Machine Learning
Supervised pattern classification based on optimum-path forest
International Journal of Imaging Systems and Technology - Contemporary Challenges in Combinatorial Image Analysis
Efficient supervised optimum-path forest classification for large datasets
Pattern Recognition
An overview of statistical learning theory
IEEE Transactions on Neural Networks
Expert Systems with Applications: An International Journal
| Hi-index | 12.05 |
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 @c'' and @d 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^oC for 10, 100 and 200h. The background echo and backscattered ultrasound signals were acquired using transducers with frequencies of 4 and 5MHz. 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.