Experimental data on the generation of compressive residual stresses via shot peening and subsequent heat treatment in SAE 5160 and DIN 51CrV4 spring steels

This dataset addresses experimental measurements of compressive residual stresses introduced by shot peening in spring steels, specifically SAE 5160 and DIN 51CrV4 types. Leaf spring pack, which are vital components in suspension systems, intrinsically depend on these near-surface stresses to prolong their fatigue life. The practical problem investigated lies in the potential reduction or redistribution of these beneficial residual stresses during subsequent thermal processes, such as the heating required for paint curing, simulated here at temperatures of 80, 160, and 200 °C with varying exposure times. The experimental methodology employed a full factorial design of experiments to investigate the influence of post-peening heating, totaling 144 prepared samples and considering four independent factors: Steel Type (SAE 5160 and DIN 51CrV4), Turbine Current (45 A and 50 A), Peening Time (7 s and 13 s), and Post-heating time (0 min and 30 min). Residual stress measurements (surface and in-depth profile) were performed using X-Ray Diffraction. The residual stress profiles were obtained by measuring the stress at successive depths, achieved by removing material layers via controlled electrolytic polishing; these profile measurements extended from the surface to a depth of 0.50 mm at 0.05 mm intervals. The dataset is structured into five main folders: raw data folders with surface residual stress measurements for shot-peened samples and shot-peened-plus-heated samples, detailing processing parameters like material type, shot peening current and time, heating temperature and soak time, along with the measured residual stress in megapascals [MPa] and its measurement error; a folder with sample depth measurement profiles containing raw data for the residual stress depth profiles; and auxiliary folders providing pre-plotted graphical data for visualization stratified by steel type, current, peening time, and temperature. This dataset adds significant value to the research field of mechanical design for heavy vehicles subject to fatigue; by providing complex, raw quantitative data, it is highly reusable for validating and calibrating numerical models in materials engineering and fatigue studies, enabling other researchers to model the stability and thermomechanical relaxation of residual stresses.