인공신경회로망을 이용한 원공결함을 갖는 유한 폭 판재의 음향방출 음원특성과 파괴거동에 관한 연구 = Acoustic Emission Source Characterization and Fracture Behavior of Finite-width Plate with a Circular Hole Defect using Artificial Neural Network

다국어 초록 (Multilingual 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) continuously, we used K-means clustering method as an unsupervised learning method for obtained 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 obtained multi-variate AE working data sets. According to the error of convergence for determinant criterion Wilk's λ, heuristic criteria D&B(Rij) and Tou values are discussed. As the results show that BPN produced some empty classes before or at the same time k-NNC detect fracture signals. And confirmed that could save trouble to AE signal processing if suitable error of convergence or acceptable encoding error gives to BPN.