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. <!----> <!----> <!----> <!---->

This research examines how atmospheric plasma spraying torch power and coating thickness jointly affect the adhesion strength, microstructure, porosity, and flexural behavior of $A{l}_2{O}_3$ coatings on 100Cr6 steel substrates. Optical microscopy, SEM and EDS mapping, 3D surface-roughness analysis, Vickers hardness testing (HV2) on polished cross-sections, and three-point bending of extracted beams were employed to develop a processing–structure–property map. This multi-technique approach enables the cross-validation of processing–structure–property relationships and supports a robust identification of the optimal power–thickness condition by jointly considering porosity (densification), adhesion strength, flexural response and failure mode. All conditions resulted in an average surface roughness Ra of approximately 1.0 µm. Increasing torch power to 45 kW generally reduced cross-sectional porosity, except at 500 µm, where globular pores appeared. Hardness (HV2) increased with power and peaked at the intermediate thickness (500 µm); adhesion up to 63 MPa was recorded for the 300 µm/45 kW coating. Flexural strength was highest at 500 µm and was consistently greater at 45 kW than at 39 kW. Fractography showed a shift in failure mode from interface-driven delamination at 39 kW to more cohesive, tortuous intra-coating cracks at 45 kW, aligned with improved splat bonding and crack-path deflection. An intermediate thickness of 500 µm deposited at 45 kW is thus identified as an optimal condition to balance densification and crack-path tortuosity, leading to enhanced hardness and flexural performance.

Digitalisation of mechanical engineering necessitates the use of predictive tools that are explainable and allow for more rapid analysis. The study began with cold spraying (CS) of high entropy alloy (HEA) coatings, Al0.1-0.5CoCrCuFeNi and MnCoCrCuFeNi coatings at nitrogen gas temperatures of 650, 750, and 850 °C. The surface roughness, Ra, was measured using profilometry and microscopy techniques. The data set comprised 20 samples with varying experimental conditions and seven measured parameters (five input variables and surface roughness (Ra) as the output. The stacking ensemble structure included three base learners, Linear Regression (LR), Extreme Gradient Boosting (XGBoost), and Gaussian Process Regression (GPR) with RidgeCV as the meta-learner. LR was chosen due to its simplicity and interpretability, GPR was chosen due to its ability to capture nonlinear trends with small datasets, and RidgeCV was used to stabilize the model by shrinking coefficients and reducing variance. This ensemble was compared with XGBoost, which is a powerful single machine learning approach. Results indicated that the stacking ensemble performed better than XGBoost in all the regression metrics. XGBoost reached 96.79% accuracy, RMSE of 0.22 µm, MAPE of 3.32%, and R2 of 0.83, whereas the stacking ensemble achieved 97.26% accuracy, RMSE of 0.17 µm, MAPE of 2.73%, and R2 of 0.89. The results validate that stacking ensembles can improve predictive and robust performance even in scenarios of small data. These novel stacking-ensemble and analysis methods can be broadened for future applicability.

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.