• Teixeira De Freitas, Sofia
• Tao, Ran
• Carboni, Michele
• De Araujo Alves Lima, Rosemere
• Bernasconi, A.
• Carrere, Nicolas

Abstract

Understanding the relationship between the sensors’ outputs and the damage evolution within the joints is becoming increasingly crucial to improving structural health monitoring systems and collecting data to improve the joint’s design. Therefore, a study of the acoustic emission method associated with visual fracture evaluation was proposed to give insights into the toughening of composite bonded joints and better understand the relationship between the acoustic emission features and the damage mechanism involved. Thus, two different layups were proposed for the substrates: [0]8 and [0/902/0]S. In addition, a toughened epoxy adhesive with an embedded carrier (AF163-2k) was used to bond the substrates. Five specimens of each stacking sequence were tested under quasi-static mode I loading conditions. A travelling microscope and a regular digital camera were used on the lateral sides of the specimens to track the crack propagation paths. One piezoelectric sensor linked to the AMSY-6 Vallen system was used to assess the acoustic emission features produced within the joints during the tests. Unsupervised machine learning algorithms based on artificial neural networks and the Morlet continuous wavelet transformation were used to pattern recognition of the acoustic emission data. Self-organising maps, together with k-means algorithms, were used for data clustering. Following that, the acoustic emission features of each cluster were associated with the insights obtained from the crack propagation images. Finally, it was observed that the different layups triggered simultaneous toughening mechanisms. The combination of the acoustic emission and the visual evaluation was crucial for a deeper understanding of the underlying phenomena.

Topics

• impedance spectroscopy
• cluster
• crack
• composite
• acoustic emission
• clustering
• machine learning