Table of Contents
Journal of Metallurgy
Volume 2014, Article ID 813234, 11 pages
Research Article

Artificial Neural Networks Investigation of Indentation Force Effects on Nano- and Microhardness of Dual Phase Steels

1Institute for Materials Testing, Materials Science and Strength of Materials, University of Stuttgart, 70569 Stuttgart, Germany
2Mechanical Engineering Department, Shahid Rajaee Teacher Training University, Tehran 16758-136, Iran

Received 14 July 2014; Accepted 15 November 2014; Published 7 December 2014

Academic Editor: Eric Hug

Copyright © 2014 A. Fotovati et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Nanoindentation test results on different grain sizes of dual phase (DP) steels are used to train artificial neural networks (ANNs). With selection of ferrite and martensite grain size, martensite volume fraction (MVF), and indentation force as input and microhardness, ferrite, and martensite nanohardness as outputs, six different ANNs are trained according to normalized datasets to predict hardness and their tolerances. A graphical user interface (GUI) is developed for a better investigation of the trained ANN prediction. The response of the ANN is analyzed in five case studies. In each case the variation of two input parameters on the output is analyzed when the other input parameters are kept constant. Reliable and reasonable results of ANN predictions are achieved in each case.