Optimizing Machining Parameters during Turning Process

This work presents an experimental investigation of the influence of the three most important machining parameters of depth of cut, feed rate and spindle speed on surface roughness during turning of mild steel. In this study, the design of experiment which is a powerful tool for experimental design is used to optimize the machining parameters for effective machining of the workpiece. Box Behnken experimental design method as well as analysis of variance (ANOVA) is used to analyze the influence of machining parameters on surface roughness height Ra . The individual parameters effect as well as effect of interactions between the machining parameters on the surface roughness height Ra is analyzed using various graphical representations. Using multiple linear regressions, mathematical models correlating the influence of machining parameters on the surface roughness Ra during the machining process were developed. Confirmation results were used to confirm that mathematical models are good enough to effectively represent machining criteria of surface roughness Ra during the study.

[1]  Gopal S. Upadhyaya,et al.  Material Science and Engineering , 2007 .

[2]  Noorul Haq,et al.  INFLUENCE OF MACHINING PARAMETERS ON SURFACE ROUGHNESS OF GFRP PIPES , 2009 .

[3]  Teuku Meurah Indra Mahlia,et al.  Effect of cutting parameters on the surface roughness of titanium alloys using end milling process , 2010 .

[4]  Mehdi Tajdari,et al.  Surface roughness modelling in hard turning operation of AISI 4140 using CBN cutting tool , 2010 .

[5]  H. Onozuka,et al.  Study on orthogonal turning of titanium alloys with different coolant supply strategies , 2009 .

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

[7]  J. L. C. Salles,et al.  Effects of Machining Parameters on Surface Quality of the Ultra High Molecular Weight Polyethylene (UHMWPE) , 2003 .

[8]  B. Ramamoorthy,et al.  Machinability investigation of Inconel 718 in high-speed turning , 2009 .

[9]  B. Sidda Reddy,et al.  Prediction of Surface Roughness in Turning Using Adaptive Neuro-Fuzzy Inference System , 2009 .

[10]  Tuğrul Özel,et al.  Predictive modeling of surface roughness and tool wear in hard turning using regression and neural networks , 2005 .

[11]  Ulaş Çaydaş,et al.  Modeling and analysis of electrode wear and white layer thickness in die-sinking EDM process through response surface methodology , 2008 .

[12]  A. Senthil Kumar,et al.  Machinability of bronze–alumina composite with tungsten carbide cutting tool insert , 2008 .

[13]  Anirban Bhattacharya,et al.  Estimating the effect of cutting parameters on surface finish and power consumption during high speed machining of AISI 1045 steel using Taguchi design and ANOVA , 2009, Prod. Eng..