Characterization of cast iron microstructure through fluctuation and fractal analyses of ultrasonic backscattered signals combined with classification techniques

Download
  1. Get@NRC: Characterization of cast iron microstructure through fluctuation and fractal analyses of ultrasonic backscattered signals combined with classification techniques (Opens in a new window)
DOIResolve DOI: http://doi.org/10.1007/s10921-011-0124-6
AuthorSearch for: ; Search for: ; Search for: ; Search for:
TypeArticle
Journal titleJournal of Nondestructive Evaluation
ISSN0195-9298
1573-4862
Volume31
Issue1
Pages9098; # of pages: 9
Subjectnondestructive testing; cast iron; statistical fluctuation analysis; fractal analysis; principal component analysis; Karhunen-Loève transformation; neural network; Gaussian classifier
AbstractThis work aims at evaluating the performance of pattern recognition methods in the identification of different microstructures presented by cast iron, namely, lamellar, vermicular and nodular microstructures, through the statistical fluctuation and fractal analyses of backscattered ultrasonic signals. The signals were obtained with a broad band ultrasonic probe with a central frequency of 5 MHz. The statistical fluctuations of the ultrasonic signals were analyzed by means of Hurst (RSA) and detrended-fluctuation analyses (DFA), and the fractal analyses were carried out by applying the minimal cover and box-counting techniques to the signals. The curves obtained from the statistical fluctuations and fractal analyses, as functions of the time window, were processed by using four pattern classification techniques, namely, principal-component analysis (PCA), Karhunen-Loève transformation (KLT), neural networks and Gaussian classifier. The best results were obtained by Karhunen-Loève expansion and neural networks, where an approximately 100% success rate has been reached for the classification of the different microstructures as well as for the training and the testing sets of events. The results presented correspond to an average taken over 100 randomly chosen sets of events. These results indicate that, within the techniques used, the Karhunen-Loève transformation and neural network associated with the statistical fluctuation analyses (RSA and DFA) are the best tools for the recognition of the different cast iron microstructures. It is worthwhile pointing out that the microstructure classification was made by using backscattering signals acquired during pulse echo ultrasonic nondestructive testing only. Therefore, that approach is a promising method for material characterization.
Publication date
LanguageEnglish
AffiliationNRC Industrial Materials Institute; National Research Council Canada
Peer reviewedYes
Identifier124
NPARC number21268390
Export citationExport as RIS
Report a correctionReport a correction
Record identifierf8748e9f-9f05-4cab-9d17-55ac23a2cc39
Record created2013-07-09
Record modified2016-05-09
Bookmark and share
  • Share this page with Facebook (Opens in a new window)
  • Share this page with Twitter (Opens in a new window)
  • Share this page with Google+ (Opens in a new window)
  • Share this page with Delicious (Opens in a new window)