MATEC Web Conf.
Volume 178, 201822nd International Conference on Innovative Manufacturing Engineering and Energy - IManE&E 2018
|Number of page(s)||6|
|Section||Advanced Machining and Surface Engineering|
|Published online||24 July 2018|
Optimizing ANN performance using DOE: application on turning of a titanium alloy
Department of Mechanical Engineering, Technological Educational Institute of Thessaly, GR 41110, Larissa, Greece
2 Department of Civil Engineering Educators, School of Pedagogical and Technological Education, GR 14121, N. Heraklion Attikis, Greece
3 Department of Mechanical Engineering Educators, School of Pedagogical and Technological Education, GR 14121, N. Heraklion Attikis, Greece
* Corresponding author: firstname.lastname@example.org
A methodology is presented to optimize the performance of an Artificial Neural Network (ANN) using Design of Experiments (DOE). 8 different feed forward back propagation (FFBP) ANNs were developed and tested according to the L8 full factorial orthogonal array. The 3 parameters tested were: Number of Hidden Neurons, Learning rate, and Momentum; each one having two levels. By utilizing the analysis of means (ANOM) and the analysis of variances (ANOVA), the optimum levels of ANN parameters were determined. The developed ANN was applied for predicting cutting forces and average surface roughness in turning Ti-6Al-4V alloy.
© The Authors, published by EDP Sciences, 2018.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (http://creativecommons.org/licenses/by/4.0/).
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