The article deals with the quality of the driving characteristics of a passenger car with passive shock absorbers depending on the tire pressure. The work was solved using experimental methods using the AHS test bench. The main goal of the work was to assess the vehicle suspension system using acceleration sensors and pressures between the wheel and the road using shock absorber test benches using the EUSAMA and CAP methodology. The results of the work demonstrated the possibilities of using the measurement of acceleration values in selected places in the vehicle. The obtained results were also verified for the possibilities of further development in the area of reducing the dynamic load when driving a passenger car on the road.
This paper proposes a solution not previously used in the forging industry, which aims to reduce the proportion of arduous human labour. The concept of a prototype robotic station for hot forging includes a system that allows the selection of batch material with its heating, the execution of the process of lubrication of forging tools and the forging itself, synchronised with the feeding and removal of material using full automation, in accordance with the idea of Industry 4.0. At the same time, by increasing the repeatability of the entire forging process and changing some of its key parameters, it will be possible to influence the durability of the tools used during its implementation. In order to verify the impact of such a modified technological process on forging tool life, computer simulations of forging were performed, where the currently applied technology using hand forging was compared with a conceptual automated process.
The proposed model of the neural network (NN) describes the optimization task of the water meter body assembly process, based on 18 selected production parameters. The aim of this network was to obtain a certain value of radial runout after the assembly. The tolerance field for this parameter is 0.2 mm. The repeatability of this value is difficult to achieve during production. To find the most effective networks, 1000 of their models were made (using various training methods). Ten NN with lowest errors of the output value prediction were chosen for further analysis. During model validation the best network achieved the efficiency of 93%, and the sum of squared residuals (SSR) was 0.007. The example of the prediction of the value of radial runout of machine parts presented in this paper confirms the adopted statement about the usefulness of the presented method for industrial conditions and is based on the analysis of hundreds of thousands of parametric and descriptive data on the process flow collected to create an effective network model.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.