Abstract
Hardness is one of the most crucial mechanical properties, serving as a key indicator of a material's suitability for specific applications and its resistance to fracturing or deformation under operational conditions. Machine learning techniques have emerged as valuable tools for swiftly and accurately predicting material behavior. In this study, regression methods including decision trees, adaptive boosting, extreme gradient boosting, and random forest were employed to forecast Vickers hardness values based solely on scanned monochromatic images of indentation imprints, eliminating the need for diagonal measurements. The dataset comprised 54 images of D2 steel in various states, including commercial, quenched, tempered, and coated with Titanium Niobium Nitride (TiNbN). Due to the limited number of images, non-deep machine learning techniques were utilized. The Random Forest technique exhibited superior performance, achieving a Root Mean Square Error (RMSE) of 0.95, Mean Absolute Error (MAE) of 0.12, and Coefficient of Determination (R2) ≈ 1, surpassing the other methods considered in this study. These results suggest that employing machine learning algorithms for predicting Vickers hardness from scanned images offers a promising avenue for rapid and accurate material assessment, potentially streamlining quality control processes in industrial settings.