Ductility prediction of Ti aluminide intermetallics through neuro-fuzzy set approach

Abstract

Several studies have been conducted to improve the room temperature ductility of titanium aluminide intermetallics through alloy design and microstructure modifications. Ductility of two phase (α2+γ) binary Ti aluminide intermetallics centered on Ti-48Al (at%) was reported as maximum (∼1.5%) in desirable heat treatment condition and so more studies were attempted near to this composition. In the present work also, ductility has been studied for the alloy variants of this composition through fuzzy modeling. Neuro-fuzzy models were developed through Adaptive Neural Fuzzy Inference System (ANFIS) using subtractive cluster techniques. The input parameters were fuzzified with Gaussian membership functions to develop the fuzzy rules. The output of each rule was obtained by the evaluation of the membership values. Finally the overall fuzzy model response was obtained as the weighted average of the individual rule response. Ductility database were prepared and three parameters viz. alloy type, grain size and heat treatment cycle were selected for modeling. Additional, ductility data were generated from literature based experimental data for training and validation of models on the basis of linearity and considering the primary effect of these three parameters. Adequacy of developed models was evaluated with the generated data sets. Different evaluation measures were considered and the resulting graphs from the developed model were analyzed. The results of the fuzzy models were found to be very close to the literature based generated data and it also showed the possibility of improving ductility upto 7% for multicomponent alloy with grain size of 10–50μm following a multistep heat treatment cycle.