Acoustic Emission Source Classification of Finite-width Plate with a Circular Hole Defect using k-Nearest Neighbor Algorithm

k-최근접 이웃 알고리즘을 이용한 원공결함을 갖는 유한 폭 판재의 음향방출 음원분류에 대한 연구

  • 이장규 (인천대학교 기계공학과) ;
  • 오진수 (인천대학교 대학원 기계공학과)
  • Published : 2009.03.31

Abstract

A study of fracture to material is getting interest in nuclear and aerospace industry as a viewpoint of safety. Acoustic emission (AE) is a non-destructive testing and new technology to evaluate safety on structures. In previous research continuously, all tensile tests on the pre-defected coupons were performed using the universal testing machine, which machine crosshead was move at a constant speed of 5mm/min. This study is to evaluate an AE source characterization of SM45C steel by using k-nearest neighbor classifier, k-NNC. For this, we used K-means clustering as an unsupervised learning method for obtained multi -variate AE main data sets, and we applied k-NNC as a supervised learning pattern recognition algorithm for obtained multi-variate AE working data sets. As a result, the criteria of Wilk's $\lambda$, D&B(Rij) & Tou are discussed.

Keywords

References

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