Damage characterization and recognition of aluminum alloys based on acoustic emission signal

With the rapid development of high-speed rails, high-strength aluminum alloys are widely used in the lightweight design, but the service safety assessment of gear boxes in high-speed trains needs to be improved in China. An acoustic emission tensile test system was built for high-speed train gearbox shells made of aluminum alloys. After training and recognition by a BP neural network, acoustic emission signal was used for characterizing tensile damage in the materials and warning the materials service status. The research provides a method of nondestructive real-time characterization and warning for damage in aluminum alloys.