Characterization And Classification On Ultrasound Signals Using Dct Transformation

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Microstructural characterization and classification on ultrasound signals Using DCT transformation in PCA framework Masoud Vejdannik a, Ali Sadr b a,b School of Electrical Engineering, Iran University of Science & Technology (IUST), Narmak, Tehran 16844, Iran a m_vejdannik@yahoo.com, b sadr@iust.ac.ir Abstract Purpose: Method: Results: Conclusions: Introduction Nb-bearing nickel-based superalloys, like the Inconel 625 alloy studied here, exhibit an outstanding combination of mechanical properties and resistance to pitting, crevice and intergranular corrosion due to the stiffening effect of chromium, molybdenum and niobium in its nickel matrix. These properties make precipitation hardening treatments unnecessary [--]. The extraordinary…show more content…
This segregation and precipitation of the secondary phases can change the mechanical properties of the alloy and decrease its resistance to corrosion [--]. In addition, the Nb-rich Laves phase has a low melting point that causes an increase in the temperature solidification range, making the alloy susceptible to solidification cracking [--]. However, an adequate selection of the welding conditions can minimize the formation of the Nb-rich Laves phases and consequently, reduce its susceptibility to solidification cracking. Therefore, it is also important to investigate the phase transformation process. Nowadays, researchers are evaluating the use artificial intelligence techniques to characterize microstructures. For example, Albuquerque, Filho, Cavalcante, and Tavares [--] quantified the porosity of synthetic materials from optical microscopic images successfully, and the solution proposed, which was based on an artificial neuronal network (ANN), proved to be more reliable. Albuquerque, de Alexandria, Cortez, and Tavares [--] characterized the microstructures in images of nodular, grey, and malleable cast irons using a multilayer perceptron
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