Determination of the damage mechanisms in armor structural materials via self-organizing map analysis

Abstract

Dynamic compression fracture behaviors together with damage mechanisms of materials subjected to a compressive load at a high strain rate were studied by using a Self-organizing map (SOM). The materials used for the analysis were Al5083, Rolled homogeneous armor (RHA) and tungsten heavy alloy (WHA). The deformation behavior and Acoustic emission (AE) signal were acquired through a Split Hopkinson pressure bar (SHPB)-AE coupled test. The self-organization map which is one of the artificial neural network technique was employed to classify the AE energy, amplitude, and peak frequency according to the characteristics of the signal. In addition, distributions of AE signals were represented in stress-strain curves. The correlation between AE characteristics and material damage mechanisms was investigated. Based on the results, it was found that cluster 1 with high AE energy, high amplitude and low frequency was the cluster of the AE signal generated near the yield point of the material. Cluster 3, which has the opposite tendency, was confirmed as a cluster of AE signals that occurred just before a fracture of the material. The change in the measured value can be seen depending on the strain rate and the material, but the overall tendency was similar.