PREDICTION OF TEMPERATURE DISTRIBUTION IN THE WORM GEAR MESHING

The selection of a rubber compound has a determining influence on the final characteristics of rubber-metal springs. Therefore, the correct selection of a rubber compound is a key factor for development of rubber-metal vibration isolation springs with required characteristics. The procedure for the selection of the rubber compound for vibration isolation of rubber-metal springs has been proposed, so that the rubber-metal elements have the necessary characteristics, especially in terms of deflection. The procedure is based on numerical simulation of spring deflection with Bergström-Boyce constitutive model in virtual experiment, with a goal to determine which parameters of the constitutive model will lead to spring required deflection. The procedure was verified by case study defined to select rubber compound for a rubber–metal spring used in railway engineering.

Appropriate lubrication of the rolling bearing is needed to lower the friction between the surfaces in mutual contact and their wear. A lubricant should completely separate the rolling elements from the raceways. The values that affect the efficiency and regimes of lubrication are analyzed in the paper, after which it is introduced a lubrication regime coefficient. This coefficient makes it possible to choose in a simple and fast manner an optimum combination of rolling bearing and lubricant based on the known shaft speed, external load, and rolling bearing operating temperature. For certain bearings with radial contact and certain lubricants, the lubrication regime dependence on the shaft speed and contact load is shown.

The reliability study is the most important part of the engineering design process, as it is the basis of analysis and assessment of future product performance in exploitation. Since performance cannot be predicted with absolute certainty, the application of reliability theory includes probability theory and unreliability modeling. The proposed approach has been applied to assess the reliability of gear planetary power transmissions. The assessment of system reliability was determined on the basis of the block diagram method, as a function of the reliability of individual components, calculated by statistical analysis. Using the Weibull model, the reliability of the planetary gear was defined on the basis of the probability of failure of the gear teeth and the results were interpreted to assess the reliability of the component and the entire planetary train. For a more precise assessment of reliability and to avoid modeling every failure and mode of occurrence, a competitive risk model was developed. The reliability assessment study was conducted with a “bottom-up” approach. Reliability has been assessed, for instantaneous, estimated and assigned failures rate of planetary train and component.

Air-cooled condensers in thermal power plants have recently become increasingly popular. Besides all the advantages they have, like no demands for water supply on the plant site and no need for taking care of environmental regulations, they also have some serious disadvantages. One of the biggest disadvantages air-cooled condensers do have is precisely the nature of the Earth?s atmosphere being their low temperature reservoir. Low density and low heat capacity of the air as the cooling medium combined with extremely stochastic behavior of the atmosphere itself put some serious challenges in front of the air-cooled condenser?s proper and steady functioning. In this paper, the operating parameters of the air-cooled condenser in the chosen thermal power plant were investigated to gain a clearer insight into the influence of the atmospheric changes on its entropy generation and consequently on its efficiency. Also, the acquired results were further proposed as a starting point for potential optimization of the process inside the device.

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.