Gears are essential machine elements that are exposed to heavy loads. In some cases, gearboxes are critical elements since they serve as machine drivers that must operate almost every day for a more extended period, such as years or even tens of years. Any interruption due to gear failures can cause significant losses, and therefore it is necessary to have a monitoring system that will ensure proper operation. Tooth surface damage is a common occurrence in operating gears. One of the most common types of damage to teeth surfaces is pitting. It is necessary for normal gear operations to regularly determine the occurrence and span of a damaged tooth surface caused by pitting. In this paper, we propose a machine vision system as part of the inspection process for detecting pitting and monitoring its progression. The implemented inspection system uses a faster R-CNN network to identify and position pitting on a specific tooth, which enables monitoring. Prediction confidence values of pitting damage detection are between 99.5–99.9%, while prediction confidence values for teeth recognized as crucial for monitoring are between 97–99%.

Friction losses and scuffing failures are interesting research topics for worm gears. One of the factors leading to scuffing is the heat generated in the contact of gear teeth. The contact geometry of worm gears is complex, leading to high friction between contact surfaces. High friction between contact surfaces during operation generates heat friction that causes the occurrence of scuffing, which in turn determines the scuffing load capacity. To analyse the thermal characteristics of a worm-gear pair and the thermal behaviour of contact teeth, a direct-coupled thermal–structural 3D finite element model was applied. The heat flux due to friction-generated heat was determined on the gear tooth to investigate thermal characteristics and predict transient temperature fields. This study permits an in-depth understanding of the temperature fields and the friction heat generation process. Also, better control of the contact pattern between worm-gear teeth would decrease friction heat and increase scuffing load capacity. This paper investigates the transient thermal behaviour among different pinion machine setting parameters that can result in an optimal tooth-contact pattern that produces a lower temperature field, thus achieving higher transmission efficiency.

Battery powered outdoor robot Unmanned Ground Vehicles (UGV) should have minimal mass for designed payload capacity to facilitate their movement on rough terrain and increase their autonomy. On the other hand, stiffness and strength of their mechanical components should be sufficient to sustain operational loads. The paper presents the case study of robot UGV lightweighting based on UGV digital twin data. The process of creation of robot UGV digital twin is described, as well as the type and quantity of data acquired from robot sensors. Based on the data from the digital twin of the robot about operational loads, the worst operational load cases were identified and the structural analysis of the robot chassis for noted load cases was performed. The results of the analysis were used in the topology optimization of the robot UGV mechanical components with a goal to satisfy design requirements and reduce the robot mass. By application of noted procedure the mass reduction of approximately 17 % was achieved.

Planetary gear trains offer numerous advantages over traditional gear systems, including high efficiency, the ability to handle large torque loads, and significant reductions in mass and size for the same torque capacity. However, their relatively complex design necessitates the use of optimization techniques to identify the most suitable configurations for specific applications. A key requirement for effective optimization is a mathematical model that accurately captures the essential operational characteristics of the system. Moreover, the optimization process must account for multiple, often conflicting, objectives. This paper focuses on the multicriteria optimization of a three-stage planetary gear train intended for use in a road vehicle winch. The development of the optimization model involves defining the objective functions, decision variables, and constraints. Optimization criteria were based on the following characteristics: overall volume, mass, transmission efficiency, and the production costs of the gear pairs. In addition to identifying the group of solutions that are Pareto optimal, the model employs the weighted coefficient method to select a single optimal solution from this set. The selected solution is then analyzed through simulation to assess potential gear failure scenarios. By combining optimization techniques with simulation and contact analysis, this study contributes to improving the reliability of planetary gear transmissions.

The paper presents the design and engineering rationale behind AgAR, a multipurpose robotic platform developed to address the evolving needs of digital agriculture. The AgAR platform represents a significant advancement in unmanned ground vehicles (UGVs) by integrating a hybrid active-passive suspension system that ensures dynamic leveling and adaptable chassis geometry, enabling safe operation on complex terrains including steep inclines up to 30°. The paper first provides a comprehensive review of state-of-the-art UGVs in agriculture, analyzing mobility strategies, suspension concepts, powertrain options, and implement integration. Building on these insights, the AgAR platform is introduced with a focus on mechanical design, emphasizing modularity, high torque electric drivetrains, standardized agricultural implement compatibility, and fast battery swapping to enable long operational autonomy. AgAR’s structural components were optimized using a digital twin approach to reduce weight while maintaining strength, thereby increasing efficiency and reducing soil compaction. Comparative analysis demonstrates that AgAR uniquely combines terrain adaptability, multi-task versatility, and system level robustness. These attributes position AgAR as a scalable, cost-effective solution suitable for a wide range of agricultural operations, from precision monitoring to heavy implement deployment. The paper concludes by identifying future design trends in agricultural UGVs informed by the AgAR development process.

This study explores the potential benefits of the brick-layer deposition pattern in FDM 3D printing, focusing on its influence on inter-layer adhesion. Standard tensile test specimens conforming to the ISO 527-2/1B geometry were printed using a Bambu Lab X1 Carbon 3D printer with PLA filament. All printing parameters—such as layer height, speed, and temperature—were kept constant, with the only variables being the deposition pattern (conventional vs. brick-layer) and extrusion flow rate (default, +5%, and +10%). A total of six sets of specimens (five per set) were printed vertically to emphasize layer adhesion characteristics. Mechanical testing was conducted via uniaxial tensile testing to failure on universal testing machine. Preliminary results indicate improved performance in specimens printed using the brick-layer method, particularly when combined with increased flow rates. These findings suggest that the brick-layer strategy, though currently underutilized, holds promise for enhancing part strength in FDM printing—warranting further investigation.