Digital twin technology has emerged as an effective approach for improving the monitoring, validation, and optimization of Computer Numerical Control (CNC) machining operations. This study presents an investigative framework integrating Computer-Aided Design/Computer-Aided Manufacturing (CAD/CAM), Software-in-the-Loop (SIL), and Hardware-in-the-Loop (HIL) methodologies for diagnosing and rectifying machining defects in CNC systems using the digital twin - virtual model (component and asset twins). Four machining experiments were conducted using Aluminum 6061-T6, Polytetrafluoroethylene (PTFE), Brass, and Stainless Steel (AISI 304) on an EMCO CNC machine integrated with Siemens 840D controls. The first two experiments, involving turning and drilling operations, are presented in detail; the third and fourth experiments, conducted on Brass and Stainless Steel (AISI 304), are included as additional experimental validations under different machining conditions to evaluate the robustness and repeatability of the proposed methodology. Real-time machining parameters, including spindle speed, feed rate, axis offsets, and drilling depth, were synchronized between the virtual simulation environment and the physical CNC machine through digital twin-based validation. The proposed framework successfully identified dimensional deviations, tool offset errors, and drill alignment defects before and during machining operations. Experimental validation demonstrated a reduction in dimensional error from approximately +0.50 mm to within ± 0.05 mm tolerance limits after digital twin optimization. Furthermore, optimization of contour-turning strategy reduced the CAM simulation time from 5.0 min to 3.0 min, corresponding to approximately a 40% improvement in process efficiency. Repeatability tests conducted over three machining trials confirmed consistent machining accuracy and process stability. The results demonstrate that digital twin-assisted CNC machining can significantly improve dimensional precision, reduce machining errors, minimize setup time, and enhance production reliability in smart manufacturing environments.
Receiving rainfall predictions in dry regions such as Saudi Arabia is challenging due to limited observational stations and the temporal and spatial variability of rainfall events. This study explores the performance of nine remote sensing rainfall datasets over the Qassim region of central Saudi Arabia. High-resolution rainfall data from ground-based sources were used to evaluate the accuracy of CHIRPS, TRMM, GPM, PERSIANN, CMORPH, CFSR, TerraClimate, TerraClimate_Monthly, and ERA5 datasets. Performance metrics such as R2, RMSE, MAE, and bias were calculated to quantify the accuracy of each dataset. Analysis revealed that ERA5 generally demonstrated better accuracy than the other datasets with a mean R2 and RMSE, MAE, and bias values of 0.59, 3.91, 2.89, and 0.43, respectively. The algorithms were trained, validated, and applied to forecast daily, 5-day, 10-day, and monthly total rainfall. Root mean square error (RMSE) values decreased by up to 52%, 50%, 56%, and 45% for daily, 5-day, 10-day, and monthly rainfall forecasts, respectively. To improve the accuracy of future predictions, multivariable models were developed that incorporated mean air temperature with precipitation predictors. Five machine-learning algorithms were tested: Gradient Boosting Regressor (GBR), Histogram Gradient Boosting Regressor (HGBR), Random Forest Regressor (RFR), Extreme Gradient Boosting Regressor (XGBR), and an advanced version of XGBR. For the 70/30 train-validation split over time, the models based on boosting methods (HGBR and XGBR) demonstrated the highest predictive ability and yielded the best statistical performance (R2 = 0.90) with the lowest errors. Combining a suite of reanalysis datasets with advanced machine learning strategies enables effective rainfall prediction in arid regions that lack adequate observational data. Such a strategy facilitates rainfall estimation in ungauged catchments, enabling water resource management and flash-flood risk assessment in the desert domain.
There are several industrial applications for BLDC motors where precise speed control is required. There has been a lot of research on and practical use of classical controllers, but when dealing with nonlinearity, changing plant parameters, moving time delays, and excessive plant noise, advanced controllers often perform better. This research compares two different methods for controlling the BLDC motor drive system speed. An advanced Sliding Mode Controller based on the super-twisting method with integral sliding surface and an advanced Proportional Integral Derivative (PID) controller with derivative filtering and anti-windup compensation are both examined in this work. The author of this study examines a BLDC motor model and evaluate both controllers under identical conditions in MATLAB/Simulink. The simulation's results show that both controllers exhibit a low steady-state error and dependable speed control. The SMC converges faster than the PID controller, whereas PID is simpler to develop computationally and has smoother control action. Performance metrics are examined to assess the dynamic response. The simulation results indicated that with settling times of less than 0.6 seconds, both controllers exhibit comparable transient responses. The SMC controller achieves a low steady state error of 0.3164 RPM compared to 2.3758 RPM for the PID controller, while also reducing chattering level and control efforts. The simulation results elaborated that both controllers are good choices in controlling motor speed and attaining precise reference tracking.
To address issues such as loose structure, low integration, and reliance on imports in existing marine electro-hydraulic actuators, this paper presents an integrated compact electro-hydraulic actuation device. The device integrates the drive unit, connection block, hydraulic actuation unit, and feedback signal box into one assembly. Through the integrated design of a built-in radial piston pump and valve block, a micro hydraulic power unit with no external pipelines is achieved. This paper elaborates on the structural composition, working principle, and key module design of the device, and analyzes the implementation of its triple redundancy operation modes: "electric - manual hydraulic - manual mechanical". Six-probability (reliability, maintainability, etc.) design and risk analysis were conducted, a reliability model was established, and the predicted Mean Time To Repair (MTTR) meets the shipboard requirement of ≤ 2h. Performance test results show that the device achieves a maximum working pressure of PN16, power ≤ 300 W, and an adjustable rotation angle of 90° ± 3°, realizing high integration and compact layout. The device was designed and manufactured in collaboration with a domestic marine valve actuator manufacturer.
Betahistine is a novel drug used to treat vertigo. The research focuses on developing and optimizing a simple, environmentally sustainable spectrophotometric approach for the analysis of BH in tablet formulations. The present investigation is based on the reaction between BH and potassium iodide and potassium iodate, monitored by Ultraviolet-Visible spectrophotometry at 352 nm. The most influential variables were optimized using response surface methodology via Box–Behnken design, resulting in reduced chemical, energy, and analysis time, and ultimately the analysis cost. Environmental sustainability was assessed using Analytical Eco-Scale (92), Analytical Greenness (0.72), Blue Applicability Grade Index (72.5) and Environmental and Practical Performance Index (89.1) metrics, which confirmed outstanding greenness compared with other reported methods. The method was validated as per International Conference on Harmonization (ICH) guidelines. The linearity, LOD and LOQ values were 2.5–12.5, 1.25 and 3.45 μ g/mL, respectively. The accuracy and precision were also excellent within the range. Moreover, the simplicity, reproducibility, and low-cost instrumental analysis of the drug enhance its wide applicability and provide a reliable solution for quality control in BH commercial formulations, chemical and pharmaceutical research laboratories, and academicians.
The goal of the study is to determine which of the chosen interpolation techniques is optimal for forecasting soil characteristics in unmeasured areas for infrastructure and building projects. The study also incorporates geotechnical data from 23 regional boreholes with the UNESCO soil classification system developed by the Food and Agriculture Organization (FAO). The mapping and accuracy of different interpolation techniques were assessed using ArcGIS 10.4. Additionally, Spline, Inverse Distance Weighting (IDW), Kriging, Natural Neighbor, and Radial Basis Functions are used to assess the precision and efficacy of several interpolation techniques. The investigation's conclusions showed that the ``natural neighbor'' strategy was the most reliable way to gauge Jazan's soil quality. With 98% accuracy, this approach yielded the lowest RMSE value error rate among the others. Compared to the other methods, this one yielded the lowest RMSE value error rate, and its 98% accuracy rate showed that the R-square validation closely matched the real data. In regions outside the Red Sea, the groundwater table is likewise much lower, at about 0.5 meters below the surface, according to the findings of soil testing. According to the study, sand, clay, and silt make up the majority of the soil in Jazan.
In recent days, Self-Compacting Geopolymer Concrete (SCGC) has the potential to revolutionize concrete technology by combining the benefits of both Geopolymer Concrete (GPC) and Self-Compacting Concrete (SCC). The production of SCGC requires proper selection of materials and proportions to achieve a composite concrete with a refined pore structure and low permeability. Additionally, chemical and mineral admixtures play a crucial role in enhancing and refining the pore structure of SCGC, as mineral admixtures reduce the absorbency of the cement matrix further and improve the porosity of the aggregates. In this investigation, the impact of high temperatures on the properties of SCGC was examined. The main focus of this research is to assess the influence of 8 M, 10 M, and 12 M NaOH solutions on the workability, mechanical properties, and durability of Fly Ash (FA) and Ground Granulated Blast Furnace Slag (GGBS)-based SCGC. The SCGC with 12 M NaOH was selected for partial replacement of normal aggregates with Recycled Brick Aggregate (RBA). In this context, Recycled Brick Aggregate replaced coarse aggregate (CA) in the range of 20% to 100%. The curing temperature was varied from ambient conditions up to 120 °C. Furthermore, the abrasion and impact resistance of the concrete, which are crucial for its durability, were also evaluated. Durability tests including water absorption, acid attack, Rapid Chloride Permeability Test (RCPT), sorptivity, and alkalinity were conducted on the SCGC samples, and the results were analysed.
Hydrogen is being explored as a potential alternative energy source, with water electrolysis emerging as one of the key methods under consideration. This process involves applying an electric current to water to generate hydrogen gas. A recent study examines a membrane-free steel electrolyzer that uses potassium hydroxide as the electrolyte and is powered by solar panels. The experiments revealed that each electrolyzer plate operated at 2–3 volts, requiring careful coordination between the number of plates and the output voltage of the solar panels. Hydrogen production ranged from 1,919 to 6,919 L/m2 of solar panel surface, highlighting the importance of optimizing both panel and plate sizes to maximize efficiency and performance.
The paper suggests a method/strategy to compensate the deviations in the system frequency and output voltage, caused by an inverter output impedance, droop coefficients terms and amplifier gain in the RDC (robust droop control) model of single phase inverter. The compensation is acquired through adopting a TDC (transient droop characteristics) enabled droop control. The suggested technique, to achieve proportionate power sharing, nominal output voltage and frequency at steady state, is applied to a communication-less system of two parallel microsource inverters. The small signal model and eigenvalue analysis are used to characterize the virtual resistor selection criteria in the inverter control block. The damping properties of power sharing responses improve when a PI controller is used in place of an integrator in a voltage-power (V-P) loop, The effectiveness of the suggested method is evaluated against model which is not equipped with the TDC. Subsequently, the restoration function of centralized secondary control is discussed for a standard microgrid setup, consisting of two inverters connected to bus loads via feeders. The control mechanism is illustrated to fix voltage and frequency of microgrid. The synchronous reference frame, also known as the d-q reference frame, describes the submodels of inverter control, such as droop controller, voltage controller, current controller, etc., in order to derive nonlinear equations that represent the dynamics of the system. The procedural steps for obtaining the small-signal model are thoroughly described. The concept of VI (virtual impedance) is discussed followed by the control algorithm to obtain its optimum parameters. The proposed schemes/models are verified through time domain simulations in Matlab/Simulink tool.
This research article is focused on Gas Metal Arc (GMA) Welding which is also known as Metal Inert Gas (MIG) welding of AISI304 of 5mm thickness. In this research article basic aim is to discover how welding parameters like arc voltage, gas flow rate, and welding speed affect the mechanical quality of weldment i.e. tensile strength and hardness. The highest tensile strength recorded is 470 N/mm2 at 20V and hardness is 196 at 24V. The strength and quality of the welds can be greatly increased by choosing the right welding parameters. In this study, Response Surface Methodology (RSM) was used to create a model that shows the connection between the welding process variable settings and the weld strength. The most significant factors for weld quality were determined using ANOVA tests, and the findings were verified at a 95% confidence level. Strong and dependable welds are crucial in the manufacturing, automotive, and construction sectors, so this research is beneficial. Future research on welding and advancements in the process will also benefit from the data gathered. The results showed that arc voltage had a considerable impact on ultimate tensile strength and hardness, and there was good agreement between the observed and anticipated values.
Heat-transfer enhancement techniques such as fins, dimples, and grooves are widely employed to improve the thermal performance of heat exchangers; however, their application is often accompanied by increased pressure losses. Most existing studies investigate a single enhancement technique over a limited Reynolds number range, with few comparative analyses conducted at high Reynolds numbers using identical geometries. Therefore, this study numerically investigates the thermal–hydraulic performance of five pipe configurations using Computational Fluid Dynamics (CFD). All geometries share the same diameter (48.34 mm) and length (7.45 m) and are examined over a Reynolds number range of 4,000–57,000. The configurations include: (1) smooth pipe, (2) pipe with spherical convex dimples, (3) pipe with spherical concave dimples, (4) pipe with circumferential grooves of depth (e) to diameter (d) ratio of 0.20, and (5) pipe with circumferential grooves of e/d\ = \ 0.50. A mesh-independence study was performed for both smooth and dimpled pipes, followed by CFD validation against available experimental data and empirical correlations from three different sources in the literature. After validation, local temperature, pressure, and velocity fields were analyzed at the highest flow Reynolds number (Re = 57,000). In addition, the effects of Reynolds number on Nusselt number, friction factor, and thermal performance factor (TPF) were evaluated for each pipe case. Results indicate that dimples and grooves promote recirculation and near-wall mixing, enhancing heat transfer relative to the smooth pipe. The groove configuration with e/d\ = \ 0.50 achieved the highest TPF (≈ 1.45 at Re = 57,000), but with a 456% increase in friction factor. Whereas the e/d\ = \ 0.20 case provided a more balanced thermal–hydraulic performance.
Graphitic carbon nitride (g-C3N4) has become a favorable photocatalyst due to its unique optoelectronic properties, however high recombination rate and low separation efficiency of the photogenerated charge carriers restrict their practical usage. To minimize the recombination rate, we synthesized a ternary nanocomposite, g-C3N4/rGO/TiO2 via a facile, simple, and cost-effective mechanochemistry technique. The ternary nanocomposite effectively degrades Rhodamine B (RhB) dye mainly due to higher visible light utilization and multi-step charge transfer mechanism. The optimal rGO plays an essential role in effective photocatalytic performance due to its unique electrical, large surface area, and optical properties. The synthesized nanocomposite degrades 80% RhB dye compared to pristine g-C3N4 and binary which degrades 39.6 and 64.8% respectively in 2h. This study showcases a simple, low-cost method for the effective degradation of dyes.
Coffee is the second most traded commodity globally after oil and represents a major source of income in many countries. Kingdom of Saudi Arabia has a great interest in growing and expanding of coffee in line with the Saudi Green Initiative as implementation of Vision 2030 AD. This expansion is accompanied by a greater spread of insect pests and the emergence of diseases affecting coffee. Recently, machine learning technologies have been beneficial in the agricultural era in detection and classification of fruit and tree diseases. The study initially focuses on kind of disease that appears in the leafy area of the coffee plant, which is susceptible to many diseases such as Cercospora spp., magnesium deficiency, and others. Deep learning, convolutional neural networks (CNN), support vector machines (SVM), and other imaging and machine learning techniques are used in this paper to detect and classify leaf diseases. The JMuBEN and JMuBEN2 databases from Kenya were used in the first experiment, and the Fyfa Mountains database from the Jizan region was used in the second experiment to automatically detect and classify coffee tree leaf disease. In an SVM model, data preprocessing and data transformation methods are used to generate accurate information to train the model. Grid search is also used across the parameter grid to optimize the estimator parameters used in applying the model. The experimental results showed superior performance compared to many modern basic methods in terms of accuracy, reaching 100%. CNNs have also proven their effectiveness and accuracy in the fields of pattern recognition and image classification. As a result, a CNN model is introduced that takes advantage of transfer learning, which significantly reduces the model training time.
Micro thermoelectric cooler (μTEC) modules have recently attracted significant attention due to their compact size, effective heat dissipation, and rapid thermal response. These characteristics make them highly suitable for applications in electronics, telecommunications, and healthcare. The present study focuses on evaluating the cooling performance of a μTEC module under varying operating conditions. A comprehensive experimental setup was developed to measure critical parameters such as temperature difference, cooling capacity, and coefficient of performance (COP). Experimental results were compared with theoretical predictions, revealing a strong correlation, particularly at lower temperature differences and moderate current levels. However, deviations were observed at higher currents, primarily attributed to increased Joule heating and contact resistance. Overall, the agreement between experimental data and the theoretical model supports its validity while also highlighting areas for potential refinement. The findings contribute to a deeper understanding of μTEC efficiency and support future enhancements in thermoelectric cooling system design.
This paper outlines developing and applying a multi-point liquid level measuring and monitoring sensor by utilizing polymer optical fiber (POF). Traditional liquid-level sensors often face limitations of susceptibility to corrosion and electromagnetic interference. The proposed POF based sensor offers advantages, including immunity to electromagnetic interference, flexibility, durability, and multi-points measurement. Through advanced fabrication techniques and sensor design, this paper presents to create a reliable, cost-effective, and versatile solution for measuring and monitoring liquid levels for various industrial and environmental applications. The research in this paper involves the fabrication of the POF sensor, optimization of its performance characteristics, and demonstration of its effectiveness through experimental testing. The resulting sensor have the potential to revolutionize liquid-level monitoring systems, offering improved accuracy, longevity, and adaptability to diverse environments. This research developed a reliable, efficient, and cost-effective solution to simultaneously measure liquid levels at multiple points, leveraging the unique properties of POF for sensing applications.
The present study is focused on investigating the steady-state and transient responses of a DC servo motor controlled by the Proportional and Integral controllers. It is also studied how the P and I controllers affect the stability, error, and damping of the DC motor response. The DC servo motor responses are obtained experimentally using a servo trainer and a PI analog controller, and theoretically using MATLAB Simulink. It is found through the results that the P controller contributes to decreasing the input-output errors of steady-state and transient responses and tends to stabilize the system. It also speeds up the system response. On the other hand, the I controller tends to diminish the error but with sluggish response and affects the system’s stability with oscillations. Also, the Simulink results show that the optimized PI controller gains (Kp = 1.0363 and Ki = 1.9438) significantly reduce the overshoot and settling time. Through the present work, it is desired to obtain a stable response of the DC servo motor to the steady-state and transient input signals. The novelty of the present work is developing a PI controller-based nonlinear model for the DC servo motor speed control and optimizing the PI controller gains for the motor’s optimum performance.
Polymer Electrolyte Membrane Fuel Cells (PEMFCs) are a promising technology for clean and efficient energy production. PEMFC was initially developed for the space programs in the 1960s, but today, it finds its application in numerous engineering applications. Growing concerns about fossil fuel depletion and global warming, along with recent advances in renewable energy and the hydrogen economy, have further supported its development. This comprehensive review consolidates modeling approaches, performance analyses, thermal management strategies, and future prospects of PEMFC technology. The analysis of the characteristics of Voltage-Current (V-I) and subsequent examination of the fuel cell’s efficiency include the evaluation of thermal and other input parameters. For instance, adopting the optimum thickness of catalyst layers of the anode (0.0231 mm) and cathode (0.0315 mm) current collectors enhances the performance of PEMFC by 6.8%. Experimental results show PEMFC performance peaks at 65°C, with lower temperatures reducing power. Optimal humidity improves performance, but excess causes flooding. Active water cooling enhances thermal management, boosting power density and stability compared to passive cooling. It is also noticed that altering the temperature and input values of the fuel cell results in variations in voltage losses. Compilation of the scattered work and providing the readers with an inventory to comprehend about the gaps in this particular research area for future work advocates the novelty of this review paper.
Fault-tolerant control (FTC) techniques have the potential to significantly enhance the dependability of voltage source inverters (VSI) and are becoming popular. This study presents an innovative fault tolerance approach for designing a very dependable induction motor drive (IMD) that incorporates both analytical (active) and hardware redundancies. The created model aims to enhance fault tolerance against the current sensor, speed sensor, and IGBT switch failures in the inverter module. The Active Fault Tolerant Control System (AFTCS) has been applied to the speed sensor to detect any faults in the sensor. If a fault is detected, the system will replace the defective value with an estimated value for open loop speed estimation. This estimation is based on a flux estimation observer. The fault tolerance of the current sensors is designed such that to replace the defective value with the average value of the other two functional current sensors in the event of a failure in one sensor assuming that only one sensor gets faulty at a time. The Fault Detection and Isolation (FDI) unit is designed to rapidly identify and replace a defective switch with a backup redundant switch in the shortest possible timeframe. The stability and convergence of the observer is also proved using the Lyapunov theorem. The use of Markov chains in the reliability investigation further substantiated the system's exceptional dependability. To evaluate the effectiveness of the suggested approach, a variable-speed induction motor with a power capacity of 1.1kW is constructed using MATLAB/Simulink. A 3-phase inverter and fault detector unit are implemented on an STM32-Nucleo-F103RB board with hardware-in-the-loop capability to validate the accuracy of the simulation results to highlight the robustness of the developed active fault-tolerant control. The simulation results coupled with the hardware-in-the-loop experiment demonstrate that the IM drive maintains its stability with little performance degradation in the events of faults in the speed and current sensors as well as inverter switches. Finally, a comparison with the existing literature was carried out to showcase the improved performance and heightened dependability of the proposed model.
Using predictions of crack-tip-opening-displacement (CTOD) to measure the extent of fatigue damage has provided the opportunity to prepare an efficient strategy for protecting mechanical structures from damage and developing a structural health monitoring system. The objective is to forecast non-measurable CTOD by using machine learning methods. In this paper, an optical metrology device, which is built by Alicona on a confocal microscope and hereafter referred to as Confocal Microscope, has been used to measure CTOD. However, two factors limit the usage of Alicona Apparatus: (i) the size of optical images, where a CTOD over 400 micrometers cannot be measured; and (ii) the need to protect the device, as a CTOD over 150 micrometers has a significant impact on the safety of Confocal Microscope. Therefore, this paper has utilized Gaussian Processes (GP) and the Support Vector Machine (SVM) to forecast the non-measurable CTOD. Four machine learning metrics, mean average error (MAE), mean square error (MSE), root mean square error (RMSE) and R-squared error have been used in this study to evaluate the performance of the regression models. The results indicate that the GP model provides a better estimate of the current CTOD measurements. However, the SVM model provides a better forecast of future CTOD data based on the behavior of the CTOD rate.
This research addresses the critical issue of enhancing public safety in smart cities, with a particular focus on industrial cities in Saudi Arabia. Despite the advanced infrastructure and high safety standards in industrial cities, there are safety challenges, particularly the vulnerability of residential properties to burglaries during holidays and extended breaks. To mitigate these risks, the research proposes leveraging existing infrastructure to implement surveillance systems that monitor vehicle movements using RFID technology and smart sensors, thereby enhancing safety, supporting law enforcement in crime detection, and protecting local communities. The study also acknowledges the reduced social interaction often associated with the heavy reliance on technology in smart cities, emphasizing the need to foster social connections alongside technological advancements. A proposed vehicle monitoring solution is introduced, capable of detecting and identifying registered and unregistered vehicles while recording movement data in urban areas for subsequent analysis. Future research is recommended to expand this concept, focusing on real-world implementation with real-time connectivity and cloud-based services integrated into secure and certified database systems.
