Hindawi Publishing Corporation International Journal of Aerospace Engineering Volume 2011, Article ID 874375, 7 pages doi:10.1155/2011/874375 Research Article Study on Ductility of Ti Aluminide Using Artificial Neural Network R. K. Gupta, 1 Rama Mehta, 2 Vijaya Agarwala, 3 Bhanu Pant, 1 and P. P. Sinha 1 1 Materials and Mechanical Entity, Vikram Sarabhai Space Center, Trivandrum 695022, India 2 National Institute of Hydrology, Roorkee 247667, India 3 Departement of Metallurgical and Materials Engineering, Indian Institute of Technology, Roorkee 247667, India Correspondence should be addressed to R. K. Gupta, rohitkumar gupta@vssc.gov.in 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. 1. Introduction Ti aluminide has been an important aerospace material due to its high temperature properties and lower density as com- pared to superalloys. The ordered structure of aluminides that make them useful for high temperature applications makes them brittle at ambient temperature [1–3]. Therefore, inspite of having good properties, the usefulness of these alloys has been limited to some specific applications only. Room temperature tensile ductility is maximum (∼1.5%) at around Ti-48Al (at%) aluminum, which is insufficient for further processing and applications. Hence, development of Ti aluminides has centered around Ti-48Al (at%) com- position. It belongs to the γ (TiAl) plus α 2 (Ti 3 Al) region of the phase diagram [4–6]. Various methods like alloy ad- dition, controlled processing, heat treatment and so forth, are applied to get optimum combination of strength and ductility. Alloying additions in the range of 1 to 10 at% is studied with Cr, V, Mn, Nb, Ta, W, and Mo [6]. The alloying additions of V, Mn, Ni, and Cr in the range of 2– 4 at% have shown enhancement in ductility of the alloy. Effect of microstructure on the mechanical properties was studied and duplex structure with fine grain size has been reported to be optimum for superior strength and ductility [4, 7–9]. To obtain desired microstructures and mechanical properties, the effect of several heat treatment cycles on aluminides has been studied at different temperatures and with varying cooling rates [9–16] and marginal improvement in ductility was reported. In this way several studies have been conducted with limited success in improving ductility of the alloy. However, experimental studies are expensive due to the use of high purity alloying elements and processing under controlled atmosphere. Here, theoretical models are very useful for optimization of process parameters. Experi- mentation with such optimized parameters shall minimize the number of experimental attempts and could lead to achieve desired ductility. During the last decade, there has been an increased in- terest in applying new emerging theoretical techniques such as fuzzy inference system (FIS) and artificial neural network (ANN) for optimization-related problems [17–20]. These are the most common data driven models. These models