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

Muataz H.F. Al Hazza; Erry Yulian Triblas Adesta

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

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 264-265Flank Wear Modeling in High Speed Hard Turning by...

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

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 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.

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Periodical:

Advanced Materials Research (Volumes 264-265)

Pages:

1097-1101

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.264-265.1097

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Online since:

June 2011

Authors:

Muataz H.F. Al Hazza, Erry Yulian Triblas Adesta

Keywords:

Artificial Neural Network (ANN), Flank Wear, Hard Turning, High-Speed, Regression Analysis, Tool Life

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