Optimization of thermal stress on the top surface of a particle-reinforced composite coated piston based on Kriging model

To further enhance the thermal-mechanical performance of the improved piston, a SiC-reinforced Ni-based composite coating was used to alleviate the thermal load on the piston crown. A 3D-printed Ti-6Al-4V (TC4) diesel piston was selected as the research object. Boundary conditions were established based on the unique structure of the combustion chamber, leading to improvements in the Random Sequential Adsorption (RSA) method. A mesoscopic mechanical model of composite coated piston was constructed. The influences of the coating thickness, SiC particle size, and volume percentage on the high-temperature mechanical performance of the coating were analyzed. Based on the Kriging agent model and the Particle Swarm Optimization (PSO) algorithm, a thermal stress optimization model was developed with the Latin hypercube sampling method. The optimal peak stress on the piston top surface was simulated to find the best technological parameters of the particle-reinforced composite coating. The results show that the optimal coating parameters consist of a thickness of 3.03 μm, a volume percentage of 5.09 vol%, and a particle size of 435.70 nm. The peak stress was reduced by 9.18 MPa, representing a 2.35 % reduction compared to the uncoated piston. The results can provide a reference for applying the particle-reinforced composite coating to improve the thermal performance of the piston.