Randomized neural network based signature for classification of titanium alloy microstructures

Abstract

This paper presents the application of the randomized neural network based signature, an innovative and powerful texture analysis algorithm, to a relevant problem of metallography, which consists of classifying zones of titanium alloys Ti-6Al-4V into two categories: “alpha and beta” and “alpha + beta”. The obtained results are very promising, with accuracy of 98.84% by using LDA, and accuracy of 98.64%, precision of 99.11% for “alpha and beta”, and precision of 98.09% for “alpha + beta” by using SVM. This performance suggests that this texture analysis method is a valuable tool that can be applied to many other problems of metallography.