Flank Wear Modeling in High Speed Hard Turning by Using Artificial Neural Network and Regression Analysis

Abstract:

Article Preview

Predicting and modeling flank wear length in high speed hard turning by using ceramic cutting tools with negative rake angle was conducted using two different techniques. Regression model is developed by using design of expert 7.1.6 and neural network technique model was built by using matlab2009b. A set of experimental data for high speed hard turning of hardened AISI 4340 steel was obtained with different cutting speeds, feed rate and negative rake angle. Flank wear length was measured to train the neural network models and to develop mathematical model by using regression analysis. Predictive neural network models are found to be capable of better predictions tool flank wear within the range that they had been trained.

Info:

Periodical:

Advanced Materials Research (Volumes 264-265)

Edited by:

M.S.J. Hashmi, S. Mridha and S. Naher

Pages:

1097-1101

DOI:

10.4028/www.scientific.net/AMR.264-265.1097

Citation:

M. H.F. Al Hazza and E. Y. T. Adesta, "Flank Wear Modeling in High Speed Hard Turning by Using Artificial Neural Network and Regression Analysis", Advanced Materials Research, Vols. 264-265, pp. 1097-1101, 2011

Online since:

June 2011

Export:

Price:

$35.00

In order to see related information, you need to Login.

In order to see related information, you need to Login.