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

► To monitor the kinetics of phase transformation from Ni-based alloy thermally aged and in the as-weld state. ► The ultrasound signals are preprocessed by detrended fluctuation analysis and the Hurst method. ► The speed and accuracy of the OPF (accuracy rate superior to 90%) used for first time in...

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Veröffentlicht in:	Expert systems with applications 2013-06, Vol.40 (8), p.3096-3105
Hauptverfasser:	Nunes, Thiago M., de Albuquerque, Victor Hugo C., Papa, João P., Silva, Cleiton C., Normando, Paulo G., Moura, Elineudo P., Tavares, João Manuel R.S.
Format:	Artikel
Sprache:	eng
Schlagworte:	Analysing. Testing. Standards Applied sciences Artificial intelligence Backscattering Bayesian classifiers Classification Classifiers Computer science control theory systems Data processing. List processing. Character string processing Detrended fluctuation analysis and Hurst method Exact sciences and technology Expert systems Feature extraction Inference from stochastic processes time series analysis Mathematics Memory organisation. Data processing Metals. Metallurgy Microstructure Nickel base alloys Nickel-based alloy Non-destructive inspection Nondestructive testing Optimum-path forest Phases Probability and statistics Sciences and techniques of general use Software Statistics Support vector machines Testing for defects Thermal aging Ultrasound
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