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A new kind of low-carbon bainite steel with excellent strength and toughness was developed, serving as the bogie of the next-generation high-speed train. However, the softening of the heat-affected zone (HAZ) in laser-arc hybrid welding (LAHW) needs to be overcome. In this study, the effect of the cooling rate of the LAHW process on the microstructure and mechanical properties in the HAZ was explored via thermal simulation. The results showed that with increased cooling rate, the grain size increased, the content of lath martensite decreased, and the lath bainite gradually changed to a granular shape in the thermal simulation specimen. With the decrease in the cooling rate, i.e., with the increase of t8/5, the strength–toughness matching of the material showed a downward trend. The thermal simulation specimen with a t8/5 of 6~8 s had higher strength and good toughness, which can be considered a potential welding parameter reference. The content of martensitic austenite (M-A) constituents was the main factor that determined the strength and toughness of the joint. During the tensile test, the axial force caused the material to tighten, and the transverse stress as obvious in the part of the M-A constituents that are prone to microcracks and many defects, resulting in cracks, paths, and multi-component layers in the center. As a result, the thermal cycle specimens had mixed fracture characteristics.

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The time–frequency analysis by smooth Pseudo-Wigner-Ville distribution (SPWVD) is utilized for the double-line laser ultrasonic signal processing, and the effective detection of the metal surface defect is achieved. The double-line source laser is adopted for achieving more defects information. The simulation model by using finite element method is established in a steel plate with three typical metal surface defects (i.e. crack, air hole and surface scratch) in detail. Besides, in order to improve the time resolution and frequency resolution of the signal, the SPWVD method is mainly used. In addition, the deep learning defect classification model based on VGG convolutional neural network (CNN) is set up, also, the data enhancement method is adopted to extend training data and improve the defects detection properties. The results show that, for different types of metal surface defects with sub-millimeter size, the classification accuracy of crack, air holes and scratch surface are 94.6%, 94% and 94.6%, respectively. The SPWVD and CNN algorithm for processing the laser ultrasonic signal and defects classification supplies a useful way to get the defect information, which is helpful for the ultrasonic signal processing and material evaluation.