A comparison of dry and air-cooled turning of grey cast iron with mixed oxide ceramic tool

Abstract The present work compares the performance of a mixed oxide ceramic tool in dry and air-cooled turning of grey cast iron. First, the study was done in the range of process parameters where dry turning provided satisfactory performance. The contours of surface roughness and tool life were generated with the help of trained neural networks. A novel procedure of neural network training is used in this work. The study was extended to the range in which dry turning performed poorly in terms of tool life. Tool wear, surface roughness of the machined job and forces and vibration during the cutting were studied. It was observed that air-cooling significantly reduces the tool wear at high cutting speed. At higher cutting speeds, where the dry turning performs very poorly, the air-cooled turning provides an improved surface finish also apart from the reduction in tool wear. In all the cases, the cutting and feed forces get reduced in air-cooling. Thus, air-cooled turning of grey cast iron with mixed oxide ceramic tools offers a promising environment-friendly option.

[1]  D. K. Ojha,et al.  An economic and reliable tool life estimation procedure for turning , 2005 .

[2]  E. L. Hines,et al.  Tool-wear prediction using artificial neural networks , 1995 .

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

[4]  Uday S. Dixit,et al.  A neural-network-based methodology for the prediction of surface roughness in a turning process , 2005 .

[5]  S. K. Bhattacharyya,et al.  The performance of ceramic tool materials for the machining of cast iron , 1989 .

[6]  Liang Li,et al.  High speed cutting of Inconel 718 with coated carbide and ceramic inserts , 2002 .

[7]  P. Sreejith,et al.  Dry machining: Machining of the future , 2000 .

[8]  A. S. Varadarajan,et al.  Investigations on hard turning with minimal cutting fluid application (HTMF) and its comparison with dry and wet turning , 2002 .

[9]  Wisley Falco Sales,et al.  Machining of nickel-base, Inconel 718, alloy with ceramic tools under finishing conditions with various coolant supply pressures , 2005 .

[10]  Deng Jianxin,et al.  Self-lubricating behaviors of Al2O3/TiB2 ceramic tools in dry high-speed machining of hardened steel , 2005 .

[11]  A. Senthil Kumar,et al.  Machinability of hardened steel using alumina based ceramic cutting tools , 2003 .

[12]  V. Astakhov.  Metal Cutting Mechanics , 1998 .

[13]  E. Ezugwu,et al.  Surface abuse when machining cast iron (G-17) and nickel-base superalloy (Inconel 718) with ceramic tools , 1995 .

[14]  Snehasis Mukhopadhyay,et al.  Selecting an artificial neural network for efficient modeling and accurate simulation of the milling process , 2002 .

[15]  Richard E. DeVor,et al.  A worn tool force model for three-dimensional cutting operations , 2000 .

[16]  D. Bhattacharyya,et al.  Multiple regression and neural networks analyses in composites machining , 2003 .

[17]  A. Ghani,et al.  Study of tool life, surface roughness and vibration in machining nodular cast iron with ceramic tool , 2002 .

[18]  A. Abrão,et al.  Turning of hardened 100Cr6 bearing steel with ceramic and PCBN cutting tools , 2003 .

[19]  D. K. Ojha,et al.  The application of a radial basis function neural network for predicting the surface roughness in a turning process , 2006 .

[20]  Necip Camuşcu,et al.  Effect of cutting speed on the performance of Al2O3 based ceramic tools in turning nodular cast iron , 2006 .