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

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2013-06-15

Autores

Nunes, Thiago M.
De Albuquerque, Victor Hugo C.
Papa, João Paulo [UNESP]
Silva, Cleiton C.
Normando, Paulo G.
Moura, Elineudo P.
Tavares, João Manuel R.S.

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Resumo

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 a new application and evaluation of artificial intelligent techniques to classify (the background echo and backscattered) ultrasound signals in order to characterize the microstructure of a Ni-based alloy thermally aged at 650 and 950 °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. © 2013 Elsevier B.V. All rights reserved.

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Bayesian classifiers, Detrended fluctuation analysis and Hurst method, Feature extraction, Nickel-based alloy, Non-destructive inspection, Optimum-path forest, Support vector machines, Thermal aging, Bayesian classifier, Detrended fluctuation analysis, Nickel based alloy, Non destructive inspection, Optimum-path forests, Artificial intelligence, Carbides, Forestry, Microstructure, Nickel, Nickel coatings, Ultrasonic waves, Alloy, Coatings

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Expert Systems with Applications, v. 40, n. 8, p. 3096-3105, 2013.