Conveners
Machine Learning in Acoustic Emission
- Václav Kůs (Department of Mathematics, FNSPE Czech Technical University in Prague)
The goal of this paper is to summarize deep learning methods and apply some of these architectures to real data from tensile tests of metallic materials. Here many existing neural networks are applied to a signal gained from acoustic emission to determine the beginning of plasticity in the material. Plastic deformation is accompanied by microscopic events such as a slip of atomic plane...
Reliable classification of acoustic emission signals is crucial for practical use of this nondestructive testing technique. During classification, signals are represented by a convenient, low-dimensional set of attributes. This presentation adresses the problem of selecting appropriate atributes and consequently describes and compares several classification methods, specifically Division...
