Search Results (2,832)

Copper (I) sulfide (Cu₂S) is a low-cost, earth-abundant, and non-toxic thermoelectric material for applications in the middle–high temperature range (>650 K). Although 3D printing these materials can simplify their manufacturing, elevated temperatures observed during sintering impair their crystal structure and energy conversion efficiency. In this study, we demonstrated a novel post-processing methodology to revert the thermoelectric properties of the 3D printed Cu_2-xS materials back to the unimpaired state via sulfur infusion. After printing and sintering, sulfur was infused into the specimens under vacuum to optimize their crystal structure and achieve high thermoelectric efficiency. Chemical analysis and X-ray Diffraction (XRD) tests showed that after the sulfur infusion process, the Cu/S ratio was reverted close to the stoichiometric level. The 3D printed Cu_2-xS showed p-type thermoelectric behavior with electrical conductivity peaking at 143 S-cm⁻¹ at 750 K and Seebeck coefficient of 175 µV-K⁻¹ at 627 K. The figure of merit (ZT) value of 1.0 at 780 K was achieved, which is the highest value ever reported for a 3D printed Cu_2-xS thermoelectrics at this temperature. The fabrication of environmentally friendly thermoelectric materials with extended dimensional freedom and conversion efficiency has the potential to impact the thermoelectric industry with new energy conversion applications and lowered manufacturing costs. Full article

In this study, the effects of several geometry factors (fin front angle, clearance, number of fins, and honeycomb cell diameter and depth) on the mass flow function of solid and honeycomb land were studied experimentally. The fin front angle considered in the experiment ranged from 60 to 90 degrees, the number of fins was varied between two and three, and the diameter and depth of the honeycomb cell ranged from 1.33 to 4.00 times and 8.08 to 13.08 times the thickness of the fin tip, respectively. The experimental results showed that the mass flow function decreased as the number of fins increased for the solid land labyrinth seal, and the mass flow function increased as the clearance increased. A fin front angle of 60 degrees was found to have the minimum mass flow function. For the honeycomb land labyrinth seal, the mass flow function decreased as the number of fins increased, and the effect of the cell depth was shown to be insignificant compared to the effect of the cell diameter. The effects of cell diameter and cell depth on the mass flow function depended on the conditions of other variables. In addition, the correlation equations of the mass flow functions of the solid land and honeycomb land labyrinth seals are presented based on the experimental results, which represent the effects of the fin front angle, clearance, pressure ratio, and diameter and depth of the honeycomb cell. The correlation equation for the solid land labyrinth seal had an $r^2$ value of 0.9822, while the correlation equation for honeycomb land had an $r^2$ value of 0.9621. Full article

This article analyzes circulating current control in single-phase power electronic converters, focusing on two different topologies: interleaved and parallel configurations. The study involves a bridgeless interleaving topology with two boost converters for increased efficiency. A parallel connection is also examined for monitoring line current, circulating currents, and power factor control. The article widely explains all current loops, including Common Mode Circulating Currents (CMCC) in the bridgeless interleaved topology and Differential Mode Circulating Currents (DMCC) in parallel-connected interleaved power converters. The proposed control scheme employs voltage and current control loops for output voltage and line current control and introduces CMCC and DMCC compensators to eliminate all types of circulating currents. An efficient Power Factor Correction (PFC) and output voltage control method is presented in this article. The effectiveness of the proposed schemes is validated through comparisons with modern control systems. The results are verified using Simulink/MATLAB and experimental setups with TI Instruments Piccolo prototypes and C2000 (TMS320F28035 microcontroller MCU) microcontrollers in parallel configurations. Full article

Additive Manufacturing (AM) is the process of joining materials by selectively depositing them layer upon layer for the purpose of manufacturing parts or assemblies which are based on a 3D digital model. The nature of these processes results in the morphing of complex component geometries, enabling a high degree of design freedom and resulting in lightweight structures with increased performance. These processes, however, experience many limitations regarding manufacturability. The aim of this study is to develop a method and tool that optimizes the design of a component to avoid overhanging geometries and the need for supports during the Additive Manufacturing process. A workflow consisting of steps for topology optimization, orientation optimization, material addition, and machine code generation is described and implemented using Rhinoceros 3D and Grasshopper software. The proposed workflow is compared to a conventional workflow regarding manufacturing Key Performance Indicators (KPIs) such as part volume, support volume, and build time. A significant reduction is observed regarding all the KPIs by using the proposed method. Examining the results from both the conventional workflow and the proposed one, it is clear that the latter has unquestionable advantages in terms of effectiveness. In the particular case study presented, a total volume reduction of around 80% is observed. The reduction in the total volume (including the required support volume) leads to a significant reduction in the material used as well as in the build time, consequently resulting in cost reduction. Full article

In this paper, we address the most pressing challenges faced by the manufacturing sector, particularly the manufacturing of small and medium-sized enterprises (SMEs), where the transition towards high-mix low-volume production and the availability of cost-effective solutions are crucial. To overcome these challenges, this paper presents 14 innovative solutions that can be utilized to support the introduction of agile manufacturing processes in SMEs. These solutions encompass a wide range of key technologies, including reconfigurable fixtures, low-cost automation for printed circuit board (PCB) assembly, computer-vision-based control, wireless sensor networks (WSNs) simulations, predictive maintenance based on Internet of Things (IoT), virtualization for operator training, intuitive robot programming using virtual reality (VR), autonomous trajectory generation, programming by demonstration for force-based tasks, on-line task allocation in human–robot collaboration (HRC), projector-based graphical user interface (GUI) for HRC, human safety in collaborative work cells, and integration of automated ground vehicles for intralogistics. All of these solutions were designed with the purpose of increasing agility in the manufacturing sector. They are designed to enable flexible and modular manufacturing systems that are easy to integrate and use while remaining cost-effective for SMEs. As such, they have a high potential to be implemented in the manufacturing industry. They can be used as standalone modules or combined to solve a more complicated task, and contribute to enhancing the agility, efficiency, and competitiveness of manufacturing companies. With their application tested in industrially relevant environments, the proposed solutions strive to ensure practical implementation and real-world impact. While this paper presents these solutions and gives an overview of their methodologies and evaluations, it does not go into their details. It provides summaries of comprehensive and multifaceted solutions to tackle the evolving needs and demands of the manufacturing sector, empowering SMEs to thrive in a dynamic and competitive market landscape. Full article

Many rivers are polluted by trash and garbage that can affect the environment. Riverbank inspection usually relies on workers of the environmental protection office, but sometimes the places are unreachable. This study applies unmanned aerial vehicles (UAVs) to perform the inspection task, which can significantly relieve labor work. Two UAVs are used to cover a wide area of riverside and capture riverbank images. The images from different UAVs are stitched using the scale-invariant feature transform (SIFT) algorithm. Static and dynamic image stitching are tested. Different you only look once (YOLO) algorithms are applied to identify riverbank garbage. Modified YOLO algorithms improve the accuracy of riverine waste identification, while the SIFT algorithm stitches the images obtained from the UAV cameras. Then, the stitching results and garbage data are sent to a video streaming server, allowing government officials to check waste information from the real-time multi-camera stitching images. The UAVs utilize 4G communication to transmit the video stream to the server. The transmission distance is long enough for this study, and the reliability is excellent in the test fields that are covered by the 4G communication network. In the automatic reconnection mechanism, we set the timeout to 1.8 s. The UAVs will automatically reconnect to the video streaming server if the disconnection time exceeds the timeout. Based on the energy provided by the onboard battery, the UAV can be operated for 20 min in a mission. The UAV inspection distance along a preplanned path is about 1 km at a speed of 1 m/s. The proposed UAV system can replace inspection labor, successfully identify riverside garbage, and transmit the related information and location on the map to the ground control center in real time. Full article

Improving the prediction accuracy of aerospace engine production line yields is of significant importance for enhancing production efficiency and optimizing production scheduling in enterprises. To address this, a novel method called Convolutional Neural Networks-Improved Support Vector Regression (CNN-ISVR) has been proposed for predicting the production line yield of aerospace engines. The method first divides the factors influencing production line yield into production cycle and real-time status information of the production line and then analyzes the key feature factors. To solve the problem of poor prediction performance in traditional SVR models due to the subjective selection of kernel function parameters, an improved SVR model is presented. This approach combines the elite strategy genetic algorithm with the hyperparameter optimization method based on grid search and cross-validation to obtain the best penalty factor and kernel function width of the Radial Basis Function (RBF) kernel function. The extracted features of production data are then used for prediction by inputting them into the improved support vector regression model, based on a shallow CNN without dimensionality reduction. Finally, the prediction accuracy of the CNN-ISVR model is evaluated using the three commonly used evaluation metrics: Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE) and coefficient of determination (R²). The model’s prediction results are then compared to those of other models. The CNN-ISVR hybrid model is shown to outperform other models in terms of prediction accuracy and generalization ability, demonstrating the effectiveness of the proposed model. Full article

The reliable operation of monoblock centrifugal pumps (MCP) is crucial in various industrial applications. Achieving optimal performance and minimizing costly downtime requires effectively detecting and diagnosing faults in critical pump components. This study proposes an innovative approach that leverages deep transfer learning techniques. An accelerometer was adopted to capture vibration signals emitted by the pump. These signals are then converted into spectrogram images which serve as the input for a sophisticated classification system based on deep learning. This enables the accurate identification and diagnosis of pump faults. To evaluate the effectiveness of the proposed methodology, 15 pre-trained networks including ResNet-50, InceptionV3, GoogLeNet, DenseNet-201, ShuffleNet, VGG-19, MobileNet-v2, InceptionResNetV2, VGG-16, NasNetmobile, EfficientNetb0, AlexNet, ResNet-18, Xception, ResNet101 and ResNet-18 were employed. The experimental results demonstrate the efficacy of the proposed approach with AlexNet exhibiting the highest level of accuracy among the pre-trained networks. Additionally, a meticulous evaluation of the execution time of the classification process was performed. AlexNet achieved 100.00% accuracy with an impressive execution (training) time of 17 s. This research provides invaluable insights into applying deep transfer learning for fault detection and diagnosis in MCP. Using pre-trained networks offers an efficient and precise solution for this task. The findings of this study have the potential to significantly enhance the reliability and maintenance practices of MCP in various industrial settings. Full article

This paper presents the experimental validation of a Fault-Tolerant Control (FTC) system for Permanent Magnet Synchronous Motor (PMSM) drives, specifically focusing on current sensors. The FTC system is designed to detect and diagnose both single and multiple faults in the current sensors and to reconfigure the control loop to ensure uninterrupted operation in the presence of such faults. Several crucial aspects are addressed in the proposed approach, including fault detection, isolation of faulty sensors, and reconfiguration of the control system through accurate current estimation. To achieve this, a novel adaptation of the Luenberger observer is proposed and employed for estimating the stator currents. The effectiveness of the fault-tolerant control strategy is demonstrated through experimental tests conducted on a 7.2 kW PMSM utilizing a field-oriented vectorial strategy implemented in a dSpace 1104 platform. Full article

This article outlines a technology for hole-finishing in short-length cylinder lines to improve wear resistance. The technology is based on an optimized diamond-burnishing (DB) process. The latter was implemented on conventional and CNC lathes, milling machines, and machining centers using a simple burnishing device with an elastic beam. The material used in this study was AISI 321 austenitic stainless steel. The governing factors used were the radius of the diamond insert, burnishing force and feed rate. The objective functions relating to surface integrity characteristics were selected on the basis of their functional importance relative to the wear resistance of the processed hole surface: height and shape roughness parameters, surface microhardness, and surface residual axial and hoop stresses. The one-factor-at-a-time method (used to reduce the factor space), a planned experiment, and regression analyses were used. The multi-objective optimization tasks, which were defined for three diamond insert radius values of 2, 3, and 4 mm, were solved via the Pareto-optimal solutions approach available for a non-dominated sorting genetic algorithm (NSGA-II). Using the optimal values of the governing factors selected from the Pareto fronts, cylinder lines were processed. Samples were then cut from these cylinder lines for reciprocating sliding wear tests under two modes: dry friction and boundary lubrication friction. Additionally, wear test samples were cut from the cylinder line, which was finished with traditional grinding. A finite element simulation was then used to select an appropriate pressing force. The results obtained from the reciprocating sliding wear tests under both the dry and boundary lubrication friction regimes show that to minimize the wear on cylinder lines made of AISI 321 steel, DB with a diamond insert of radius 2 mm is the optimal finishing process. Full article

In this paper, the model reference adaptive system (MRAS) method has been employed to observe speed in sensorless field-oriented control (FOC) with flux weakening (FW) and maximum torque per ampere (MTPA) operations for the interior permanent-magnet synchronous motor (IPMSM). This paper focuses on the modified MRAS observer, which is based on the sigmoid function as a switching function and also the adaptive sliding mode coefficient. The sliding mode strategies are employed for the adaptation mechanism instead of the PI controller. The conventional PI-MRAS causes oscillations in rotor speed. To solve this problem, the modified adaptive super-twisting algorithm (STA)-based MRAS method is proposed by utilizing the sigmoid function. The proposed modified MRAS is compared to conventional methods. Additionally, it is examined for performance against the fast terminal sliding mode (FTSM), which is applied to the MRAS as an adaptation mechanism in terms of sliding mode strategies. The modified STA-MRAS is explored under the ECE and EUDC (Extra Urban Driving Cycle) drive cycles for electric vehicle applications. Finally, the obtained results show the validity and capability of the proposed adaptive STA-MRAS in terms of speed tracking. Full article

This paper presents the utilization of robust nonlinear control schemes for a single-rotor unmanned aerial vehicle (SR-UAV) mathematical model. The nonlinear dynamics of the vehicle are modeled according to the translational and rotational motions. The general structure is based on a translation controller connected in cascade with a P-PI attitude controller. Three different control approaches (classical PID, Super Twisting, and Adaptive Sliding Mode) are compared for the translation control. The parameters of such controllers are hard to tune by using a trial-and-error procedure, so we use an automated tuning procedure based on the Particle Swarm Optimization (PSO) method. The controllers were simulated in scenarios with wind gust disturbances, and a performance comparison was made between the different controllers with and without optimized gains. The results show a significant improvement in the performance of the PSO-tuned controllers. Full article

Since musculoskeletal disorders are one of the most common work-related diseases for assemblers and machine operators, it is crucial to find new ways to alleviate the physical load on workers. Support systems such as exoskeletons or handheld power tools are promising technology to reduce the physical load on the humans. The development of such systems requires consideration of the interactions between human and technical systems. The physical relief effect of the exoskeleton can be demonstrated in experimental studies or by simulation with the digital human model (DHM). For the digital development of these support systems, an application-oriented representation of the workload is necessary. To facilitate digital development, an application-oriented workload model (ApOL model) of an overhead working task is presented. The ApOL model determines the load (forces, torques) onto the DHM during an overhead screw-in task using a cordless screwdriver, based on experimental data. The ApOL model is verified by comparing the simulated results to the calculated values from a mathematical model, using experimental data from three participants. The comparison demonstrates successful verification, with a maximum relative mean-absolute-error (rMAE) of the relevant load components at 11.4%. The presented ApOL model can be utilized to assess the impact of cordless screwdriver design on the human workload and facilitate a strain-based design approach for support systems e.g., exoskeletons. Full article

The success of laser communications heavily relies on the stiffness, dynamic properties, static performance, and manufacturability of the U-frame. The U-frame is a fundamental element in satellite-to-ground laser communication telescopes. However, there is currently a lack of research on the optimal design of U-frames, leading to a significant gap between ideal construction and practical manufacturability. To address these concerns, this study proposes a comprehensive approach that combines multi-objective topology optimization and multi-start size optimization techniques. This approach considers the multidisciplinary constraints imposed by mechanical, control, and optical systems. The objective is to achieve both the conceptual and detailed design of a novel U-frame, while also ensuring thorough consideration of the structure’s manufacturability during the optimization process. The prototype of the optimized U-frame was successfully fabricated using laser welding processes. The tensile test conducted on the prototype supported the idea that laser welding can enhance the micro-grain size of the joint, leading to improved overall mechanical properties. In particular, the joint strength achieved through laser welding was found to be 1.5 times greater than that achieved through TIG (Tungsten Inert Gas) welding. Additionally, the results obtained from the free vibration experiment closely aligned with the simulation, confirming the feasibility of manufacturing the optimized structure. The optimized structure demonstrated an improvement of 7.13% in dynamic performance and 29.61% in static performance compared to the first-generation structure. Additionally, there was a reduction of 29.89% in mass without affecting the remaining performance aspects. The successful fabrication of the prototype validates the feasibility of the proposed welding process and highlights the superiority of the new U-frame. Full article

Addressing the inherent drawbacks of conventional positive displacement pumps, such as complex structure, poor sealing, low volumetric efficiency, and high noise, an innovative design of an elliptical rotor scraper pump (ERSP) was proposed. By segregating the pump chamber into high-pressure and low-pressure cavities, the scraper minimizes operational noise and significantly improves volumetric efficiency. To analyze the motion state of the ERSP, a mathematical model was established, determining the coordinated movement between the scraper and rotor using different optimization methods. The equations of coordinated action were derived and validated with relevant software through constraints applied to three algorithms and polynomial fitting. The flow field model of the ERSP was defined based on the established coordinated movement equation, and computational fluid dynamics (CFD) simulations were conducted to analyze pressure and velocity fields within the pump. A prototype of the ERSP was fabricated and tested, confirming its feasibility and advantages in enhancing fluid pressure and flow speed. This study provides valuable insights into the dynamic characteristics and structural optimization of fluid rotor pumps, contributing to anticipating and resolving potential faults and promoting the development of fluid power machinery. Full article