Monitoring and predictive modeling of crack propagation in aeronautical structural aluminum using a strain gauge network

Abstract Structural health monitoring techniques, while effective, often lack cost-efficiency and real-time capabilities. This demands innovative solutions or the improvement of traditional methods. For this reason, this study utilizes a strain gauge network to monitor and model crack propagation in aluminum 6082-T6 fuselage components for aeronautical applications. Aluminum 6082-T6 specimens (without defects, with notches, and with a pre-crack) were tested under static tensile test. The specimen with notches and a pre-crack was tested under three different fatigue loads (3 kN, 3.5 kN, and 4 kN) with six strategically placed strain gauges. Additionally, crack length data obtained from these sensors were validated through Eddy current inspections. After the experimental procedure, a novel mathematical modeling was developed to predict crack growth by correlating strain measurements with crack length. This model demonstrated strong reliability, with correlation coefficients exceeding 0.97 in experimental tests. The findings highlight the potential of integrating strain gauge networks with a predictive formula to enhance SHM practices, minimizing aircraft downtime and improving maintenance efficiency.