Automatic microstructural characterization and classification using dual tree complex wavelet-based features and Bees Algorithm

Abstract During the gas tungsten arc welding of nickel-based superalloys, the secondary phases such as Laves and carbides are formed in final stage of solidification. But, other phases such as γ″ and δ phases can precipitate in the microstructure, during aging at high temperatures. However, it is possible to minimize the formation of the Nb-rich Laves phases and therefore reduce the possibility of solidification cracking by adopting the appropriate welding conditions. This paper aims at the automatic microstructurally characterizing the kinetics of phase transformations on an Nb-base alloy, thermally aged at 650 and 950 °C for 10, 100 and 200 h, through backscattered ultrasound signals at frequency of 4 MHz and dual tree complex wavelet transform (DTCWT)-based feature extraction technique. The feature set comprises of statistical attributes (such as variance, skewness and kurtosis) extracted from the complex wavelet coefficients which are obtained using the DTCWT decomposition of a backscattered ultrasound signal. Also, the performance of the proposed feature extraction technique is compared with the conventional discrete wavelet transform. Finally, these features are fed to the probabilistic neural network (PNN) and radial basis function classifiers to automatic microstructural classification. The training process of these networks depends on the selection of the smoothing parameter of the networks’ activation function in a hidden layer. In this article, we introduce the application of the Bees Algorithm to the automatic adaptation of smoothing parameters. The proposed feature extraction technique coupled with the optimized PNN yielded the highest average accuracy of 96 and 83 %, respectively, for thermal aging at 650 and 950 °C. Thus, the proposed processing system provides high reliability to be used for microstructure characterization through ultrasound signals.