The copper alloy is widely used to prepare pipes in ocean engineering. The surface is washed and corroded by seawater for a long time, which gradually shortens the service life of the condenser tube. In order to improve the wear and corrosion resistance of copper alloy, a Ni60-WC coating was fabricated on a preheated copper alloy by laser cladding. Experiments on the Ni60-WC coatings were carried out by SEM, XRD, Vickers-microhardness meter, wear tester and electrochemical workstation. The microstructure, phases, hardness, wear and corrosion resistance were investigated. The results show that from the top to bottom, the microstructures were columnar dendrites, dendritic crystals and grains, respectively. The wear rate of the Ni60-WC coating was only 4.9 × 10⁻⁵ mm³·N⁻¹·m⁻¹, which was only 1.14% of copper substrate. In addition, the corrosion current density was 2.34 × 10⁻⁷ mA·mm⁻², which was much lower than that of copper alloy substrate (1.14 × 10⁻⁶ mA·mm⁻²). The experimental results show that this Ni60-WC coating has a good metallurgical bonding and hardness, and it also has good wear and corrosion resistance, which is helpful to improve the service life of the condenser tube.

Longitudinal cracks are a common defect on the surface of continuous casting slabs, and cause increases in additional processing costs or long-time interruptions. The accurate identification of surface longitudinal cracks is helpful to ensure the casting process is adjusted in time, which significantly improves the quality of slabs. In this research, the typical temperature characteristics of thermocouples at the position of longitudinal cracks and their adjacent locations were extracted. The principal component analysis (PCA) method was used to reduce the dimensions of these characteristics to remove the redundant information. The particle swarm optimization (PSO) method was introduced to optimize the parameter. On this basis, a recognition model of surface longitudinal cracks was established, based on a particle swarm optimization–eXtreme gradient boosting (XGBOOST) model. Finally, this model was trained and tested using longitudinal crack and non-longitudinal crack samples and compared with the decision tree, the gradient boosting decision tree (GBDT) and XGBOOST models. The test results showed that PSO-XGBOOST had the best identification performance in all evaluation indexes. The accuracy, F1 score and alarm rate results were 95.8%, 92.3% and 100%, respectively, and the false alarm rate was as low as 5.5%. The research results provide a theoretical basis and a reliable model for surface longitudinal crack identification.