In the paper, the authors present the solution aimed at increasing reliability of the conveyor units. The analysis of technological and operational defects of conveyor rollers is presented. The changes in manufacturing technology have been proposed, which allowed for avoiding welding and provided the required level of tightness. Computer simulation of the motion of air in the labyrinth seal of the roller was conducted to determine the numerical parameters of possible airflows. It is proved that the airflow is present in the gap of the labyrinth seal due to the roller rotation. It is shown that the reason for the penetration of abrasive particles through the labyrinth seal after stopping is decompression, which occurred as a result of temperature change and push out of airflows during rotation. It is also suggested that the number of stops during the operation should be taken into account when determining the durability of rollers. Practical recommendations are given for preventing the penetration of abrasive particles during conveyor stops and the need for combined seals. The results can be used for the construction of roller conveyor belts in any industry.
The presented paper concerns CFD optimization of the straight-through labyrinth seal with a smooth land. The aim of the process was to reduce the leakage flow through a labyrinth seal with two fins. Due to the complexity of the problem and for the sake of the computation time, a decision was made to modify the standard evolutionary optimization algorithm by adding an approach based on a metamodel. Five basic geometrical parameters of the labyrinth seal were taken into account: the angles of the seal’s two fins, and the fin width, height and pitch. Other parameters were constrained, including the clearance over the fins. The CFD calculations were carried out using the ANSYS-CFX commercial code. The in-house optimization algorithm was prepared in the Matlab environment. The presented metamodel was built using a Multi-Layer Perceptron Neural Network which was trained using the Levenberg-Marquardt algorithm. The Neural Network training and validation were carried out based on the data from the CFD analysis performed for different geometrical configurations of the labyrinth seal. The initial response surface was built based on the design of the experiment (DOE). The novelty of the proposed methodology is the steady improvement in the response surface goodness of fit. The accuracy of the response surface is increased by CFD calculations of the labyrinth seal additional geometrical configurations. These configurations are created based on the evolutionary algorithm operators such as selection, crossover and mutation. The created metamodel makes it possible to run a fast optimization process using a previously prepared response surface. The metamodel solution is validated against CFD calculations. It then complements the next generation of the evolutionary algorithm.