department of Software Engineering, SRM University, 603 203, Chennai, Tamilnadu, India aut NATIONALLICENCE 700 1- Vinayagam B. Department of Mechatronics, SRM University, 603 203, Chennai, Tamilnadu, India aut NATIONALLICENCE 773 0- Neural Computing and Applications Springer London 26/1(2015-01-01), 41-55 0941-0643 26:1<41 2015 26 521