Process Optimization and Prediction Model of Laser Cladding CoCrFeNiTi High-entropy Alloy

In laser cladding process, as important factors, cladding parameters directly determine the surface quality and morphology characteristics of coatings. At present, there are few explorers on the impact of cladding parameters on the surface quality and morphology characteristics of CoCrFeNiTi high-entropy alloy coatings. The work aims to investigate how cladding parameters affect the quality and morphology characteristics of CoCrFeNiTi high-entropy alloy coatings. In order to accurately control of geometric morphology of laser cladding, a prediction model for the properties and geometric morphology of the CoCrFeNiTi high-entropy alloy coatings was established based on support vector regression. According to the Taguchi Design experiment scheme in Minitab 19 software, L25(53) orthogonal experiment was schemed. CoCrFeNiTi high-entropy alloy coatings were prepared on the surface of 30CrMnSiA under different laser process parameters. The most influential cladding parameters (laser power, scanning speed, and powder feeding rate) were selected as impact factors and the dilution rate, height, width, crack density and width to height ratio as response targets. The relationship between influencing factors and response targets were analyzed through variance and signal-to-noise ratio method in detail. By comprehensively considering various response objectives the optimized process parameters were obtained. Based on the experimental data, a prediction model for the properties and geometric morphology of the CoCrFeNiTi high-entropy alloy coating was established by support vector regression. The results indicated that the dilution rate, crack density and width of coatings were impacted by laser power, and they all magnified with the increase of the laser power. The height and width to height ratio were affected by scanning speed and powder feeding. The scanning speed and powder feeding rate were positively correlated to the height of cladding layer and negatively correlated to the width to height ratio. The optimal cladding parameters were laser power 600 W, scanning speed 18 mm/s, and powder feeding rate 1.6 r/min. Based on experimental data, the predictive model was tested, the predictive model determination coefficient values of each feature were all greater than 0.93. Especially, the predictive model determination coefficient value of the height of cladding layer was up to 0.989 9. These consequences indicated that there was a good correlation between the predicted consequences of characteristics and the input parameters of the prediction model, the established prediction model could accurately predict the morphology characteristics of the cladding layer. The geometric morphology prediction model of CoCrFeNiTi high-entropy alloy coating based on support vector regression has high prediction accuracy, which can achieve accurate prediction of the morphology of the CoCrFeNiTi high-entropy alloy cladding layer. By using this model, the geometric characteristic parameters of the cladding layer can be obtained prior to its application. By adjusting the process parameters, it is possible to obtain the cladding layer with the least machining, thus enhancing the work efficiency of subsequent cutting processing. This research work gives a guideline for the selection of appropriate cladding parameters for CoCrFeNiTi high-entropy alloy coatings and provides a new idea for controlling the morphology of the cladding layer. © 2024 Chongqing Wujiu Periodicals Press. All rights reserved.