Prediction of tool life in end milling of hardened steel AISI D2

Most published research works on the development of tool life model in machining of hardened steels have been mainly concerned with the turning process, whilst the milling process has received little attention due to the complexity of the process. Thus, the aim of present study is to develope a tool life model in end milling of hardened steel AISI D2 using PVD TiAIN coated carbide cutting tool. The hardness of AISI D2 tool lies within the range of 56-58 HRC. The independent variables or the primary machining parameters selected for this experiment were the cutting speed, feed, and depth of cut. First and second order models were developed using Response Surface Methodology (RSM). Experiments were conducted within specified ranges of the parameters. Design-Expert 6.0 software was used to develop the tool life equations as the predictive models. The predicted tool life results are presented in terms of both 1st and 2nd order equations with the aid of a statistical design of experiment software called Design-Expert version 6.0. Analysis of variance (ANOVA) has indicated that both models are valid in predicting the tool life of the part machined under specified condition and the prediction of average error is less than 10%.

[1]  Margaret J. Robertson,et al.  Design and Analysis of Experiments , 2006, Handbook of statistics.

[2]  Imtiaz Ahmed Choudhury,et al.  Wear mechanism of TiN coated carbide and uncoated cermets tools at high cutting speed applications , 2004 .

[3]  Ping Yi Chao,et al.  An improved neural network model for the prediction of cutting tool life , 1997, J. Intell. Manuf..

[4]  Aitzol Lamikiz,et al.  Improving the surface finish in high speed milling of stamping dies , 2002 .

[5]  A. Mansour,et al.  Surface roughness model for end milling: a semi-free cutting carbon casehardening steel (EN32) in dry condition , 2002 .

[6]  P. Clayton,et al.  High-speed five-axis milling of hardened tool steel , 2000 .

[7]  M. C. Shaw Metal Cutting Principles , 1960 .

[8]  XiaoQi Chen,et al.  An experimental study of tool wear and cutting force variation in the end milling of Inconel 718 with coated carbide inserts , 2006 .

[9]  Margaret A. Nemeth Response Surface Methodology: Process and Product Optimization Using Designed Experiments, 2nd Edition , 2003 .

[10]  I. S. Jawahir,et al.  Development of hybrid predictive models and optimization techniques for machining operations , 2007 .

[11]  Imtiaz Ahmed Choudhury,et al.  Performance of P10 TiN coated carbide tools when end milling AISI H13 tool steel at high cutting speed , 2004 .

[12]  S. Shanmugasundaram,et al.  Prediction of tool wear using regression and ANN models in end-milling operation , 2008 .

[13]  H. S. Shan,et al.  Failure of cemented carbide tools in intermittent cutting , 1979 .

[14]  S. F. Yu,et al.  A predicted modelling of tool life of high-speed milling for SKD61 tool steel , 2005 .

[15]  George-Christopher Vosniakos,et al.  Predicting surface roughness in machining: a review , 2003 .

[16]  J. A. Ortiz,et al.  Analysis of factors affecting the high-speed side milling of hardened die steels , 2005 .

[17]  K. C. Ee,et al.  Performance-Based Predictive Models and Optimization Methods for Turning Operations and Applications: Part 1—Tool Wear/Tool Life in Turning with Coated Grooved Tools , 2006 .