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The influence of regular relief formation modes on the microhardness of the formed groove surface near the apex at the bottom of the groove has been studied. It has been established that the rate of plastic deformation, expressed as the feed rate of the deforming element, has a significant impact on the plastic deformation mechanism, and the microstructure of the formed subsurface layer, as well as on the microhardness of the groove surface. The influence of the type of partially regular reliefs on the degree of plastic deformation was also investigated. It was found that the third type of partially regular relief, which has the highest groove density, provides higher microhardness values than the first and second types. This is explained by the significantly greater density of these type of partially regular relief grooves, which exert a mutual strengthening effect on the surface during formation. The experimental study conducted enabled the derivation of regression equations describing the influence of the feed rate of the deforming element and the type of partially regular relief created on the surface microhardness beneath the lateral ridges and the bottoms of the plastically deformed traces.

The present research is related to the application of artificial intelligence (AI) approaches for classifying surface textures, specifically regular reliefs patterns formed by ball burnishing operations. A two-stage methodology is employed, starting with the creation of regular reliefs (RRs) on test parts by ball burnishing, followed by 3D topography scanning with Alicona device and data preprocessing with Gwyddion, and Blender software, where the acquired 3D topographies are converted into a set of 2D images, using various virtual camera movements and lighting to simulate the symmetrical fluctuations around the tool-path of the real camera. Four pre-trained convolutional neural networks (DenseNet121, EfficientNetB0, MobileNetV2, and VGG16) are used as a base for transfer learning and tested for their generalization performance on different combinations of synthetic and real image datasets. The models were evaluated by using confusion matrices and four additional metrics. The results show that the pretrained VGG16 model generalizes the best regular reliefs textures (96%), in comparison with the other models, if it is subjected to transfer learning via feature extraction, using mixed dataset, which consist of 34,037 images in following proportions: non-textured synthetic (87%), textured synthetic (8%), and real captured (5%) images of such a regular relief.