Flank Wear Modeling in High Speed Hard Turning by using artificial Neural Network and Regression Analysis

Al Hazza, Muataz Hazza Faizi and Adesta, Erry Yulian Triblas (2011) Flank Wear Modeling in High Speed Hard Turning by using artificial Neural Network and Regression Analysis. Advanced Materials Research , 264-5. pp. 1097-1101. ISSN 1022-6680

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Abstract

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 MATLAB 2009b. 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.

Item Type:	Article (Journal)
Additional Information:	5515/8439
Uncontrolled Keywords:	Flank wear, High speed, Hard turning, ANN, Regression analysis, Tool life
Subjects:	T Technology > TS Manufactures > TS200 Metal manufactures. Metalworking
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Engineering > Department of Manufacturing and Materials Engineering
Depositing User:	Prof. Dr. Erry Yulian Triblas Adesta
Date Deposited:	02 Dec 2011 16:39
Last Modified:	30 May 2013 13:02
URI:	http://irep.iium.edu.my/id/eprint/8439

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