Prediction of cutting temperatures by using back propagation neural network modeling when cutting hardened H-13 steel in CNC end milling

Al Hazza, Muataz Hazza Faizi and Adesta, Erry Yulian Triblas and Suprianto, M.Y and Riza, Muhammad (2012) Prediction of cutting temperatures by using back propagation neural network modeling when cutting hardened H-13 steel in CNC end milling. Advanced Materials Research, 576. pp. 91-94. ISSN 1022-6680

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Abstract

Machining of hardened steel at high cutting speeds produces high temperatures in the cutting zone, which affects the surface quality and cutting tool life. Thus, predicting the temperature in early stage becomes utmost importance. This research presents a neural network model for predicting the cutting temperature in the CNC end milling process. The Artificial Neural Network (ANN) was applied as an effective tool for modeling and predicting the cutting temperature. A set of sparse experimental data for finish end milling on AISI H13 at hardness of 48 HRC have been conducted to measure the cutting temperature. The artificial neural network (ANN) was applied to predict the cutting temperature. Twenty hidden layer has been used with feed forward back propagation hierarchical neural networks were designed with Matlab2009b Neural Network Toolbox. The results show a high correlation between the predicted and the observed temperature which indicates the validity of the models.

Item Type:	Article (Journal)
Additional Information:	6852/30262
Uncontrolled Keywords:	end milling, ANN, temperature, AISI H13. End milling
Subjects:	T Technology > T Technology (General)
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Engineering > Department of Manufacturing and Materials Engineering
Depositing User:	Dr Muataz Hazza Alhazza
Date Deposited:	20 Sep 2013 10:47
Last Modified:	20 Sep 2013 10:49
URI:	http://irep.iium.edu.my/id/eprint/30262

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