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International Journal of Aerospace Engineering
Volume 2011, Article ID 874375, 7 pages
Research Article

Study on Ductility of Ti Aluminide Using Artificial Neural Network

1Materials and Mechanical Entity, Vikram Sarabhai Space Center, Trivandrum 695022, India
2National Institute of Hydrology, Roorkee 247667, India
3Departement of Metallurgical and Materials Engineering, Indian Institute of Technology, Roorkee 247667, India

Received 25 March 2011; Revised 7 August 2011; Accepted 16 August 2011

Academic Editor: Kenneth M. Sobel

Copyright © 2011 R. K. Gupta 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.


Improvement of ductility at room temperature has been a major concern on processing and application of Ti aluminides over the years. Modifications in alloy chemistry of binary alloy (Ti48 Al) and processing conditions were suggested through experimental studies with limited success. Using the reported data, the present paper aims to optimize the experimental conditions through computational modeling using artificial neural network (ANN). Ductility database were prepared, and three parameters, namely, alloy type, grain size, and heat treatment cycle were selected for modeling. Additionally, ductility data were generated from the literature for training and validation of models on the basis of linearity and considering the primary effect of these three parameters. Model was trained and tested for three different datasets drawn from the generated data. Possibility of improving ductility by more than 5% is observed for multicomponent alloy with grain size of 10–50 μm following a multistep heat treatment cycle.