The aim of the present study is to characterize the damage in bi-material steel-to-composite double-lap adhesively-bonded joints using Acoustic Emission (AE). Two different structural adhesives, a ductile (Methacrylate-based) and brittle (Epoxy-based), were used to bond CFRP skins to a steel core. The fabricated joints were loaded in tension while damage evolution was monitored by AE. Due to the difference in the fracture nature of the adhesives “ductile vs. brittle”, different damage mechanisms were observed; including cohesive failure within the adhesive layer, steel deformation, failure at the adhesive/adherends interface (adhesive failure) and delamination in the CFRP skin. To classify these damages by AE, the AE features of each damage mechanism were first obtained by conducting standard tests on the individual constituents. Then, these AE reference patterns were used to train an ensemble decision tree classifier. The best parameters of the ensemble model were obtained by Bayesian optimization, and the confusion matrix showed that the model was sufficiently trained with the accuracy of 99.5% and 99.8% for Methacrylate-based and Epoxy-based specimens respectively. Afterwards, the trained model was used to classify the AE signals of the double-lap specimens. The AE demonstrated that the dominant damage mechanisms in the case of the Methacrylate-based were cohesive and adhesive failures while in the case of the Epoxy-based they were CFRP skin failure and adhesive failure. These results were consistent with the Digital Image Correlation, Fiber Optic Sensor and camera results. This study demonstrates the potential of AE technique for damage characterization of adhesively-bonded bi-material joints.
Original languageEnglish
Article number107356
Pages (from-to)107356
Number of pages1
JournalComposites Part B: Engineering
Volume176
DOIs
Publication statusPublished - 2019

    Research areas

  • Adhesion, Damage mechanics, Acoustic emission, joints/joining, Supervised classification, Damage mechanics, Acoustic emission, Adhesion, joints/joining, Supervised classification

ID: 56020077