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Sensors 2015, 15(6), 12474-12497; doi:10.3390/s150612474

Ultrasonic Sensor Signals and Optimum Path Forest Classifier for the Microstructural Characterization of Thermally-Aged Inconel 625 Alloy

1
Programa de Pós-Graduação em Informática Aplicada, Universidade de Fortaleza, Fortaleza, Ceará 60811-905, Brazil
2
Departamento de Engenharia Metalúrgica e de Materiais, Universidade Federal do Ceará, Fortaleza, Ceará 60455-900, Brazil
3
Programa de Pós-Graduação em Ciências da Computação, Instituto Federal de Educação, Ciência e Tecnologia do Ceará, Fortaleza, Ceará 61939-140, Brazil
4
Departamento de Ciência da Computação, Universidade Estadual Paulista, Bauru, São Paulo 17033-360, Brazil
5
Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial, Departamento de Engenharia Mecânica, Faculdade de Engenharia, Universidade do Porto, Porto 4200-465, Portugal
*
Author to whom correspondence should be addressed.
Academic Editor: Vittorio M.N. Passaro
Received: 26 April 2015 / Accepted: 20 May 2015 / Published: 27 May 2015
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [617 KB, uploaded 27 May 2015]   |  

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 γ'' and δ phases. This work presents an evaluation of the powerful optimum path forest (OPF) classifier configured with six distance functions to classify background echo and backscattered ultrasonic signals from samples of the inconel 625 superalloy thermally aged at 650 and 950 \(^\circ\)C for 10, 100 and 200 h. The background echo and backscattered ultrasonic signals were acquired using transducers with frequencies of 4 and 5 MHz. The potentiality of ultrasonic sensor signals combined with the OPF to characterize the microstructures of an inconel 625 thermally aged and in the as-welded condition were confirmed by the results. The experimental results revealed that the OPF classifier is sufficiently fast (classification total time of 0.316 ms) and accurate (accuracy of 88.75% and harmonic mean of 89.52) for the application proposed. View Full-Text
Keywords: ultrasonic sensor; metric function; optimum path forest; signal classification; microstructural characterization ultrasonic sensor; metric function; optimum path forest; signal classification; microstructural characterization
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

de Albuquerque, V.H.C.; Barbosa, C.V.; Silva, C.C.; Moura, E.P.; Filho, P.P.R.; Papa, J.P.; Tavares, J.M.R.S. Ultrasonic Sensor Signals and Optimum Path Forest Classifier for the Microstructural Characterization of Thermally-Aged Inconel 625 Alloy. Sensors 2015, 15, 12474-12497.

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