دانلود مقاله ISI انگلیسی شماره 52402
ترجمه فارسی عنوان مقاله

ویژگی های میکرو سازمانی خودکار و طبقه بندی با استفاده از تکنیک های هوش مصنوعی در سیگنال های اولتراسوند

عنوان انگلیسی
Automatic microstructural characterization and classification using artificial intelligence techniques on ultrasound signals
کد مقاله سال انتشار تعداد صفحات مقاله انگلیسی
52402 2013 10 صفحه PDF
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Expert Systems with Applications, Volume 40, Issue 8, 15 June 2013, Pages 3096–3105

چکیده انگلیسی

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.