Unveiling Surface Roughness Trends and Mechanical Properties in Friction Stir Welded Similar Alloys Joints Using Adaptive Thresholding and Grayscale Histogram Analysis

Surface roughness plays a vital role in determining surface integrity and function. Surface irregularities or reduced quality near the surface can contribute to material failure. Surface roughness is considered a crucial factor in estimating the fatigue life of structures welded by FSW. This study attempts to provide a deeper understanding of the nature of the surface formation and roughness of aluminum joints during FSW processes. In order to form more efficient joints, the frictional temperature generated was monitored until reaching 450 °C, where the transverse movement of the tool and the joint welding began. Hardness and tensile tests showed that the formed joints were good, which paved the way for more reliable surface roughness measurements. The surface roughness of the weld joint was measured along the weld line at three symmetrical levels using welding parameters that included a rotational speed of 1250 rpm, a welding speed of 71 mm/min, and a tilt angle of 1.5°. The average hardness in the stir zone was measured at 64 HV, compared to 50 HV in the base material, indicating a strengthening effect induced by the welding process. In terms of tensile strength, the FSW joint exhibited a maximum force of 2.759 kN. Average roughness (Rz), arithmetic center roughness (Ra), and maximum peak-to-valley height (Rt) were measured. The results showed that along the weld line and at all levels, the roughness coefficients (Rz, Ra, and Rt) gradually increased from the beginning of the weld line to its end. The roughness Rz varies from start to finish, ranging between 9.84 μm and 16.87 μm on the RS and 8.77 μm and 13.98 μm on the AS, leveling off slightly toward the end as the heat input stabilizes. The obtained surface roughness and mechanical properties can give an in-depth understanding of the joint surface forming and increase the ability to overcome cracks and defects. Consequently, this approach, using adaptive thresholding image processing coupled with grayscale histogram analysis, yielded significant understanding of the FSW joint’s surface texture.