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

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.

Soft-chemical synthesis, characterization and humidity sensing behavior of WO3/TiO2 nanopowders

Tungsten oxide/titania (WO3/TiO2) nanopowders were synthesized by the polymeric precursor method which varied the WO3 content between 0 and 10 mol%. The powders were thermally treated in a conventional furnace and their structural, microstructural and electric properties were evaluated by X-ray diffraction (XRD), Raman spectrometry, N 2 physisorption, NH3 chemisorption, temperature-programmed reduction (TPR), X-ray absorption near-edge spectroscopy (XANES) in situ XANES and extended X-ray absorption fine structure spectroscopy (EXAFS) and transmission electron microscopy (TEM). XRD and Raman spectrometry confirmed the homogeneous distribution of an amorphous WO3 phase in the TiO 2 matrix which stabilized the anatase phase through the generation of [TiO5·V0] or [TiO5·V 0] complex sites. Conventional TPR-H2 (temperature programmed reduction) along with XANES TPR-H2 and XANES TPR-EtOH showed that WO3/TiO2 sample reduction occurs through the formation of these complex clusters. Moreover, the addition of WO3 promoted an increase in the surface acidity of doped samples as revealed by NH3 chemisorption. The WO3/TiO2 bulk-ceramic samples were further used to estimate their potential application in a humidity sensor in the range of 15-85% relative humidity. Probable reasons that lead to the different humidity sensor responses of samples were given based on the structural and surface characterizations. Correlation between the sensing performance of the sensor and its structural features are also discussed. Although all samples responded as a humidity sensor, the W2T sample (2 mol% added WO3) excelled for sensitivity due to the increase in acid sites, optimum mean pore size and pore size distribution. © 2013 Elsevier B.V.

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