Open AccessCharacterization of acoustic emission signals under 3-point bending test
Author(s) -
N B G Nguyen,
Hirpa G. Lemu,
O Gabrielsen,
Idriss ElThalji
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1201/1/012034
Subject(s) - acoustic emission , bending , signal (programming language) , computer science , point (geometry) , three point flexural test , characterization (materials science) , test data , signal processing , test (biology) , acoustics , data mining , pattern recognition (psychology) , artificial intelligence , structural engineering , materials science , engineering , mathematics , telecommunications , physics , geology , nanotechnology , geometry , paleontology , radar , programming language
This paper summarizes a master’s thesis project which explored whether the characteristics of Acoustic Emission Testing (AET) signals can be used to detect yielding in steel samples undergoing a three-point bending test. A subset of existing data from a three-point bending test was exported and used as input. Data was processed by utilizing and developing tools to visualize and analyse the signal characteristics, primarily through a parameter-based approach. Signals were visualized, and parameters were optimized to identify and classify signal types. According to the obtained results, some limitations on classification were experienced due to the length of the hit data recorded. Though the work reported in this article lead to a reliable method for detecting yielding, the developed algorithms were not successful in identifying characteristics that could be used to detect yielding.