Microstructure identification via detrended fluctuation analysis of ultrasound signals

We describe an algorithm for simulating ultrasound propagation in random one-dimensional media, mimicking different microstructures by choosing physical properties such as domain sizes and mass densities from probability distributions. By combining a detrended fluctuation analysis (DFA) of the simulated ultrasound signals with tools from the pattern-recognition literature, we build a Gaussian classifier which is able to associate each ultrasound signal with its corresponding microstructure with a very high success rate. Furthermore, we also show that DFA data can be used to train a multilayer perceptron which estimates numerical values of physical properties associated with distinct microstructures.Comment: Submitted to Phys. Rev.

Mechanical Properties and Microstructural Characterization of Aged Nickel-based Alloy 625 Weld Metal

The aim of this work was to evaluate the different phases formed during solidification and after thermal aging of the as-welded 625 nickel-based alloy, as well as the influence of microstructural changes on the mechanical properties. The experiments addressed aging temperatures of 650 and 950 A degrees C for 10, 100, and 200 hours. The samples were analyzed by electron microscopy, microanalysis, and X-ray diffraction in order to identify the secondary phases. Mechanical tests such as hardness, microhardness, and Charpy-V impact test were performed. Nondestructive ultrasonic inspection was also conducted to correlate the acquired signals with mechanical and microstructural properties. The results show that the alloy under study experienced microstructural changes when aged at 650 A degrees C. The aging was responsible by the dissolution of the Laves phase formed during the solidification and the appearance of gamma aEuro(3) phase within interdendritic region and fine carbides along the solidification grain boundaries. However, when it was aged at 950 A degrees C, the Laves phase was continuously dissolved and the excess Nb caused the precipitation of the delta-phase (Ni3Nb), which was intensified at 10 hours of aging, with subsequent dissolution for longer periods such as 200 hours. Even when subjected to significant microstructural changes, the mechanical properties, especially toughness, were not sensitive to the dissolution and/or precipitation of the secondary phases

Ultrasound, eddy current and magnetic Barkhausen noise as tools for sigma phase detection on a UNS S31803 duplex stainless steel

Sigma phase is a deleterious one which can be formed in duplex stainless steels during heat treatment or welding. Aiming to accompany this transformation, ferrite and sigma percentage and hardness were measured on samples of a UNS S31803 duplex stainless steel submitted to heat treatment. These results were compared to measurements obtained from ultrasound and eddy current techniques, i.e., velocity and impedance, respectively. Additionally, backscattered signals produced by wave propagation were acquired during ultrasonic inspection as well as magnetic Barkhausen noise during magnetic inspection. Both signal types were processed via a combination of detrended-fluctuation analysis (DFA) and principal component analysis (PCA). The techniques used were proven to be sensitive to changes in samples related to sigma phase formation due to heat treatment. Furthermore, there is an advantage using these methods since they are nondestructive. (C) 2010 Elsevier B.V. All rights reserved.FUNCAPCNPqCAPESUF

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