To address the severe erosion wear of hydraulic turbine blades and other flow-passing components in sediment-laden water flows, this work used 0Cr13Ni5Mo stainless steel as the substrate and prepared a Ni-WC reinforced AlCoCrFeNi high-entropy alloy composite coating by laser cladding. The study focused on intelligent optimization of process parameters and ultrasonic vibration-assisted regulation of microstructure and properties. First, 30 laser cladding experiments were designed via central composite design , with laser power, scanning speed, powder feeding rate, and overlap rate as input variables and dilution rate and surface flatness as output indicators; a random forest prediction model was established. GA, PSO, WOA, and HGA-ACO were respectively employed to optimize the key hyperparameters of RF, and comparative analysis showed that HGA-ACO-RF achieved the highest prediction accuracy. It was further coupled with the NSGA-III multi-objective optimization algorithm to obtain optimal process parameters: laser power 1450 W, scanning speed 4.3 mm/s, powder feeding rate 1.3 r/min, and overlap rate 60%. Experimental verification showed that the prediction error was less than 10%. On this basis, a stepped ultrasonic horn was optimized by finite element simulation, and a 20 kHz ultrasonic-assisted laser cladding system was developed. The effects of ultrasonic power (0, 400, 800, and 1200 W) on the macroscopic morphology, microstructure, and erosion resistance of the coatings were systematically investigated. The results showed that ultrasonic assistance effectively inhibited the agglomeration of Ni-WC particles, promoted the transformation from coarse columnar/dendritic grains to fine equiaxed grains, and improved the microhardness of the coatings. At 800 W, the microstructure was the most uniform, grain refinement was the most significant, and the erosion resistance was the best, with a cumulative mass loss of only 10.3 mg. However, at 1200 W, excessive energy caused defects such as cracks and pores, leading to deteriorated performance. This study established a technical route combining data-driven parameter optimization and ultrasonic physical field assistance, providing a theoretical basis and process reference for surface strengthening and erosion-resistant coating preparation of hydraulic turbine blades and other flow-passing components.
 

laser cladding，machine learning，multi-objective optimization，ultrasound，Erosion resistance