Experimental Investigation of Surface Roughness and Power Consumption in Turning Operation of EN 31 Alloy Steel

[1]  W. Konig,et al.  Machining hard materials with geometrically defined cutting edges , 1990 .

[2]  Matthew A. Davies,et al.  On Chip Morphology, Tool Wear and Cutting Mechanics in Finish Hard Turning , 1996 .

[3]  Y. S. Tarng,et al.  Design optimization of cutting parameters for turning operations based on the Taguchi method , 1998 .

[4]  George-Christopher Vosniakos,et al.  Prediction of surface roughness in CNC face milling using neural networks and Taguchi's design of experiments , 2002 .

[5]  Fu Gang Yan,et al.  Experimental study on hard turning hardened GCr15 steel with PCBN tool , 2002 .

[6]  S. G. Deshmukh,et al.  A genetic algorithmic approach for optimization of surface roughness prediction model , 2002 .

[7]  J. Davim Design of optimisation of cutting parameters for turning metal matrix composites based on the orthogonal arrays , 2003 .

[8]  Y. K. Chou,et al.  Tool nose radius effects on finish hard turning , 2004 .

[9]  Hari Singh,et al.  Tool wear optimization in turning operation by Taguchi method , 2004 .

[10]  Sulaiman Hasan,et al.  Analyses of surface roughness by turning process using Taguchi method , 2007 .

[11]  Muammer Nalbant,et al.  Application of Taguchi method in the optimization of cutting parameters for surface roughness in turning , 2007 .

[12]  Hari Singh,et al.  Optimizing power consumption for CNC turned parts using response surface methodology and Taguchi's technique—A comparative analysis , 2008 .

[13]  L. B. Abhang,et al.  Power Prediction Model for Turning EN-31 Steel Using Response Surface Methodology , 2010 .

[14]  V. Pandurangadu,et al.  Cutting power prediction model for turning of GFRP composites using response surface methodology , 2012 .