In this study, an acoustic behaviour of S-polymer gears made of the material combination POM/PA66 was investigated and compared to the standardised involute gears (E-gears). Basic evaluating characteristics included noise during operation, which is of particular significance when noise reduction is expected. The measured signals were analysed in time and frequency domains and the levels of acoustic activity were compared. The experimental results have shown that the sound pressure level of both E- and S-polymer gears are proportional to the torque. However, the comprehensive noise evaluation has shown some advantages of S-polymer gears if compared to the E-polymer gears. In that respect, S-polymer gears were found more appropriate for noise reduction of gear drive systems in the case of normal loading and typical drive speed. Future studies in the operating behaviour of S-polymer gears could also cover noise evaluation using new methods of sound signal analysis at different temperatures of gears.

A comprehensive experimental investigation of the noise evaluation of coated spur polymer gears made of POM was performed in this study. The three Physical Vapour Deposition (PVD) coatings investigated were aluminium (Al), chromium (Cr), and chromium nitrite (CrN). The gears were tested on an in-house-developed testing machine under a torque of 20 Nm and at a rotational speed of 1000 rpm. The noise measurements were performed with the tested gear pair on the testing device with a sound-proof acoustic foam used for the acoustic sound-proof insulation. The sound signal was analysed in time, frequency, and time–frequency domains and typical phenomena were identified in the signal. Experimental results showed that the noise level was higher for polymer gears with different coatings if compared to the polymer gears without coatings. With sound analysis in the time–frequency domain, precise degradation of the coatings could be noticed. In future studies, it would be appropriate to use a new method for signal analysis, e.g., high-order statistics and hybrid technique.

The manufacture of lightweight components is one of the most important requirements in the automotive and aerospace industries. Gears, on the other hand, are among the heaviest parts in terms of their total weight. Accordingly, a spur gear was considered, the body of which was configured as a lattice structure to make it lightweight. In addition, the structure was optimized by topology optimization using ProTOP software. Subsequently, the gear was manufactured by a selective laser melting process by using a strong and lightweight material, namely Ti-6Al-4V. This study defeated the problems of manufacturing orientation, surface roughness, support structure, and bending due to the high thermal gradient in the selective laser melting process. To experimentally investigate the benefits of such a lightweight gear body structure, a new test rig with a closed loop was developed. This rig enabled measurements of strains in the gear ring, hub, and tooth root. The experimental results confirmed that a specifically designed and selectively laser-melted, lightweight cellular lattice structure in the gear body can significantly influence strain. This is especially significant with respect to strain levels and their time-dependent variations in the hub section of the gear body.

In industry, metal workpieces are often heat-treated to improve their mechanical properties, which leads to unwanted deformations and changes in their geometry. Due to their high hardness (60 HRC or more), conventional bending and rolling straightening approaches are not effective, as a failure of the material occurs. The aim of the research was to develop a predictive model that predicts the change in the form of a hardened workpiece as a function of the arbitrary set of strikes that deform the surface plastically. A large-scale laboratory experiment was carried out in which a database of 3063 samples was prepared, based on the controlled application of plastic deformations on the surface of the workpiece and high-resolution capture of the workpiece geometry. The different types of input data, describing, on the one hand, the performed plastic surface deformations on the workpieces, and on the other hand the point cloud of the workpiece geometry, were combined appropriately into a form that is a suitable input for a U-Net convolutional neural network. The U-Net model’s performance was investigated using three statistical indicators. These indicators were: relative absolute error (RAE), root mean squared error (RMSE), and relative squared error (RSE). The results showed that the model had excellent prediction performance, with the mean values of RMSE less than 0.013, RAE less than 0.05, and RSE less than 0.004 on test data. Based on the results, we concluded that the proposed model could be a useful tool for designing an optimal straightening strategy for high-hardness metal workpieces. Our results will open the doors to implementing digital sustainability techniques, since more efficient handling will result in fewer subsequent heat treatments and shorter handling times. An important goal of digital sustainability is to reduce electricity consumption in production, which this approach will certainly do.

Automated storage and retrieval systems for long and heavy unit loads (LH AS/RSs) are already applied in industrial environments. However, the existing literature does not provide quantitative analyses of how the load-mass distribution influences energy consumption and energy regeneration. The present study addresses this gap by implementing an analytical model and developing an advanced simulation model that evaluates how the mass distribution of transport unit loads (TULs) affects the energy behaviour of LH AS/RSs. The model considers three velocity profiles under two storage strategies: random storage and class-based storage. The class-based storage strategy incorporates vertical mass-based zoning, in which the storage height of each TUL is assigned according to the statistical distribution of TUL masses. The simulation results show that mass-based zoning can reduce energy consumption by up to 9% for the combined movement of the stacker crane and lifting platform and by up to 11% for the vertical movement of the lifting platform alone. In addition, both the random and class-based storage strategies achieve approximately 35% energy regeneration. These findings provide the first explicit quantification of the energy savings achievable through mass-based vertical zoning in LH AS/RSs and offer practical guidance for warehouse designers and managers on how to leverage TUL mass distribution when selecting storage strategies and configuring storage rack layouts to improve energy efficiency, support sustainability goals, and enhance LH AS/RS performance.

To ensure the viability of polymer materials, given the properties and limitations of polymers, hybrid materials have been developed that blend the features of all included components. Researchers have not explored the impacts of the length aspect ratio of nanofillers on the mechanical properties of hybrids in great detail previously. Multi-walled carbon nanotubes are a valuable option because they exhibit improved mechanical properties. Using numerical simulation, the impacts of nanofiller content and the size aspect ratio on two base materials—polyamide 6, polyoxymethylene—and their blends, were determined as a function of the volume ratio, the MWCNTs aspect ratio and the base material blend composition. Numerical analysis employed the ANSYS Material Designer. Random samples of chopped-fibre representative volume elements were generated, meshed and analysed by finite element analysis to obtain the Young’s modulus and Poisson’s ratio for each sample. The results showed a generally linear dependence. Rises in both aspect ratio and volume fraction of MWCNTs increased the Young’s modulus up to 46% and decreased the Poisson’s ratio up to 1.6%. The findings suggest that although the impact of the aspect ratio is not as large as that of the volume ratio, longer MWCNTs are preferable.