Weak adhesion detection-Enhancing the analysis of vibroacoustic modulation by machine learning

Adhesive bonding is a well-established technique for composite materials. Despite advanced surface treatments and preparations, surface contamination and application errors still occur, resulting in localised areas with a reduced adhesion. The dramatic reduction of the bond strength limits the applicability of adhesive bonds and hampers further industrial adaptation. This study aims to detect weak-bonds due to manufacturing errors or contamination by analysing and interpreting the vibroacoustic modulation signals with the aid of machine learning. An ultrasonic signal is introduced into the specimen by a piezoceramic actuator and modulated through a low frequency vibration excited by a servo-hydraulic testing system. Tested samples are single-lap shear specimens, according to ASTM D5868-01, with artificial circular debonding areas introduced as PTFEfilms or a release agent contamination. It is shown that an artificial neural network can identify various defects in the bonded joint robustly and is able to predict residual strengths and hence demonstrates great potential for non-destructive testing of adhesive joints.

Automated summary

This study utilizes machine learning to analyze and interpret vibroacoustic modulation signals to detect weak bonds in adhesive materials. By introducing ultrasonic signals and low frequency vibration, an artificial neural network is shown to robustly identify various defects in bonded joints and predict residual strengths, demonstrating great potential for non-destructive testing in adhesive joints.