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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Automatic microstructural characterization and classification using artificial intelligence techniques on ultrasound signals

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Author(s):
Nunes, Thiago M. [1] ; de Albuquerque, Victor Hugo C. [2] ; Papa, Joao P. [3] ; Silva, Cleiton C. [4] ; Normando, Paulo G. [1] ; Moura, Elineudo P. [4] ; Tavares, Joao Manuel R. S. [5]
Total Authors: 7
Affiliation:
[1] Univ Fed Ceara, Dept Engn Teleinformat, Fortaleza, Ceara - Brazil
[2] Univ Fortaleza, Programa Posgrad Informat Aplicada, Fortaleza, Ceara - Brazil
[3] Univ Estadual Paulista, Dept Ciencia Comp, Bauru, SP - Brazil
[4] Univ Fortaleza, Dept Engn Met & Mat, Fortaleza, Ceara - Brazil
[5] Univ Porto, Fac Engn, Dept Engn Mecan, Inst Engn Mecan & Gestao Ind, P-4100 Oporto - Portugal
Total Affiliations: 5
Document type: Journal article
Source: EXPERT SYSTEMS WITH APPLICATIONS; v. 40, n. 8, p. 3096-3105, JUN 15 2013.
Web of Science Citations: 24
Abstract

Secondary phases such as Laves and carbides are formed during the final solidification stages of nickel based superalloy coatings deposited during the gas tungsten arc welding cold wire process. However, when aged at high temperatures, other phases can precipitate in the microstructure, like the gamma `' and delta phases. This work presents a new application and evaluation of artificial intelligent techniques to classify (the background echo and bacicscattered) ultrasound signals in order to characterize the microstructure of a Ni-based alloy thermally aged at 650 and 950 degrees C for 10,100 and 200 h. The background echo and backscattered ultrasound signals were acquired using transducers with frequencies of 4 and 5 MHz. Thus with the use of features extraction techniques, i.e., detrended fluctuation analysis and the Hurst method, the accuracy and speed in the classification of the secondary phases from ultrasound signals could be studied. The classifiers under study were the recent optimum-path forest (OPF) and the more traditional support vector machines and Bayesian. The experimental results revealed that the OPF classifier was the fastest and most reliable. In addition, the OPF classifier revealed to be a valid and adequate tool for microstructure characterization through ultrasound signals classification due to its speed, sensitivity, accuracy and reliability. (C) 2012 Elsevier Ltd. All rights reserved. (AU)

FAPESP's process: 09/16206-1 - New trends on optimum-path forest-based pattern recognition
Grantee:João Paulo Papa
Support type: Research Grants - Young Investigators Grants