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YOLO standing for You Only Look Once is one of the most famous algorithms in computer vision used for detecting objects in a real-time environment. The newest version of this algorithm, namely YOLO with the seventh version or YOLOv7, is proposed in the present study for cashew nut detection (good, broken and not peeled) in packaging and quality inspection lines. Furthermore, this algorithm using an efficient convolutional neural network (CNN) to be able to successfully detect and identify unsatisfactory cashew nuts, such as chipped or burnt cashews. In order to deal with the quality inspection process, a new dataset called CASHEW dataset has been built at first by collecting cashew images in environments with different brightness and camera angles to ensure the model's effectiveness. The quality inspection of cashew nuts is tested with a huge number of YOLOv7 models and their effectiveness will also be evaluated. The experimental results show that all models are able to obtain high accuracy. Among them, the YOLOv7-tiny model employs the least number of parameters, i.e. 6.2M but has many output parameters with higher accuracy than that of some other YOLO models. As a result, the proposed approach should clearly be one of the most feasible solutions for the cashew's quality inspection.

Fine-grinding is a final machining method used to reach the low friction and high-quality surface. The workspeed (vw) and crossfeed (vc) motions are perpendicular to each other and differ in value during the grinding process. Thus, the surface quality in workspeed and crossfeed direction is not the same and leads to the anisotropy of friction in the working surface. This paper presents the results of the study on the direct effect of grinding parameters on anisotropic surface friction of AISI 1045 Steel. The research was performed on the UCETR-UMT multifunctional test system, and the experimental samples were ground under the workspeed of 15,4; 19.2, and 23 m/min, respectively, and crossfeed of 0.3 m/min. The research results show that the experimental coefficient is 7 to 17% larger than the calculated friction coefficient. The experimental friction force consists of 2 components: X F is friction force according to the measuring motion direction, Y F is the perpendicular anisotropic friction force X F . Total friction ( X F + Y F ) changes 8-18% in the workspeed direction and 15-23% in the crossfeed direction in the experimental region. The value of the anisotropic friction force Fy varied from 0.78% to 41% compared to Fx. Therefore, the anisotropy friction can be expressed by the angle ani =arctgFy/Fx, which depends on the direction of motion during the grinding and the grinding parameters.

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