Additive manufacturing (AM) technology refers to the process of producing 3D objects by adding material in successive layers. Fused deposition modeling (FDM) is one of the AM technologies where objects are built by adding layers of melted thermoplastic filament onto the printing surface. Mechanical properties of FDM printed part depend on many influencing factors such as material composition, extruding temperature, printing parameters and environment temperature.

Mechanisms of machines completing a repeating operation have many applications in the processing industry. During the working cycle, the output link of the mechanism has one or more periods of dwell, while the input link moves continuously. The paper presents the graphical synthesis of 6-bar linkage mechanism with high class structure groups. The procedure is based on the synthesis at the extreme positions of the mechanism, simplifying the task to a problem with a finite number of precise positions. At the core of this procedure are simple, easily visible and understandable geometric constructions that are then used to form equations suitable for further analysis and optimization. Motion of the mechanism resulting from the synthesis has some inaccuracy - the output link moves during the dwell phase, completing an angle of ±0.5°, which is considered accurate enough for a wide range of practical applications.

The deep groove ball bearing is one of the most important components of the rotary motion system and is the research subject in this paper. After factory assembly, new ball bearings need to pass quality control. The conventional approach relies on measuring the vibration amplitudes for each unit and sorting them into classes according to the vibration level. In this paper, based on experimental research, models are created to predict the vibration class and analyze the dynamic behavior of new ball bearings. The models are based on artificial neural networks. A feedforward multilayer perceptron (MLP) was applied, and a backpropagation learning algorithm was used. A specific method of training groups of artificial neural networks was applied, where each network provided an answer to the input within the group, and the final answer was the mean value of the answers of all networks in the group. The models achieved a prediction accuracy of over 90%. The main aim of the research was to construct models that are able to predict the vibration class of a new ball bearing based on the geometric parameters of the bearing rings. The models are also applied to analyze the influence of surface roughness of the raceways and the internal radial clearance on bearing vibrations.

Universal gear reducers are quite simple products, so today there are many manufacturers around the world that produce this kind of reducers. Their connecting dimensions and shape are not defined by the standard, so there is a large number of solutions. The most of manufacturers produce gear reducers with connecting dimensions and axis height, usually the same like the biggest manufacturers do (mainly company SEW), because they want to provide interchangeability of their gear reducers. It is interesting to note that most manufacturers are trying to adopt such a form of reducer that will be different from competing solutions in order to avoid an accusation of coping with someone else's solution, although manufacturers have almost identical shape and competing (copied) solutions. The paper presents, due to the volume of matter, only the analisys of forms of the housing of modern single-stage universal gear reducers in order to point out the expected directions of their further development.