Surface quality is crucial for precise micro-Vickers hardness measurements. Current standards provide limited quantitative guidance on acceptable surface roughness levels. This study examines the impact of varying surface roughness levels on measured hardness values and measurement accuracy for two materials: aluminium alloy 6082-T651 and stainless steel Uddeholm Ramax HH. For each material, six samples were prepared with distinct surface roughness levels (Sa from ~ 0.02 to 0.72 µm) and characterised by 3D surface texture measurements. Microhardness tests were conducted across a range of applied loads from 0.005 to 1 kgf. Higher surface roughness (Sa > ~0.2 µm) led to increased measurement variability, and larger differences in indentation diagonal lengths—effects most pronounced at test loads ≤ 0.1 kgf—whereas ISO 6507-1:2023 precision (coefficient of variation < 5%) was consistently achieved when roughness was reduced to Sa ≤ 0.05 µm for the aluminium alloy and Sa ≤ 0.07 µm for the stainless steel. A quantitative threshold based on the ratio of roughness to indentation depth (Sa/h) is proposed, offering practical limits for surface preparation. These findings highlight the importance of surface quality and provide new insights for enhancing the reliability of microhardness measurements in materials characterisation.

Micro-Vickers hardness measurements are essential for evaluating micro-scale material hardness, providing insights into surface properties and material strength. However, many factors can influence the precision of hardness measurements. This study examines the influence of surface roughness on the results of micro-Vickers hardness measurements. A polished stainless steel gage block is used as a test sample. Sample surfaces are textured using different grit sandpapers. The surface texture was analysed using the 3D surface roughness measuring system. A test matrix with eight different test forces ranging from 0.01 to 0.5 kgf was used for micro-Vickers hardness measurements. The measurement results indicated that a small change in the surface roughness impacts the measurement results with a more significant effect when applied test forces are below 0.1 kgf. Obtained surface hardness values increase with surface roughness, accompanied by a rise in the standard deviation of the measurements.

This article discusses the micromechanical properties and true microhardness determination of nanostructured tribological coatings (NTCs) based on a multilayered alternating nitride/carbonitride bilayer substructure for transition metals. The constituent nitride/carbonitride bilayers in the superlattice structure of the NTC were alloyed with refractory metals, denoted as Me = Me₁ or Me₂= Cr, Hf, Nb, W, and Zr. The resulting NTC coatings were deposited onto 100Cr6 steel substrates using an advanced physical vapor deposition (PVD) technique, referred to here as high-power ion-plasma magnetron sputtering (HiPIPMS). The comprising crystalline nanometer-scale TiAlSiMe₁-N/TiMe₂-CN nanoparticles strengthened by Me additives significantly increased the NTC microhardness to over 3200 HV. The primary focus of this research was to determine the true microhardness of the NTC film samples. The apparent microhardness (H_a) of the film/substrate system for various NTC samples was measured during microindentation testing using the Vickers method. Nine NTC samples were tested, each generating a corresponding microindentation dataset containing between 430 and 640 imprints, depending on the specific NTC sample. These datasets were analyzed using three distinct empirical approaches: (i) the inverse power-law model (IPL-Model), (ii) the sigmoid-like decay model (SLD-Model), and (iii) the error function model (ERF-Model). The observed solid correlation between the proposed models and experiments suggests that the true microhardness estimates (H_f) obtained through the empirical mathematical modeling approach are reliable.

Laser surface hardening (LSH) is an efficient and flexible technique for improving the surface integrity of steel components used in high-load automotive applications. In this study, the surface changes occurring during laser hardening of 42CrMo4 steel were systematically investigated using a 4 kW high-power diode laser. The influence of laser power and scanning speed on surface roughness, hardness distribution, hardened layer depth, tribological behavior, and phase composition was analyzed. Surface topography was evaluated using three-dimensional laser scanning microscopy, while mechanical performance was assessed through microhardness and scratch testing. Phase transformations and residual structural changes were examined by X-ray diffraction (XRD) at different depths beneath the treated surface. The results demonstrate that laser processing parameters strongly affect surface integrity through competing mechanisms of surface melting, oxidation, and self-quenching. High laser power combined with low scanning speed produced deep hardened layers but promoted surface melting and retained austenite formation, whereas lower power and higher scanning speed yielded a stable martensitic surface with reduced roughness and a steep hardness gradient. XRD analysis confirmed that oxide formation was limited to the near-surface region, while the subsurface hardened zone consisted predominantly of martensitic/bainitic phases. An optimal processing window was identified that balances surface hardness, roughness, and microstructural stability without compromising surface integrity. These findings provide practical guidelines for optimizing diode laser hardening of 42CrMo4 steel gears in industrial automotive applications.

This study investigates how data-processing decisions influence ISO 25178 areal surface roughness parameters and examines which of those parameters most reliably characterise surfaces used in micro-Vickers hardness testing. Six processing variants—two form-removal polynomial degrees, two resampling densities, and two L-filter cut-off wavelengths—were applied independently to measurements of stainless steel and aluminium alloy surfaces prepared at three roughness levels ( S _a = 0.014–0.42 µ m). Parameter sensitivity to processing was found to depend strongly on surface roughness level, with smooth surfaces exhibiting deviations an order of magnitude larger than rough surfaces under identical processing changes. Among the processing variants examined, the L-filter cut-off wavelength was identified as the dominant source of parameter variation, followed by form-removal polynomial degree and resampling density. S _a , S _q , S _k , and S _pk showed the strongest correlations with hardness values ( r = 0.79–0.96), with Vmp providing supplementary correlation ( r = 0.80–0.91). On smooth surfaces ( S _a < 0.02 µ m), no parameter remained within acceptable deviation bounds under any λ c variation, identifying strict filter standardisation as an absolute requirement at this roughness level. Consistent documentation of processing methodology is essential for valid cross-study comparison of surface texture–hardness relationships.