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Acoustic Emission Source Characterization and Fracture Behavior of Finite-width Plate with a Circular Hole Defect using Artificial Neural Network  

Rhee, Zhang-Kyu (인천대학교 기계공학과)
Woo, Chang-Ki (인천대학교 기계공학과)
Publication Information
Transactions of the Korean Society of Machine Tool Engineers / v.18, no.2, 2009 , pp. 170-177 More about this Journal
Abstract
The objective of this study is to evaluate an acoustic emission (AE) source characterization and fracture behavior of the SM45C steel by using back-propagation neural network (BPN). In previous research Ref. [8] about k-nearest neighbor classifier (k-NNC) continuity, we used K-means clustering method as an unsupervised learning method for obtaining multi-variate AE main data sets, such as AE counts, energy, amplitude, risetime, duration and counts to peak. Similarly, we applied k-NNC and BPN as a supervised learning method for obtaining multi-variate AE working data sets. According to the error of convergence for determinant criterion Wilk's ${\lambda}$, heuristic criteria D&B(Rij) and Tou values are discussed. As a result, in k-NNC before fracture signal is detected or when fracture signal is detected, showed that produce some empty classes in BPN. And we confirmed that could save trouble in AE signal processing if suitable error of convergence or acceptable encoding error give to BPN.
Keywords
Acoustic Emission; k-Nearest Neighbor Classifier; Back-Propagation Neural Network; Error of Convergence; Heuristic Criterion; Empty Class;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
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