Wear Performance and Microstructure Characterization of EN39B Alloy Steel: An Integrated Approach Using Experimental Testing and Machine Learning

This study investigates the wear performance of carburized EN39B alloy steel, a very rarely explored aerospace gear material, using a hybrid Machine Learning-Particle Swarm Optimization (PSO) approach for predictive modelling and process optimization. An L16 orthogonal array-based experimental design was adopted, and wear data were obtained using a Pin-on-Disc tribometer. The experimental results revealed minimum and maximum wear values of 93 and 418 microns respectively. The results of ANOVA revealed that speed is a significant influencing input parameter with a contribution of 77.67%, followed by load with a contribution of 16.26% on the variation of output characteristics. The Gradient Boosting (GB) model achieved an R 2 regression coefficient of 0.9977, indicating the model's adequacy and efficiency in the present study. The PSO algorithm was used to optimize wear parameters, resulting in an optimal configuration (Speed = 556.47 RPM, Load = 29.92 N, Time = 85.59 min, Track Diameter = 25.57 mm), which resulted in a predicted wear value of 92.4 microns. 3D surface plots and heatmaps revealed nonlinear interactions between the parameters, with speed and load being the major influencing parameters. Microstructural characterization of the selected samples and optimized run revealed transitions in wear mechanisms. An integrated analytical framework was developed where multi-objective optimization is combined with multidimensional parameter sensitivity and thermodynamic energy-flow modelling, which mapped complex input-output relations resulting in a unified P.V (Contact pressure vs Sliding Velocity) phase map that clearly delineates distinct wear regimes using a non-dimensional Severity Index. These findings provide a comprehensive framework for predictive wear analysis that can be employed on conventional materials used for aerospace applications.