Quantitative evaluation of surface crack depth on notched bars with laser-infrared detection technology

To achieve non-destructive detection of crack depth in the process of notching and cracking of metal bars in low-stress cropping, a quantitative detection method for crack depth of notched bars based on infrared thermography technology under laser excitation is proposed in this paper. Combined with physical experiments, a simulation model of the temperature distribution of the laser-excited bar is established for efficient data acquisition, and the temperature curves of the bar surface under laser excitation are analysed from the perspective of space and time. On this basis, the study selects the feature parameters of the three types of defects on the surface of the metal bar, namely notch, surface crack and crack of notch bottom. A backpropagation (BP) neural network model is established for crack depth by dividing the crack into two types of a notch with crack and unnotched crack, and compared with other common prediction models. The results show that the selected features can accurately characterise the cracks in this BP neural network model, and the detected error in crack depth assessment is less than 3%. Performance metrics are established to evaluate the model, which has good reliability under different noises.