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Randomized Neural Network Based Signature for Classification of Titanium Alloy Microstructures

  • Jarbas Joaci de Mesquita Sá Junior
  • André R. Backes
  • Odemir Martinez Bruno
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10657)

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.

Keywords

Randomized neural network based signature Titanium alloy Texture Metallography 

Notes

Acknowledgments

Jarbas Joaci de Mesquita Sá Junior thanks CNPq (National Council for Scientific and Technological Development, Brazil) (Grant: 152054/2016-2 and 453835/2017-1) for the financial support of this work. André R. Backes gratefully acknowledges the financial support of CNPq (Grant #302416/2015-3) and FAPEMIG (Foundation to the Support of Research in Minas Gerais) (Grant #APQ-03437-15). Odemir M. Bruno gratefully acknowledges the financial support of CNPq (307797/2014-7 and 484312/2013-8) and FAPESP (14/08026-1). We also thank the authors of the paper [9] for kindly providing the titanium alloy images used in this paper.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Jarbas Joaci de Mesquita Sá Junior
    • 1
    • 2
  • André R. Backes
    • 3
  • Odemir Martinez Bruno
    • 1
  1. 1.São Carlos Institute of PhysicsUniversity of São PauloSão CarlosBrazil
  2. 2.Department of Computer EngineeringCampus de Sobral - Universidade Federal do CearáSobralBrazil
  3. 3.School of Computer ScienceUniversidade Federal de UberlândiaUberlândiaBrazil

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