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Damage mechanism identification has scientific and practical ramifications for the structural health monitoring, design, and application of composite systems. Recent advances in machine learning uncover pathways to identify the waveform-damage mechanism relationship in higher-dimensional spaces for a comprehensive understanding of damage evolution. This review evaluates the state of the field, beginning with a physics-based understanding of acoustic emission waveform feature extraction, followed by a detailed overview of waveform clustering, labeling, and error analysis strategies. Fundamental requirements for damage mechanism identification in any machine learning framework, including those currently in use, under development, and yet to be explored, are discussed.

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Silicon carbide coated onto Hi-Nicalon Type S fiber is of great interest to the aerospace industry. This work focuses on tuning the reaction parameters of atmospheric pressure SiC CVI using CH 3 SiCl 3 to control the morphology of the coatings produced. Depth of CH 3 SiCl 3 from 1 to 14 cm, temperature from 1000 to 1100 °C, and flow rate of H 2 carrier gas from 5 to 30 SCCM were examined. Coating morphologies ranged from smooth to very nodular, where spherical growths were present along the entire deposition zone. The parameters that yielded a smooth deposition throughout the 20 cm deposition zone were 4–6 cm of CH 3 SiCl 3(l) depth, 1100 °C, and 10 SCCM of H 2 as a carrier gas. Tensile testing using acoustic emission sensors was performed on SiC f /BN/CVI-SiC minicomposites with different coating morphologies. The tensile tests revealed that smooth coatings have better mechanical performance than the nodular coatings; nodular coatings promote premature ultimate brittle failure, while smooth coatings exhibit toughening mechanisms. Smooth coatings had higher average matrix cracking strength (248 MPa) and ultimate tensile strength (541 MPa) than average nodular coating matrix cracking strength (147 MPa) and ultimate strength (226 MPa).

In this work, we demonstrate that damage mechanism identification from acoustic emission (AE) signals generated in minicomposites with elastically similar constituents is possible. AE waveforms were generated by SiC/SiC ceramic matrix minicomposites (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 they produced, despite the similar constituent elastic 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.