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General Information
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2012 Vol.2(6): 825-830 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2012.V2.246

Artificial Neural Network (ANN) Approach for Predicting Friction Coefficient of Roller Burnishing AL6061

S. H. Tang, N. Hakim, W. Khaksar, S. Sulaiman, M. K. A. Ariffin, and R. Samin

Abstract—Artificial Neural Network (ANN) approach is a fascinating mathematical tool, which can be used to simulate a wide variety of complex scientific and engineering problems. Due to its highly reliable prediction quality, the usage of it is growing rigorously and had already become an ultimate tool for various applications in the field of engineering. In this study an ANN technique was used to predict friction coefficient of roller burnishing AL6061 for two orientations which is parallel burnishing orientation (PB) and cross burnishing orientation (CB). The input parameters were defined by widths of roller curvature (7.5mm, 8mm and 8.5mm), burnishing speeds (110rpm, 230rpm, 330rpm and 490rpm), and burnishing forces (155.06N, 197.45N, 239.83N and 282.22N) while the output parameter was friction coefficient. 173 data was used for training the ANN and another 115 data was used to test the ANN. 60 different configurations of ANN was trained by using 6 different training algorithms. It was found that feed-forward back-propagation network with 15 neurons in hidden layer that was trained by Levenberg-Marquardt training algorithm gave the best result when compared to other training algorithms used. From the results it was found that the training performance and prediction performance was 0.000809 and 0.710 respectively. From this study, it became obvious that the selected ANN with the configuration and training algorithm proved to be the most suitable among the other ANN investigated for similar applications.

Index Terms—Friction coefficient; neural network; roller burnishing AL6061.

S. H. Tang, W. Khaksar, S. Sulaiman, M. K. A. Ariffin , and R. Samin are with the Department of Mechanical and Manufacturing Engineering, Faculty of Engineering, Universiti Putra Malaysia, Selangor, Malaysia (email:saihong@eng.upm.edu.my,email:wkhie@yahoo.com,email:suddin@ eng.upm.edu.my, email:khairol@eng.upm.edu.my)
N. Hakim is with a Malaysian government ministry (e-mail: hakim717@yahoo.com).


Cite: S. H. Tang, N. Hakim, W. Khaksar, S. Sulaiman, M. K. A. Ariffin, and R. Samin, "Artificial Neural Network (ANN) Approach for Predicting Friction Coefficient of Roller Burnishing AL6061," International Journal of Machine Learning and Computing vol. 2, no. 6, pp. 825-830, 2012.

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