This study presents a numerical investigation of failure initiation and evolution in thin-walled extruded aluminum tubes subjected to three-point bending and dynamic axial compression. An explicit finite element framework is employed, combining the Müschenborn--Sonne forming limit diagram (MSFLD) with stress-state-dependent ductile and shear fracture criteria to capture multiple competing failure mechanisms. The approach accounts for instability-driven necking as well as fracture governed by evolving stress triaxiality and Lode parameter. Numerical predictions are validated against experimental results through both quantitative comparison of load-displacement responses and qualitative assessment of deformation patterns and fracture locations. The results demonstrate that incorporating stress-state-dependent fracture criteria significantly improves the predictive accuracy of crash simulations involving thin-walled aluminum structures.
This paper presents a comparative study between cycloidal drive components manufactured using Fused Deposition Modeling (FDM) with PLA plastic and CNC laser-cut aluminum. Building upon previous work with polymer-based cycloidal reducers, this research investigates the performance characteristics, mechanical properties, and practical applications of precision-machined aluminum cycloidal discs and input shafts for high-torque gear transmission systems. Experimental testing was conducted on 24:1 reduction ratio cycloidal drives with identical geometries but different materials for the cycloidal disc and input shaft components. Key performance metrics including maximum torque capacity and thermal behavior were measured and analyzed through torque testing and thermal monitoring. Improvements in torque capacity were primarily demonstrated by the aluminum construction (1.7× increase) along with trade-offs in manufacturing cost, weight, and production complexity being identified. The aluminum cycloidal disc achieved 31.5 Nm maximum output torque compared to 18.3 Nm for PLA, with substantially better thermal stability. To verify design decisions and understand structural response, finite element analysis was also conducted. This research serves as a knowledge base filled with real-life scenarios that will aid the engineers to make a confident decision in the choice of manufacturing technology for the next robotic and mechatronic devices projects.
The focus of this research is on the geometric design and finite element method (FEM) analysis of a connecting spur gear pair as a part of a mechanical wire bending machine. This goal of this study is to improve the performance of the machine and durability, this will be achieved by optimizing the gear geometry and analyzing stress distribution (Static stress study). Previously, the gear pair was first designed using CATIA and Fusion 360, following standard design parameters like module, center distance and pressure angle. The designed model was analyzed using FEM instruments to model deformation, static stress and contact behavior between teeth. The simulation results were compared with theoretical calculations to validate design accuracy and identify major stress regions. This study provides important insights into enhancing load transmission efficiency, extending gear life and minimizing wear in bending machinery applications.
The application of virtual factory simulation plays a pivotal role in the design and optimization of modern production systems. By enabling the evaluation and refinement of manufacturing processes prior to physical implementation, process simulation significantly enhances decision-making efficiency. Real-time optimization has attracted considerable attention in the process industry and is widely adopted, as it typically relies on external databases and parameter sets. This study investigates the import, use, and management of such external parameters, providing a comprehensive overview. A detailed case study is developed and solved to demonstrate the proposed approach. The results show that process simulation with parameterized models improves system efficiency and enables real-time optimization capabilities.
The local government of Dunaújváros, Hungary, has long recognized the important role environmental protection plays in enhancing the city's competitiveness and attractiveness. Since 1997, the municipality has employed an environmental specialist with a university degree. Environmental status reports, adopted by resolutions of the general assembly, have been published annually since that year. Since 1998, the city has had a municipal environmental protection programme and an environmental financial fund. In 2007, Dunaújváros became the first Hungarian municipality to operate a certified EMAS (Eco-Management and Audit Scheme) environmental management system [6]. The city has been a member of the Covenant of Mayors for Climate and Energy since 2017 [1]. Dunaújváros has participated — and continues to participate — in several European environmental projects and has received numerous national and European Union awards for its achievements in the field of environmental protection. The primary goal of the municipal environmental protection programme is to protect human health, preserve and promote the sustainable use of natural resources and assets. The programme supports the protection and sustainable utilization of individual environmental elements and systems, identifies threats, and aims to resolve and mitigate environmental conflicts in line with the city's characteristics and economic capacities [2]. The newly completed municipal environmental protection programme for the period 2025–2030 is the city's fifth such plan. The requirements concerning the preparation and content of the program are regulated by Act LIII of 1995 on the General Rules of Environmental Protection. The program includes: an assessment of the current situation, based on the condition of environmental elements and analysis of the main influencing factors; environmental protection objectives and target states aligned with sustainable development; the main actions required to achieve these goals (particularly those related to ongoing or planned developments and operations), along with an implementation schedule; regulatory, monitoring, and evaluation tools to support goal achievement; and a breakdown of the expected costs of implementing the measures and tools, including planned funding sources [4].
Motor vehicles are part of our everyday life, and it is difficult to conceive mobility without them as they represent a form of independence. However, conventional vehicles are being transformed not only from a technological perspective, but also from their propulsion systems. This transformation is driven by the scarcity of fossil fuel reserves, geopolitical concerns, environmental pollution, energy transition challenges, and energy independence goals. The electro-mobility of the future will extend beyond electric vehicles to become an integral part of a diverse green energy mix. Hydrogen, with its zero emissions and high energy content, has gained significant attention in this context. The adoption of such new technology by society requires ensuring safety to prevent accidents that could hinder its evolution. High-priority research directions in the hydrogen economy include safety as a technical, psychological, and sociological issue. The safe and effective operation of hydrogen refuelling stations presents numerous challenges due to hydrogen's physical properties, such as its propensity to leak, flammability, and high-pressure storage requirements. Digital twin technology, which creates virtual replicas of physical systems based on real-time data, has significant potential to address these challenges. This study examines how digital twin technology can be implemented to operate hydrogen facilities more safely and effectively. Hydrogen, as a clean energy carrier, plays a key role in sustainable energy systems, but its handling poses substantial safety challenges. This research provides insights into the potential, benefits, and future outlook for digital twin technology in hydrogen refuelling stations, particularly for enhancing safety and reliability. The findings demonstrate that digital twin technology offers considerable potential for safely developing hydrogen infrastructure and may play a crucial role in transitioning to a sustainable energy system. The study describes the four main components of the technology: physical entity, virtual model, data flow, and analytical system, collectively demonstrating practical applications where 3D visualization enables users to quickly identify hazards. The digital twin system significantly improves safety through real-time leak detection and automatic alerts, increases efficiency by optimizing energy consumption and refuelling processes, and enhances reliability through predictive maintenance. However, widespread adoption of this technology faces challenges including managing large volumes of data, accurately modelling complex processes, and implementing effective solutions. Future development directions include integrating quantum computing, deploying advanced sensor systems, and leveraging artificial intelligence, which together may contribute to developing safer and more sustainable hydrogen infrastructure.
The accurate characterization of non-Newtonian fluid pipe flow is essential for engineering applications, such as vibration dampers, and fluid-processing industrial applications. Since traditional measurement setups require circulating significant fluid volumes, this study introduces a novel, compact measurement device developed to determine flow coefficients for a pipe flow using minimal volume of media. This research investigates the pipe flow of shear thickening fluid samples synthesized from PEG200 and fumed silica at 18 wt.% and 24 wt.% concentrations. The experimental results confirmed that increasing the solid volume fraction intensifies the shear thickening characteristics, which causes a sudden increase in pressure loss. The measured values were compared with analytical predictions, and the close agreement between them validates the reliability of this novel measurement technique for characterizing these complex fluids.
Recent years have seen growing faith in data-driven tools for condition monitoring and fault detection, a trend accelerated further by advances in artificial intelligence. In many industrial systems typical examples like chemical plants and wind-energy installations—the underlying process variables do not behave in a stable or predictable manner. Their statistical features shift over time, and conventional monitoring methods which assume an essentially stationary assumption, often struggle to handle high-dimensional signals. Much of the difficulty stems from the fact that several variables move together over long periods but differ in their degree of non-stationarity. To deal with this challenge, two monitoring strategies are designed to react reliably to deviations even when the data show complicated stochastic behaviour. The framework consists of two cointegration-based schemes. Scheme 1 treats all series jointly (i.e., mixed order), regardless of their integration order, while Scheme 2, first separates them into (0), (1), and (2) groups using the augmented DickeyFuller (ADF) test and then each group fed to cointegration model individually. In both cases, the residuals serve as indicators. Monitoring statistics are estimated based on the Mahalanobis Distance (MD), utilizing residuals from testing set; the control limit (CL) is computed based on the Kernel Density Estimation (KDE) utilizing residuals from training set. Any deviation of monitoring statistics crossing the CL highlights the abnormal conditions in the system. Numerical case studies demonstrate the efficacy of non-joint cointegration-based monitoring (Scheme 2), which provides a flexible and computationally efficient method for monitoring non-stationary processes. In comparison to traditional PCA and CA-based Schemes, the Scheme 2 framework has better performance with a lower false alarm.
Enhancing manufacturing efficiency increasingly relies on integrating Overall Equipment Effectiveness (OEE), Industry 4.0 technologies, and Artificial Intelligence (AI) with Machine Learning (ML). Traditionally, OEE aggregates Availability, Performance, and Quality, but in Industry 4.0 it can evolve into a real-time decision-support tool enriched with predictive analytics. This paper provides a conceptual systematic synthesis of the interplay between OEE and AI-driven methodologies, emphasizing the role of fuzzy logic and hybrid models in managing uncertainty. AI/ML applications—predictive maintenance, quality assurance, and process optimization—reduce downtime, minimize scrap, and increase productivity by improving OEE components through pattern recognition and forecasting. A further research focus is domain shift and transfer learning, which impact the scalability and robustness of industrial AI systems across changing equipment and factories. Transfer learning approaches such as fine-tuning, feature alignment, and adversarial adaptation can reduce retraining effort while maintaining performance. Finally, integrating AI with OEE supports sustainable manufacturing by improving energy efficiency and reducing environmental impacts, contributing to the long-term vision of self-optimizing digital factories capable of responding dynamically to market and environmental changes.
This research presents a model for calculating the productivity of metal removal during external cylindrical grinding, taking into account the instability of the technological process during the workpiece operating cycle and during the processing of a set of products. The relationship between the productivity of the grinding process using CNC digital control systems and the cutting forces, the physical and mechanical properties of the machined material, the properties of the grinding stone, the rigidity of the technological system, cutting systems, the characteristics of layer removal in areas opposite the wheel rotation, the achieved machining accuracy, and other technological factors affecting the process.
Vehicle dynamics models play an important role in understanding and predicting vehicle behavior under different operating conditions; however, their practical usefulness strongly depends on how well simulated responses reflect real vehicle performance. For this reason, model validation using real-world measurement data is essential. This study focuses on the validation of a MATLAB–Simulink-based longitudinal vehicle dynamics model using CAN bus data recorded during on-road driving. The measurement data were collected using a CANedge2 data logger, which enables the recording of key vehicle parameters such as vehicle speed, engine speed, brake pressure, and accelerator pedal position. The recorded MF4 files were decoded and processed using the asammdf software framework in combination with appropriate DBC files to extract physically meaningful signals. The measured signals were subsequently compared with the simulation results to assess the model’s ability to reproduce real vehicle behavior under different driving conditions. The presented workflow demonstrates a practical and reproducible approach for CAN-based model validation using real vehicle data and provides a basis for further refinement and extension of longitudinal vehicle models.
This paper briefly summarises the structure of hierarchical shell finite elements and presents computational results for rotationally symmetric shell structures.
Solar photovoltaic systems are the most dynamically expanding renewable energy technology, their capacities have doubled globally over the last two years. The transition to solar energy offers numerous advantages, it poses significant challenges for the electricity grid stability. These systems have high production fluctuation within the day and vast overproduction in peak hours. To improve stability, economic operation and efficiency of electricity network, both fluctuation balancing and excess energy storage are essential. Presently two technologies are mostly used: battery-based and pumped hydropower energy storage systems. This study focuses on evaluating the operating efficiency of solar-driven pumped hydropower energy storage system in the catchment of Lake Velence under changing weather conditions. Lake Velence has been struggling with severe drought problems for years, posing challenges to water experts, and making fair water distribution difficult for water users. The nearby northern hilly region of the lake provides suitable location for municipal-scale pumped hydropower developments in terms of social, techno-economic and topographical conditions. Furthermore, due to scarce water resources, inefficient operation could cause additional tension among water users, therefore, detailed evaluation of system efficiency is required, along with life-cycle economic analysis and hydrodynamic optimization of reservoirs and pipelines. Using potential reservoir locations identified in a case study from 2025 for pumped hydropower energy storage, together with meteorological data, we analyze and optimize in a self-developed Matlab model 1) the hydrodynamics of the system and 2) evaporation losses of reservoirs, on a daily basis over years, under changing weather conditions. Hydrological evaporation and hydrodynamic friction losses, together with possible interventions to reduce their negative effects, were assessed with life-cycle cost analysis to present environmental-social and economic benefits. Changing weather conditions have significant impact on operational efficiency and can negatively affect water requirements that raise conflicts among water users. The solely solar-driven pumped hydropower systems are spreading but still in their early stages of development. Long-term and extended data on their operation is still limited and their performance under changing weather conditions requires further research. This research investigates hydrodynamic and hydraulic operation efficiency and couples with economic life-cycle cost analysis, supporting guidance for optimization and evaluation of future developments.
Accurately predicting real estate values remains challenging in volatile and rapidly growing real estate markets, especially for expensive homes worldwide. Traditional statistical and economic models frequently miss the temporal dynamics and nonlinear dependencies affecting changes in property values. This study uses a large-scale, multi-market dataset to anticipate real estate values utilizing sophisticated deep learning architectures, including Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), and Dense Neural Networks (DNN). To increase learning efficiency, data pretreatment techniques included time-series sequencing, categorical encoding, and normalization. Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and the Coefficient of Determination (R2) were used to assess the model's performance. The findings show that the LSTM model obtained the lowest average prediction error (MAE) and produced extremely accurate forecasts by effectively incorporating long-term temporal dependencies. According to the study's findings, deep learning gives a solid, scalable foundation for accurate property price predictions. It also has practical ramifications for urban planners, investors, and legislators in dynamic housing markets.
This study investigates the influence of fiber and CO₂ laser cutting parameters on the material properties of wear-resistant Hardox 450 steel. The research focuses on the: effect of laser power, assist gas pressure, and cutting speed on the microhardness and surface integrity of the cut material. A full factorial experimental design was employed, consisting of 162 specimens cut under different process conditions. Hardness was evaluated using the Rockwell method, while surface roughness was measured with a Mitutoyo SJ-210 profilometr. The results revealed that gas pressure and laser power have a dominant effect on hardness variation in the heat-affected zone (HAZ), whereas cutting speed significantly affects the surface roughness.A comparative analysis between fiber and CO₂ laser technologies showed that fiber laser cutting produces a narrower HAZ and better preserves the base-material hardness, minimizing microstructural degradation. In contrast, CO₂ laser cutting results in a wider HAZ with slightly reduced hardness but improved smoothness due to longer thermal exposure. Statistical analysis (ANOVA) and regression modelling enabled the identification of key interactions between process parameters and material response. The study contributes to a deeper understanding of the laser–material interaction mechanisms in high-strength steels and provides recommendations for optimizing cutting conditions to maintain the mechanical integrity of wear-resistant materials.
Hedge cutters are electric power tools used to trim vegetation in domestic and professional environments. The cutting performance, along with vibration patterns and mechanical stress on the blades, depends heavily on the eccentric mechanism, which converts rotary motor motion into back-and-forth blade motion. The research investigates AC-powered hedge cutters by studying three commercial devices that maintain constant speed through their different eccentric-disk designs. The research investigates how eccentricity affects blade movement, speed, acceleration, and jerk through both mathematical kinematic modeling and computer-based simulation. The results demonstrate that higher eccentricity values produce greater peak velocities and accelerations, which could improve cutting performance but would also increase dynamic forces and vibration. The comparison provides numerical data that helps understand how eccentric design elements affect the system, enabling better mechanical design and optimization of the hedge-trimmer mechanism.
Reversible ploughs are modern agricultural implements that allow ploughing operations to be carried out with increased efficiency by turning the furrows in the same direction, regardless of the movement direction of the tractor–plough unit. Their functionality is ensured by a reversing mechanism, typically actuated by a hydraulic cylinder, which rotates the movable frame by 180° around the tractor's longitudinal axis at the end of each pass. This paper presents a static analysis of such a mechanism, with the objective of determining the distribution of mechanical stresses, displacements and equivalent deformations, as well as the safety factor of the main structural components. The applied methodology included defining the three-dimensional geometric model of the reversing assembly and performing numerical simulation using the Finite Element Method (FEM), with the SolidWorks Simulation software. The calculation assumptions considered the maximum load generated by the hydraulic cylinder during the rotation phase, with proper application of contact conditions and mechanical constraints. The results highlighted equivalent stresses, calculated according to the Von Mises criterion, below the allowable limits for the materials used, displacements and deformations within functional limits, and safety factors ranging from 2.1 to 3.5 for critical components (rotation shaft, support frame, and cylinder attachment points). The conclusions of the analysis confirm that the mechanism’s design is appropriate for the static loading conditions encountered during regular agricultural operation.
One of the most popular controller designs in control engineering is the so-called linear matrix inequality (LMI) method. In this method, the optimization is defined within a convex polytope, for which the first step is to define the convex polytope describing the problem. With the tensor product model transformation, we can directly derive such convex hulls from the linear parameter variable (LPV, qLPV) description of the nonlinear system, for example SNNN, IRNO, CNO. A transition can be formed between them, with which an infinite number of polytope representations can be produced for a system. The literature shows that narrower hulls (CNO) result in smaller control signals, which are easier to implement in practice. An easy-to-understand representation of the size of these polytopes is currently still a challenge in the literature. There is also a physical content behind them, so the norms known in mathematics are not sufficiently representative. This research presents the currently known representation techniques and their limitations.
Buildings represent nearly 40% of global energy consumption; therefore, improving their energy efficiency and thermal comfort has become a key research priority. This study presents the development of a detailed EnergyPlus-based simulation model of a five-building office complex located in Debrecen. The model integrates geometric, building physics and HVAC system characteristics, real operational schedules, weather data, and occupancy profiles. Simulations evaluate electricity and heat consumption as well as thermal comfort indicators such as temperature and PMV. Results show that the model accurately reproduces annual heating and cooling energy demand: simulated heating demand (2190 GJ) closely matches measured data (2108 GJ), while simulated cooling demand (119,283 kWh) aligns with measurements (119,356 kWh). The model provides a reliable foundation for future optimization studies, including data-driven and hybrid predictive control strategies. Future work includes calibration using real measurements and the integration of AI-assisted control.
The rapid spread of electromobility and renewable energy sources is fundamentally transforming contemporary energy systems. The Vehicle-to-Grid (V2G) technology offers a new role for electric vehicles to function as distributed energy storage units, supporting grid stability and the integration of renewable energy into everyday energy use. The aim of the study is to apply a SWOT analysis to identify the main strengths, weaknesses, opportunities, and threats associated with the introduction of V2G technology in the European Union, with a focus on Hungary in Eastern Europe. The analysis highlights that the primary strengths of V2G systems lie in EU-level regulatory support and the growing electric vehicle fleet, while weaknesses include limited charging infrastructure, technological uncertainty, and economical aspects like GDP. Opportunities include energy communities, secondary battery use, and the development of smart grid solutions, while regulatory delays and high investment costs are identified as the main threats. The results of the research confirm that the successful implementation of V2G technology is only possible with a complex approach that addresses technological, economic, and social aspects.
