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 ($R^2$) ≈ 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.

Hardness is one of the most important mechanical properties of materials, since it is used to estimate their quality and to determine their suitability for a particular application. One method of determining quality is the Vickers hardness test, in which the resistance to plastic deformation at the surface of the material is measured after applying force with an indenter. The hardness is measured from the sample image, which is a tedious, time-consuming, and prone to human error procedure. Therefore, in this work, a new automatic method based on image processing techniques is proposed, allowing for obtaining results quickly and more accurately even with high irregularities in the indentation mark. For the development and validation of the method, a set of microscopy images of samples indented with applied forces of $5N$ and $10N$ on AISI D2 steel with and without quenching, tempering heat treatment and samples coated with titanium niobium nitride (TiNbN) was used. The proposed method was implemented as a plugin of the ImageJ program, allowing for obtaining reproducible Vickers hardness results in an average time of $2.05$ seconds with an accuracy of $98.3\%$ and a maximum error of $4.5\%$ with respect to the values obtained manually, used as a golden standard.

This study focused on investigating the adhesion and tribological properties of niobium-doped titanium nitride (TiNbN) coatings deposited on D2 steel substrates at various substrate temperatures (Ts) under simulated cutting conditions. X-ray diffraction confirmed the presence of coatings with an FCC crystalline structure, where Nb substitutes Ti atoms in the TiN lattice. With increasing Ts, the lattice parameter decreased, and the crystallite material transitioned from flat-like to spherical shapes. Nanoindentation tests revealed an increase in hardness (H) with Ts, while a decrease in the elastic modulus (E) resulted in an improved elastic strain limit for failure (H/E) and plastic deformation resistance (H³/E²), thereby enhancing stiffness and contact elasticity. Adhesion analysis showed critical loads of ~50 N at Ts of 200 and 400 °C, and ~38 N at Ts of 600 °C. Cohesive failures were associated with lateral cracking, while adhesive failures were attributed to chipping spallation. The tribological behavior was evaluated using a pin-on-disk test, which indicated an increase in friction coefficients with Ts, although they remained lower than those of the substrate. Friction and wear were influenced by the surface morphology, facilitating the formation of abrasive particles. However, the absence of coating detachment in the wear tracks suggested that the films were capable of withstanding the load and wear.