RECOGNITION OF DEFECTS ON STEEL ROD USING ULTRASONIC GUIDED WAVES BASED ON NEURAL NETWORK

A kind of defect recognition algorithm based on BP neural network is used to extract characteristic quantities from the samples obtained at different experimental conditions, and distinguish the different sizes and positions of radial cracks in steel rods. Firstly, the axisymmetric longitudinal guided waves mode in 235 kHz are excited to detect the radial cracks in a steel rod. Experimental results show that obtained guided waves contain quite single L(0,2) mode in 235 kHz, it avoids the problem of weaker detection capability using L(0,1) mode to detect small size defects, and reduces the difficulty to distinguish the defect echo because more modes involved when axisymmetric longitudinal high order modes are used to detect the steel rod. Secondly, the algorithm is used to recognize the radial cracks in a steel rod. Results show that the proposed defect recognition algorithm can identify different depths and positions of cracks well, and the correct rate of recognition has stabled at 87% in existing experimental samples.