Advances in Science and Technology Vol. 181

Abstract: Single point incremental forming (SPIF) is a die-less forming technique well-suited for rapid prototyping and low-volume production. The deformation mechanism differs significantly from conventional metal forming processes, which contribute to enhanced formability. On this basis, the coefficient of friction (COF) derived from the contact at the interface between the tool and the sheet is a critical aspect during forming. This study is devoted to discover the effect of critical process parameters on the friction evolution in SPIF using a finite element analysis after model validation. Results ndicated that increasing overall step size, wall angle, and tool diameter contribute significantly to the increase of the COF. In addition, when the geometry benchmark was designed with ψ = 70◦, the the friction indicator μ∗ attains 0.63.

Abstract: Wire Bending Machines have served as repositories of engineering knowledge and heritage for millennia. As one of the earliest forms of engineering craftmanship, their often period-defining design and operating principles can be used to trace the development of engineering practice through the ages. The 19^th century Mechanical Wire Bending Machine investigated in this article is typical of this development and presents a wide array of possible inspection areas for modern research to delve into. In this article, we specifically address the manufacturing working analysis of an identical CAD assembly of the machine as a demonstration of the application of modern techniques in preserving early institutional engineering expertise. The CAD models, having been designed on CATIA V5 are imported into Autodesk’s Fusion 360 software then assembled using Fusion’s motion-based assembly features to create a true-to-scale assembly of the working machine. We then analyze the working mechanism of the assembly, describing analytically its manufacturing technology with all the technological parameters involved in bending. Ultimately, we will make and present our deductions.

Abstract: This paper presents the design and experimental validation of a micro-phasor measurement unit (uPMU) system developed for real-time protection and power quality analysis in low-voltage distribution networks. The proposed architecture integrates a Raspberry Pi-based Central Protection Unit, an AD7606 synchronous analog-to-digital converter for high-speed multi-channel sampling, and a DAC8568 digital-to-analog converter for test and simulation purposes. The system supports hardware time synchronization via the Precision Time Protocol (PTP) and enables both real and simulated grid event measurements. Experimental results confirm the feasibility of achieving up to 200~kSPS sampling per channel, providing the temporal resolution required for sub-cycle fault detection. The long-term objective is to apply artificial intelligence (AI) techniques to predict the next waveform samples and detect deviations that indicate faults or abnormal grid conditions within microseconds.

Abstract: The main objective of the developed automated pallet changing system is to increase the productivity of CNC machining centres and to minimize machine idle times. The system’s core component is a loading unit equipped with two independently controlled forks, enabling fast and precise pallet handling while significantly reducing non - productive time. The entire structure is modular and fully electrically driven, utilizing linear actuators and servo motors that ensure high positioning accuracy. The design and verification of the structure were performed using the Finite Element Method (FEM) to guarantee adequate rigidity and operational reliability. The innovative semi-circular pallet storage provides higher capacity and a more compact layout compared to conventional linear storage systems. Additionally, a rotary loading station was introduced to further reduce setup and handling times. A functional prototype was produced using additive manufacturing with PLA material and is controlled by an Arduino-based system, demonstrating the feasibility of the concept. The developed system is currently under patent protection in Slovakia, confirming its industrial applicability and technological originality.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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 𝐼(0), 𝐼(1), and 𝐼(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.

Abstract: Uzbekistan’s industrial sector, a major contributor to the nation’s projected 6.2% GDP growth in 2025, increasingly depends on the reliable operation of induction motors across key areas such as mining, energy, and manufacturing. In line with the “Uzbekistan 2030” strategy, the government’s modernization agenda emphasizes the integration of predictive maintenance technologies—particularly vibration-based condition monitoring—to enhance system reliability, reduce downtime, and extend motor lifespan. Within the energy sector, where aging infrastructure continues to challenge efficiency, vibration diagnostics play a vital role in detecting motor faults in power generation and transmission systems, thereby supporting tariff liberalization and privatization reforms. The mining industry, strengthened by the 2024 subsoil resource legislation, applies vibration analysis to critical machinery such as conveyors and pumps to ensure operational safety and energy efficiency during the exploration of rare earth elements. Furthermore, Free Economic Zones (FEZs) and Small Industrial Zones (SIZs) attract foreign investment in high-tech manufacturing, where the adoption of vibration-based monitoring systems supports import substitution and cost optimization. As Uzbekistan moves toward WTO accession by 2026, industrial enterprises are adopting advanced monitoring systems to meet international standards, improving competitiveness in export-oriented sectors like metallurgy and mechanical engineering. The growing implementation of IoT-enabled vibration sensors reflects a broader transition from reactive to predictive maintenance practices, fostering sustainability and productivity within newly privatized enterprises. Collectively, these developments position vibration-based condition monitoring as a critical component of Uzbekistan’s industrial transformation and its shift toward a resilient, market-oriented economy.

Abstract: This paper introduces a specialized Parallel Inception-style 1D-CNN (PINC) architecture designed for real-time condition monitoring of the Universal Robots UR3e platform. Traditional deep learning models for anomaly detection often require substantial computational resources. In contrast, our proposed PINC model uses per-channel parallel feature extraction to identify multi-scale physical anomalies with high accuracy. Using the CASPER dataset, the architecture achieves a diagnostic accuracy of 99.1% while maintaining a low memory footprint of just 152 KB. These findings show that the PINC model effectively captures vibrational signatures without depending on high-latency cloud processing. The results offer a scalable and efficient hardware solution that enables localized, real-time fault detection in human-robot shared workspaces.