A Machine Learning Framework for Damage Mechanism Identification from Acoustic Emission in Unidirectional SiC/SiC CMCs

A challenging opportunity in structural health monitoring of ceramic matrix composite (CMC) materials is using machine learning (ML) methods to sort acoustic emissions according to the damage mechanism that emitted the signal. Historically, it has been hypothesized that the chemical and elastic similarity of SiC/SiC CMCs constituents would prevent damage mechanism discrimination between the two dominant damage mechanisms of matrix cracking and fiber failure.<br/>In this work, we demonstrate that damage mechanism identification from acoustic emission (AE) signals generated in SiC/SiC minicomposites with elastically similar constituents is possible. Three sets of AE waveforms were generated by SiC/SiC CMCs loaded under uniaxial tension and recorded by four sensors (two models with each model placed at two ends). Signals were encoded with a modified partial power scheme and subsequently partitioned through spectral clustering. Matrix cracking and fiber failure were identified based on the frequency information contained in the AE event, despite the similar constituent properties of the matrix and fiber. Importantly, the resultant identification of AE events closely followed CMC damage chronology, wherein early matrix cracking is later followed by fiber breaks, even though the approach is fully domain-knowledge agnostic. Additionally, the partitions were highly precise across both the model and location of the sensors, and the partitioning was repeatable. The presented approach is promising for CMCs and other composite systems with elastically similar constituents.