Evaluation of failure mechanism on Zirconia Reinforced alumina composites using by unsupervised clustering technique

The objective of this study is to utilize Principal component analysis (PCA) and Fuzzy clustering Means (FCM) as unsupervised clustering technique for the evaluation and classification of failure mechanism based on the analysis of the acoustic emission (AE) signals of presented. Materials used were two kinds of alumina and any kinds of zirconia reinforced alumina composite (ZrO2/Al2O3) specimens with the different microstructure, subjected to a three point bending test. Different AE sources are characterized and as a result, two types of failure mechanisms are distinguished. It is observed that at very low strain levels void nucleation is the main source for AE. At higher levels, the micro pop-in of primary voids and their eventual coalescence results in a different type of AE. In fine particle reinforced materials, when the amplitude of AE events in void nucleation at fine particles is not high enough to be detected, the main source of AE events is only the void coalescence

Keywords:

Acoustic emission , ceramic , composite , failure mechanism , evaluation ,