Search Results (3,822)

The rapid growth of electric vehicle (EV) deployment has created a strong demand for charging systems capable of handling higher power levels while preserving grid stability and maintaining satisfactory energy quality. In this work, a fast-charging architecture for 400 V battery systems is developed using a Vienna rectifier on the AC front end and a DC–DC buck converter on the DC stage. To enhance the performance of this topology, two complementary control techniques are combined: the Crystal Structure Algorithm (CryStAl), used for offline optimization of switching behavior, and a Random Decision Forest (RDF) model, employed for real-time adaptation to operating conditions. A clear, step-oriented derivation of the converter state–space equations is included to support controller design and ensure reproducibility. This control framework improves the key performance indices, including Total Harmonic Distortion (THD), ripple suppression, efficiency, and power factor correction. Specifically, the Vienna rectifier works on input current shaping and enhances the power quality, while the buck converter maintains a constant DC output appropriate for reliable battery charging. The simulation studies show that the combined CryStAl–RDF approach outperforms the conventional PI- and Particle Swarm Optimization (PSO)-based controllers. The proposed method achieves THD less than 2%, conversion efficiency higher than 97.5%, and a power factor close to unity. The voltage and current ripples are also significantly reduced, which justifies the extended life of the batteries and reliable charging performance. Overall, the results portray the potential of the combined metaheuristic optimization with machine learning-based decision techniques to enhance the behavior of power electronic converters for EV fast-charging applications. The proposed control method offers a practical and scalable route for next-generation EV charging infrastructure. Full article

With the focus on the growing concern of voltage instability and its inherent risks connected to blackouts, this study addresses the importance of Volt/VAR control (VVC) in maintaining voltage stability, optimizing power factor, and reducing losses. As such, this scientific article presents a review of the methodologies used to estimate the quantity of reactive power required to restore voltage in power grids. Although reviews exist on classical methods, optimization, and machine learning, a study unifying these approaches is lacking. This gap hinders an integrated comparison of methodologies and constitutes the main motivation for this study in 2025. This absence of a consolidated and up-to-date review limits both academic progress and practical decision-making in modern power systems, especially as DER penetration accelerates. This research was conducted using the Scopus database through the selection of articles that address reactive power estimation methods. The results indicate that traditional numerical and optimization methods, although accurate, demonstrate high computational costs for real-time application. In contrast, techniques such as Deep Reinforcement Learning (DRL) and hybrid models show greater potential for dealing with uncertainties and dynamic topologies. The conclusion reached is that the solution for reactive power management lies in hybrid approaches, which combine machine learning with numerical methods, supported by an intelligent and robust data infrastructure. The comparative analysis shows that numerical methods offer high precision but are computationally expensive for real-time use; optimization techniques provide good robustness but depend on detailed models that are sensitive to system conditions; and machine learning-based approaches offer greater adaptability under uncertainty, although they require large datasets and careful training. Given these complementary limitations, hybrid approaches emerge as the most promising alternative, combining the reliability of classical methods with the flexibility of intelligent models, especially in smart grids with dynamic topologies and high penetration of Distributed Energy Resources (DERs). Full article

Background and Objectives: This biomechanical study aimed to compare the fixation stability of proximal fragments and assess the mechanical properties in models of unstable basicervical intertrochanteric fractures. Materials and Methods: Thirty-six synthetic femur models were utilized. After cephalomedullary nail insertion, unstable basicervical intertrochanteric fractures were created using an engraving machine. Specimens were divided into three groups based on the femoral head fixation method: Group 1 (n = 12, single 100 mm lag screw); Group 2 (n = 12, lag screw + 75 mm anti-rotation screw); and Group 3 (n = 12, lag screw + 95 mm anti-rotation screw). The anti-rotation screws were full-threaded locking screws positioned just below the lag screw. After applying 10,000 vertical cyclic loads, stereophotogrammetry was used to evaluate the proximal fragment rotation in three planes (coronal, sagittal, and axial), and screw-tip displacement was measured radiographically. Vertical load was then applied at a 10 mm/min rate until structural failure. Results: Rotational change in the sagittal plane was least in Group 3 (Group 1 = 1.7 ± 1.3°, Group 2 = 1.0 ± 0.8°, Group 3 = 0.6 ± 0.6°, p = 0.038). Varus (coronal plane) and retroversion (axial plane) collapse did not differ significantly among the three groups. While cranial migration showed no difference, axial migration was the significantly lowest in Group 3 (Group 1 = 1.07 ± 0.62 mm, Group 2 = 0.60 ± 0.57 mm, Group 3 = 0.50 ± 0.43 mm, p = 0.040). Failure load was slightly higher in Groups 2 and 3 than in Group 1, but without statistical significance. No significant differences were observed between Group 2 and Group 3 in any biomechanical outcomes. Conclusions: The novel cephalomedullary nail with a long inferior anti-rotation screw significantly reduced rotational instability and axial migration compared to a single-lag screw. There was no significant difference in the rotational stability between the 75 mm and 95 mm anti-rotation screw groups. This novel nail demonstrates superior biomechanical properties in this experimental model and warrants clinical evaluation for treating unstable basicervical intertrochanteric fractures. Full article

In this study, a novel picosecond laser ring-cut drilling method was employed to drill holes in alumina ceramics. The morphology, dimensions, taper angle, and heat-affected zone (HAZ) of the resultant micro-holes were systematically characterized under various laser processing parameters. The crystal structure, microstructure, and elemental composition of micro-holes processed under specific parameters were characterized. The results showed that the micro-hole entrance and exit dimensions and HAZ area increased with increasing spot-scanning number. However, the micro-hole taper angle initially decreased before stabilizing with an increasing spot-scanning number. Furthermore, the micro-hole entrance and exit dimensions and HAZ area gradually decreased with increasing spot-scanning speed. Conversely, the micro-hole taper angle increased with increasing spot-scanning speed. Additionally, the micro-hole entrance and exit dimensions and HAZ area gradually increased with increasing average power. However, the micro-hole taper angle gradually decreased with increasing average power. Under processing parameters of spot-scanning number N = 90, scanning speed v = 600 mm/s, and average power P = 24 W, the micro-holes exhibited a taper angle α of 4.32° and a HAZ width of approximately 0.207 mm². In contrast to the large bright grains on the original substrate, fine grains were observed around the machining area. Compared to the original substrate surface, the percentage of oxygen atoms decreased, whereas the percentage of aluminum atoms increased at the micro-hole edge and HAZ surface. The results of this study have potential applications in the field of ceramic manufacturing. Full article

Semiconductor manufacturing equipment such as vacuum pumps, wafer handling mechanisms, etching machines, and deposition systems operates for a long time under high vacuum, high temperature, strong electromagnetic, and high-precision continuous production environments. Its reliability is directly related to the yield and stability of the production line. During equipment operation, the fault signals are often weak, the noise is strong, and the working conditions are variable, so traditional methods are difficult to achieve high-precision recognition. To solve this problem, this paper proposes a fault diagnosis and health monitoring method for semiconductor manufacturing equipment based on deep learning and subspace transfer. Firstly, considering the cyclostationary characteristics of the operating signals of key equipment, the cyclic spectral analysis technology is used to obtain the cyclic spectral coherence map, which effectively reveals the feature differences under different health states. Then, a deep fault diagnosis model based on the convolutional neural network (CNN) is constructed to extract deep feature representations. Furthermore, the subspace transfer learning technology is introduced, and group normalization and correlation alignment unsupervised adaptation layers are designed to achieve automatic alignment and enhancement of the statistical characteristics of deep features between the source domain and the target domain, which effectively improves the generalization and adaptability of the model. Finally, simulation experiments based on the public bearing dataset verify that the proposed method has strong feature representation ability and high classification accuracy under different working conditions and different loads. Because the key components and experimental scenarios of semiconductor manufacturing equipment have similar signal characteristics, this method can be directly transferred to the early fault diagnosis and health monitoring of semiconductor production line equipment, which has important engineering application value. Full article

Monitoring the α-biodiversity indicators of mangrove forests and understanding their spatiotemporal trends can guide mangrove restoration strategies. Taking Qinglan Port in Hainan Province, China, as our study area, we compared multiple machine learning methods to predict the spatial distribution of α-biodiversity indicator Shannon’s diversity index (SHDI) by integrating LiDAR points and Worldview-2 images. In addition, the relationship between mangrove forests’ SHDI values and growth years was analyzed. The study extracted 28 spectral features and 99 LiDAR features from Worldview-2 and LiDAR data, respectively. The RReliefF method was adopted to select informative features. Four machine learning methods, including support vector machines (SVMs), extreme gradient boosting (XGBoost), deep neural networks (DNNs), and Gaussian process regression (GPR), were used to establish SHDI prediction models. The leave-one-out cross-validation (LOOCV) method was used to evaluate prediction accuracy, and the optimal model was adopted to generate a spatial map of SHDI. Based on Google Earth and Worldview-2 images, the spatial regions of mangrove forests in 2008, 2013, 2018, and 2023 were identified. The SHDI values within different restoration periods were statistically analyzed by using the mangroves’ spatiotemporal distributions. The results showed that RReliefF selected a total of 30 features, including 13 spectral features and 17 LiDAR features. Using preferred features, GPR had the highest prediction accuracy, with an LOOCV R² of 0.51, followed by SVM (R² = 0.44) and DNN (R² = 0.32); the accuracy of XGBoost (R² = 0.29) was relatively poor. The increased areas of rehabilitated mangrove forests in the periods of 2008–2013, 2013–2018, and 2018–2023 were 0.31 km², 0.13 km², and 1.35 km², respectively. Mangroves growing before 2008 owned the highest mean SHDI value of 0.74, followed by mangroves in 2008–2013 and 2013–2018; mangrove forests restored in 2018–2023 had the lowest mean SHDI value of 0.63. The results indicated that mangrove SHDI can be predicted by integrating LiDAR and Worldview-2. The mangrove population exhibited more diverse α-biodiversity characteristics as growth time increased. In subsequent mangrove restoration processes, planting mangroves of diverse species is beneficial to ensure the stability of the mangrove community. Full article

Artificial intelligence transforms lubricant research by linking molecular modeling, diagnostics, and industrial operations into predictive systems. In this regard, machine learning methods such as Bayesian optimization and neural-based Quantitative Structure–Property/Tribological Relationship (QSPR/QSTR) modeling help to accelerate additive design and formulation development. Moreover, deep learning and hybrid physics–AI frameworks are now capable to predict key lubricant properties such as viscosity, oxidation stability, and wear resistance directly from molecular or spectral data, reducing the need for long-duration field trials like fleet or engine endurance tests. With respect to condition monitoring, convolutional neural networks automate wear debris classification, multimodal sensor fusion enables real-time oil health tracking, and digital twins provide predictive maintenance by forecasting lubricant degradation and optimizing drain intervals. AI-assisted blending and process control platforms extend these advantages into manufacturing, reducing waste and improving reproducibility. This article sheds light on recent progress in AI-driven formulation, monitoring, and maintenance, thus identifying major barriers to adoption such as fragmented datasets, limited model transferability, and low explainability. Moreover, it discusses how standardized data infrastructures, physics-informed learning, and secure federated approaches can advance the industry toward adaptive, sustainable lubricant development under the principles of Industry 5.0. Full article

Factor of Safety (FOS) is a significant index to measure the stability condition of a rock slope in mining or civil engineering. In this paper, we evaluate and compare four different machine learning models, Gaussian Process Regressor (GPR), Support Vector Regressor (SVR), Random Forest (RF), and a hybrid genetic algorithm–multi-layer perceptron (GA-MLP), using two separate real-world datasets. The two separate datasets used in this study are from a previously conducted study on highway excavation with rock cutting in China, and another one in a mining site in Peru, with five geotechnical properties used as inputs, including slope height, slope angle, unit weight, cohesion, and friction angle. The two separate datasets were separated into training, validation, and testing datasets. The testing dataset of the models is unseen data used to assess model performance in an unbiased manner. The result shows that the SVR had the highest prediction accuracy, followed by GPR for the mining dataset, and GPR had the highest performance among all the models for the highway excavation dataset. From the boxplot, we can see that SVR, while having the highest predictive accuracy, has a larger variance in prediction compared to GPR for the mining dataset. Full article

High-feed face milling is widely adopted in industry for its productivity advantages, especially when machining medium carbon steels. However, the combined effects of lubrication regimes on both the cutting forces and surface quality remain insufficiently explored, creating a research gap in optimizing process parameters for improved performance. This study presents an experimental investigation into the effects of lubrication on cutting forces and surface topography during the high-feed face milling of C45 steel. Using a design of experiments (DOE) approach, eight distinct machining setups were developed by varying the cutting speed, depth of cut, and feed per tooth. Each setup was tested under two lubrication conditions: with flood coolant and under dry machining. Cutting forces in the X, Y, and Z directions were recorded using a dynamometer, while the post-machining surface quality was evaluated using 3D areal surface topography measurements. The results revealed that feed per tooth was the primary factor affecting both the cutting forces and surface roughness, with depth of cut having a moderate effect and cutting speed a minor influence. Flood lubrication reduced the peak forces, stabilized force fluctuations, and improved surface uniformity, particularly in the valley depths and skewness parameters. This work provides (i) a combined analysis of cutting forces and surface topography under high-feed milling, (ii) quantitative evidence of lubrication effects on force and surface consistency, and (iii) identification of dominant process parameters for optimization, offering practical guidance for enhancing productivity, surface quality, and tribological performance in high-feed milling operations. Full article

Condition monitoring has come to the forefront of intelligent manufacturing and is particularly important in Computer Numerical Control (CNC) machining processes, where reliability, precision, and productivity are crucial. The traditional methods of monitoring, which are mostly premised on single sensors, the localized capture of data, and offline interpretation, are proving too small to handle current machining processes. Being limited in their scale, having limited computational power, and not being responsive in real-time, they do not fit well in a dynamic and data-intensive production environment. Recent progress in the Industrial Internet of Things (IIoT), cloud computing, and edge intelligence has led to a push into distributed monitoring architectures capable of obtaining, processing, and interpreting large amounts of heterogeneous machining data. Such innovations have facilitated more adaptive decision-making approaches, which have helped in supporting predictive maintenance, enhancing machining stability, tool lifespan, and data-driven optimization in manufacturing businesses. A structured literature search was conducted across major scientific databases, and eligible studies were synthesized qualitatively. This systematic review synthesizes over 180 peer-reviewed studies found in major scientific databases, using specific inclusion criteria and a PRISMA-guided screening process. It provides a comprehensive look at sensor technologies, data acquisition systems, cloud–edge–IoT frameworks, and digital twin implementations from an architectural perspective. At the same time, it identifies ongoing challenges related to industrial scalability, standardization, and the maturity of deployment. The combination of cloud platforms and edge intelligence is of particular interest, with emphasis placed on how the two ensure a balance in the computational load and latency, and improve system reliability. The review is a synthesis of the major advances associated with sensor technologies, data collection approaches, machine operations, machine learning, deep learning methods, and digital twins. The paper concludes with what can and cannot be performed to date by providing a comparative analysis of what is known about this topic and the reported industrial case applications. The main issues, such as the inconsistency of data, the lack of standardization, cyber threats, and old system integration, are critically analyzed. Lastly, new research directions are touched upon, including hybrid cloud–edge intelligence, advanced AI models, and adaptive multisensory fusion, which is oriented to autonomous and self-evolving CNC monitoring systems in line with the Industry 4.0 and Industry 5.0 paradigms. The review process was made transparent and repeatable by using a PRISMA-guided approach to qualitative synthesis and literature screening. Full article

Aiming at the key problems of the material removal rate and surface integrity of existing tools in the lapping of sapphire hard and brittle crystals, an efficient lapping tool has been developed to explore a new process for HFVCD (hot filament chemical vapor deposition) diamond tools to efficiently lap sapphire wafers. With the premise of ensuring the surface roughness of the wafer is Ra ≤ 0.5 μm, the material removal rate is increased to more than 1 μm/h. To explore a high-efficiency lapping process for sapphire wafers using HFCVD diamond tools. The influence of key preparation parameters on the surface characteristics of CVD (chemical vapor deposition) diamond films was systematically investigated. Three types of CVD diamond coating tools with distinct surface morphologies were fabricated. These tools were subsequently employed to conduct lapping experiments on sapphire wafers in order to evaluate their processing performance. The experimental results demonstrate that the gas pressure, methane concentration, and substrate temperature collectively influenced the surface morphology of the diamond coatings. The fabricated coatings exhibited well-defined grain boundaries and displayed pyramidal, prismatic and spherical features, corresponding to high-quality microcrystalline and nanocrystalline diamond layers. In the lapping experiments, the prismatic CVD diamond coating tool exhibited the highest material removal rate, reaching approximately 1.7 μm/min once stabilized. The spherical diamond coating tool produced the lowest surface roughness on the lapped sapphire wafers, with a value of about 0.35 μm. Surface morphology-controllable diamond tools were used for the lapping processing of the sapphire wafers. This achieved a good surface quality and high removal rate and provided new ideas for the precision machining of brittle hard materials in the plane or even in the curved surface. Full article

This paper deals with the control of two five-phase permanent magnet synchronous motors (PMSMs), which are connected in series and operating at different speeds and torques. The topology under study is intended for use in an electrical naval propulsion system. The backstepping control strategy, which uses the Lyapunov stability concept, is employed to control the speed of the two machines considering the series connection of the PMSM stator windings. A comparative study, with respect to classical Vector Control (VC) using PI regulators, is provided to demonstrate the robustness of the proposed control strategies in both healthy and faulty conditions. Typically, dual PMSMs in series cannot operate in the degraded mode in the event of faults. This study optimizes their operation by adapting to such modes, including faults caused by symmetrical parameter changes or by an asymmetrical High Resistance Connection (HRC) in the stator windings, thereby ensuring continuity of service. The HRC is investigated and verified in one stator phase, in two adjacent stator phases and in two non-adjacent stator phases, as well as in a symmetrical HRC fault across all phases. Matlab-based simulation results validate the control design to achieve the desired performance and prove the effectiveness and the asymptotic stability of backstepping control for two series-connected 5-Ф PMSMs, thereby providing redundancy for the naval electric propulsion system. Full article

This paper presents a linear algebra-based control algorithm for multivariable gas turbine systems using matrix polynomial theory and the Kronecker product to assign block roots (i.e., block eigenvectors with prescribed latent structure). State and state-derivative feedback strategies are investigated and validated through simulations on an industrial gas turbine machine. The proposed method enables direct assignment of block roots governing closed-loop stability and transient response, while block eigenvectors shape the dynamic behavior of key turbine variables. Applicability of the approach requires block controllability and/or block observability, ensuring analytical transparency, design flexibility, and effectiveness for multivariable gas turbine control. Full article

The increasing integration of hybrid AC/DC networks in modern power systems introduces new challenges in fault detection and grounding scheme design, necessitating advanced techniques for stable and reliable operation. This paper investigates fault detection and grounding schemes in hybrid AC/DC networks using a machine learning (ML) approach to enhance accuracy, speed, and adaptability. Traditional methods often struggle with the dynamic and complex nature of hybrid systems, leading to delayed or incorrect fault identification. To address this, we propose a data-driven ML framework that leverages features such as voltage, current, and frequency characteristics for real-time detection and classification of faults. Additionally, the effectiveness of various grounding schemes is analyzed under different fault conditions to ensure system stability and safety. Simulation results on a hybrid AC/DC test network demonstrate the superior performance of the proposed ML-based fault detection method compared to conventional techniques, achieving high precision, recall, and robustness against noise and varying operating conditions. The findings highlight the potential of ML in improving fault management and grounding strategy optimization for future hybrid power grids. Full article

This paper presents test results of the performance comparison of 5G standalone (SA) and non-standalone (NSA) networks in the context of gathering data of remote sensors and machines. The study evaluates key network characteristics such as latency, throughput, jitter and packet loss (for UDP protocol only) using standardized tests to gain insights into the impact of these factors on real-time and data-intensive communication. In addition, a range of communication protocols including OPC UA, Modbus, MQTT, AMQP, CoAP, EtherCAT and gRPC were tested to assess their efficiency, scalability and suitability with different send data sizes. By conducting experiments in a controlled hardware environment, we have analyzed the impact of the 5G architecture on protocol behavior and measured the transmission performance at different data sizes and connection configurations. Particular attention is paid to protocol overhead, data transfer rates and responsiveness, which are crucial for industrial automation and IoT deployments. The results show that SA networks consistently offer lower latency and more stable performance, where robust and low-latency data transfer is essential. In contrast, lightweight IoT protocols such as MQTT and CoAP demonstrate reliable operation in both SA and NSA environments due to their low overhead and adaptability. These insights are equally important for time-critical industrial protocols such as EtherCAT and OPC UA, where stability and responsiveness are crucial for automation and control. The study highlights current limitations of 5G networks in supporting both remote sensing and industrial use cases, while providing guidance for selecting the most suitable communication protocols depending on network infrastructure and application requirements. Moreover, the results indicate directions for configuring and optimizing future 5G networks to better meet the demands of remote sensing systems and Industry 4.0 environments. Full article