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

• Type: Article in International Scientific Journal
• Date: 2013

Authors

Thiago M. Nunes (Author) Other; Victor Hugo C. de Albuquerque (Author) FEUP; João P. Papa (Author) Other; Cleiton C. Silva (Author) Other; Paulo G. Normando (Author) Other; Elineudo P. Moura (Author) Other; João Manuel R. S. Tavares (Author) FEUP

Journal

Title: Expert Systems with Applications

Vol. 40 No. 16

Pages: 3096-3105

ISSN: 0957-4174

Publisher: Elsevier

Indexing

ISI Web of Knowledge - 24 Citations

ISI Web of Science

Scopus - 29 Citations

Scientific classification

FOS: Engineering and technology

CORDIS: Technological sciences

Other information

Authenticus ID: P-005-012

DOI: 10.1016/j.eswa.2012.12.025

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 10