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

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....

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Main Authors: Al Hazza, Muataz Hazza Faizi, Adesta, Erry Yulian Triblas
Format: Article
Language:English
Published: Trans Tech Publications 2011
Subjects:
Online Access:http://irep.iium.edu.my/8439/1/AMR.264-265.1097.pdf
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author Al Hazza, Muataz Hazza Faizi
Adesta, Erry Yulian Triblas
author_facet Al Hazza, Muataz Hazza Faizi
Adesta, Erry Yulian Triblas
author_sort Al Hazza, Muataz Hazza Faizi
collection IIUM
description 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.
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spelling oai:generic.eprints.org:84392013-05-30T05:02:24Z http://irep.iium.edu.my/8439/ Flank Wear Modeling in High Speed Hard Turning by using artificial Neural Network and Regression Analysis Al Hazza, Muataz Hazza Faizi Adesta, Erry Yulian Triblas TS200 Metal manufactures. Metalworking 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. Trans Tech Publications 2011-06-30 Article PeerReviewed application/pdf en http://irep.iium.edu.my/8439/1/AMR.264-265.1097.pdf 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 http://www.ttp.net/1022-6680.html DOI:10.4028/www.scientific.net/AMR.264-265.1097
spellingShingle TS200 Metal manufactures. Metalworking
Al Hazza, Muataz Hazza Faizi
Adesta, Erry Yulian Triblas
Flank Wear Modeling in High Speed Hard Turning by using artificial Neural Network and Regression Analysis
title Flank Wear Modeling in High Speed Hard Turning by using artificial Neural Network and Regression Analysis
title_full Flank Wear Modeling in High Speed Hard Turning by using artificial Neural Network and Regression Analysis
title_fullStr Flank Wear Modeling in High Speed Hard Turning by using artificial Neural Network and Regression Analysis
title_full_unstemmed Flank Wear Modeling in High Speed Hard Turning by using artificial Neural Network and Regression Analysis
title_short Flank Wear Modeling in High Speed Hard Turning by using artificial Neural Network and Regression Analysis
title_sort flank wear modeling in high speed hard turning by using artificial neural network and regression analysis
topic TS200 Metal manufactures. Metalworking
url http://irep.iium.edu.my/8439/1/AMR.264-265.1097.pdf
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