The aim of this work is to verify the applicability of thermography as a non-destructive technique to quantify the wear performance of several high-entropy alloy coatings. Thermal profiles obtained from passive and active thermography were analyzed and the results were correlated with the classical tribological approaches defined in standards. HEA coatings made of several chemical compositions (Al_xCoCrCuFeNi and MnCoCrCuFeNi) and realized by using different cold spray temperatures (650 °C, 750 °C, and 850 °C) were tested in a pin-on-disk configuration, with a dedicated pin developed for the wear tests. Then, the wear performances of each sample were analyzed with the hardness and wear parameter results. The thermal profiles of passive and active thermography allowed a complete characterization of the wear resistance and performance analysis of the coatings analyzed. The results are also compared with those presented in the literature.

Recent research has indicated that Al_0.1–0.5CoCrCuFeNi and MnCoCrCuFeNi high-entropy alloys (HEAs) exhibit superior mechanical and thermal properties under extreme conditions. This chapter provides an account of the wear and surface characteristics of cold-sprayed HEA coatings at various temperatures. The inputs of surface roughness and volume variation are analyzed by analysis of variance (ANOVA), employing formulas optimized by genetic algorithms. Gaussian process regression, support vector regression (SVR), and artificial neural networks are machine learning (ML) methods that predict surface roughness and volume variation with high accuracy. For surface roughness, SVR achieves a coefficient of determination of 0.97, which is lower than that determined from the other models. Furthermore, all three models achieve a coefficient of determination of 0.99 for volume variation. The results indicate the ability of ML to generalize effectively across datasets, capturing nonlinear patterns with precision. These findings emphasize the potential of HEAs for high-wear applications and the reliability of predictive modeling. <!----> <!----> <!----> <!---->

In mechanical engineering, predictive tools are increasingly used to enable faster analysis. The study began with cold spraying of high-entropy alloy coatings, Al _0.1-0.5 CoCrCuFeNi and MnCoCrCuFeNi coatings at nitrogen gas temperatures of 650°C, 750°C, and 850°C. The surface roughness, Ra, as the output, was measured using profilometry and microscopy techniques. The experimental sample set consisted of 20 samples of different experiments. The stacking ensemble structure included three base learners: Linear Regression, Extreme Gradient Boosting, and Gaussian Process Regression with RidgeCV as the meta-learner. This ensemble was compared with Extreme Gradient Boosting, which is a powerful single machine learning approach. Results indicated that the stacking ensemble performed better than Extreme Gradient Boosting in all the regression metrics. Extreme Gradient Boosting reached RMSE of 0.22 µm, MAPE of 3.32%, and R ² of 0.83, whereas the stacking ensemble achieved RMSE of 0.17 µm, MAPE of 2.73%, and R ² of 0.89. A sensitivity analysis was used to qualitatively assess the influence of input variables. The results suggest that stacking ensembles can improve predictive performance even in scenarios of small data.

Surface coatings are essential for enhancing the mechanical and functional properties of materials. Among these, annealed high-entropy alloy (HEA) coatings have gained attention for improving wear resistance and durability. This study comprehensively analyzes HEA-annealed coatings, focusing on their surface roughness and wear behavior. A systematic and thorough approach is employed to examine the impact of annealing on coating characteristics. The research involves depositing Al _0.1–0.5 CoCrCuFeNi and MnCoCrCuFeNi coatings using the cold spray (CS) method, followed by a controlled annealing process. Surface roughness is evaluated through profilometry and microscopy techniques to assess modifications due to annealing. Tribological tests are conducted to investigate the wear performance of the coatings, and the findings are correlated with roughness measurements, offering insights into the relationship between surface texture and wear resistance.