A Data-Driven Acoustic Emission Framework for Fracture Mode Identification in Adhesive Bonded Joints under Mode I Fatigue Loading

Adhesively bonded joints are extensively utilised in multi-material lightweight structures, including those with high load-bearing demands, such as wind turbine blades, and in the automotive and naval sectors. However, for certification in critical safety applications, adhesively bonded joints cannot be relied on alone and require backup measures, such as rivets and bolts, to mitigate the risk of sudden failure. A typical example is the secondary bonded parts in aircraft. To increase the reliability of adhesively bonded joints, it is crucial to have monitoring methods that accurately identify their damage evolution and different fracture modes, even under fatigue loading. This work aims to show how acoustic emission can act as a real-time monitoring technique for identifying various fracture modes, such as adhesive and cohesive failure, during mode I fatigue loading. To this end, an acoustic emission system was used to monitor adhesively bonded double-cantilever beam specimens under continuous mode I fatigue loading. The acquired datasets were analysed using a workflow that combines unsupervised artificial neural networks for clustering, followed by waveform inspection, and statistical analyses including distribution fitting, bootstrapping, and confidence intervals. After post-processing, it was possible to distinguish the fracture modes of bonded joints under fatigue failure, cohesive or adhesive, by observing distinct patterns and behaviours in the acoustic emission features. At the specimen level, cohesive failure resulted in more hits and higher cumulative energy than adhesive failure.