Towards non-destructive machine learning-based acoustic resonance testing of aluminium 2024 riveted joints

In the aerospace industry, non-destructive testing is commonly used to ensure structural integrity. Methods such as eddy current, ultrasonic and radiographic testing are applied, but often require relatively expensive equipment and experienced operators for scanning and data interpretation. Acoustic resonance testing is an objective and cost-effective method that can test entire structures. This study investigated a machine learning-based acoustic resonance testing method for predicting relative stiffness loss as an indicator of progressive damage in riveted joints. Fatigue tests were performed in which the specimens were excited by a hammer impulse and acoustic emissions were measured at intervals. Two feature extraction approaches were applied to the frequency response function and different machine learning models for the prediction. The best results were obtained using a Convolutional Neural Network. It achieved an R² of 0.98 for predicting the loss of the joint stiffness and a classification accuracy of 100 % for defect detection based on these predictions.