Search Results (603)

Refractory high-entropy alloys (RHEAs) are categorized as difficult-to-machine materials due to their excellent mechanical properties. Electrical discharge machining (EDM) is a special processing method for RHEAs, which faces challenges such as low machining efficiency. In this work, electrochemical discharge machining (ECDM) was proposed for (TiVZrTaW)_99.5N_0.5 and (TiVZrTa)W₅ (at. %, denoted as W20N0.5 and W5, respectively) RHEAs, and their machining performances were investigated and compared with EDM. At a peak current of 25 A, the material removal rate (MRR) using ECDM is more than twice that of EDM for W20N0.5 (reaching to 1.24 mm³/min) and 1.5 times higher than that for W5. Both W20N0.5 and W5 RHEAs exhibited higher MRR in ECDM based on the analyses of the influence of top diameter, bottom diameter, machining depth, and surface roughness (Ra). The process and mechanisms of material removal were examined through the microstructural morphology and elemental distribution analyses. This work proposed a more effective route for machining RHEAs by ECDM compared to the conventional EDM. Full article

Film-cooling holes are the key cooling structures of turbine blades, and there are still great challenges in manufacturing high-quality film-cooling holes. Although abrasive flow machining can be used as a post-processing technique to optimize the quality of film-cooling holes, its action process and influence mechanism have not been systematically studied. Herein, the drilling method of femtosecond laser combined with abrasive flow is studied in detail. Moreover, for comparison, the drilling methods of single femtosecond laser, single electrical discharge machining, and electrical discharge machining combined with abrasive flow are also discussed. The microstructure and composition distribution of the hole walls before and after abrasive flow machining were systematically characterized, indicating that abrasive flow can effectively remove the recast layer and cause local plastic deformation. Due to the surface hardening and non-uniform residual stress caused by abrasive impact, abrasive flow machining can increase the high-temperature endurance time of film-cooling holes while reducing the elongation. The combination of femtosecond laser and abrasive flow machining demonstrates the best high-temperature mechanical properties, with the endurance time and elongation reaching 136.15 h and 12.1%, respectively. The fracture mechanisms of different drilling methods are further discussed in detail. The research results provide theoretical guidance for the manufacturing of high-quality film-cooling holes through the composite processing of femtosecond laser and abrasive flow. Full article

Silicon carbide (SiC) ceramics have gained significant attention in advanced engineering applications because of their superior mechanical properties, resistance to wear and corrosion, and thermal stability. However, the precision machining of these materials is extremely challenging because of their intrinsic hardness and brittleness. Wire Electrical Discharge Machining (WEDM) has become increasingly popular as a viable technique for processing SiC ceramics because of its ability to produce intricate geometries and high-quality surface finishes. In this review paper, a comprehensive overview of WEDM technology applied to SiC ceramics is presented, emphasizing the influence of process parameters, wire materials, and dielectric fluids on cutting efficiency and quality. This research explores recent experimental findings related to Wire Electrical Discharge Machining (WEDM) and highlights the challenges in reducing material damage. It also presents strategies to improve machining performance. Additionally, potential future directions are discussed, providing a roadmap for further research and the application of WEDM in processing silicon carbide (SiC) and its variants, including solid silicon carbide (SSiC) and silicon-infiltrated silicon carbide (SiSiC). Full article

In the micro-EDM blind-hole machining of C_f-ZrB₂-SiC ceramics, defects such as bottom surface protrusion and machining fillets are often encountered. The implementation of an electrode-assisted oscillating device has proven effective in improving machining outcomes. To unravel the fundamental reasons behind the optimization enabled by this auxiliary oscillating device, this paper presents fluid simulation research, providing a quantitative comparison of the differences in machining gap flow field characteristics and debris motion behaviors under conditions with and without the assistance of the oscillating device. Firstly, this paper briefly describes the characteristics of C_f-ZrB₂-SiC discharge products and flow field deficiencies during conventional machining and introduces the working principle of electrode-assisted oscillation devices to establish the background and objectives of the simulation study. Subsequently, this research established simulation models for both conventional machining and oscillating machining based on actual processing conditions. CFD numerical simulations were conducted to compare flow field differences between conditions with and without auxiliary machining devices. The results demonstrate that, compared to conventional machining, electrode oscillation not only increases the maximum velocity of the working fluid by nearly 32% but also provides a larger debris accommodation space, effectively preventing secondary discharge. Regarding debris agglomeration, oscillating machining resolves the low-velocity zone issues present in conventional modes, increasing debris velocity from 0 mm/s to 7.5 mm/s and ensuring continuous debris motion. Furthermore, the DPM was used to analyze particle distribution and motion velocities, confirming that vortex effects form within the hole under oscillating conditions. These vortices effectively draw bottom debris outward, preventing local accumulation. Finally, from the perspective of debris distribution, the formation mechanisms of micro-hole morphology and the tool electrode wear patterns were explained. Full article

Examining the electrical discharge machining (EDM) process is challenging in manufacturing technology due to the complexity of the physical events that take place in the gaps between electrodes. In this study, we examined the EDM process in detail and developed multiple machine learning (ML) models to describe the relationship between the EDM independent (process parameters) and dependent (responses) variables. The collected experimental data was used to train the machine learning models. According to the results, the GPR model outperformed other ML models across different materials, with average RMSE values of 0.9234 and 3.0216 for the material removal rate (MRR) and surface roughness (Sa), respectively, highlighting the effectiveness of ML tools at modeling complex machining processes, such as EDM. In addition, as a practical implication, this study opens the door to employing the developed ML models to predict the EDM process performance. Full article

Tribological performance is critical for the longevity and efficiency of machined components in industries such as aerospace, automotive, and biomedical. This study investigates whether electrical discharge machining recast layers can serve as a cost-effective and time-efficient alternative to conventional tungsten inert gas cladding coatings for enhancing surface properties. The samples were prepared using electrical discharge machining and tungsten inert gas cladding. For electrical discharge machining, various combinations of electrical and non-electrical parameters were applied using Taguchi’s L18 orthogonal array. Similarly, tungsten inert gas cladding coatings were prepared using a suitable combination of current, voltage, powder size, and speed. The samples were characterized using, scanning electron microscopy, optical microscopy, microhardness testing, tribological testing, energy-dispersive X-ray spectroscopy, X-ray diffraction analysis and profilometry. The electrical discharge machining recast layers exhibited superior tribological performance compared to tungsten inert gas cladding coatings. This improvement is attributed to the formation of carbides, such as TiC and Ti₆C_3.75. The coefficient of friction and specific wear rate were reduced by 11.11% and 1.57%, respectively, while microhardness increased by 10.93%. Abrasive wear was identified as the predominant wear mechanism. This study systematically compares electrical discharge machining recast layers with tungsten inert gas cladding coatings. The findings suggest that optimized electrical discharge machining recast layers can serve as effective coatings, offering cost and time savings. Full article

Magnetic gears (MGs) are attracting increasing attention in power transmission systems due to their contactless operation principles, low frictional losses, and high efficiency. However, the broad application potential of these technologies requires a comprehensive evaluation of engineering parameters, such as material selection, energy efficiency, and structural design. This review focuses solely on solid-core magnetic gear systems designed using laminated electrical steels, soft magnetic composites (SMCs), and high-saturation alloys. This review systematically examines the topological diversity, torque transmission principles, and the impact of various core materials, such as electrical steels, soft magnetic composites (SMCs), and cobalt-based alloys, on the performance of magnetic gear systems. Literature-based comparative analyses are structured around topological classifications, evaluation of material properties, and performance analyses based on losses. Additionally, the study highlights that aligning material properties with appropriate manufacturing methods, such as powder metallurgy, wire electrical discharge machining (EDM), and precision casting, is essential for the practical scalability of magnetic gear systems. The findings reveal that coaxial magnetic gears (CMGs) offer a favorable balance between high torque density and compactness, while soft magnetic composites provide significant advantages in loss reduction, particularly at high frequencies. Additionally, application trends in fields such as renewable energy, electric vehicles (EVs), aerospace, and robotics are highlighted. Full article

This study presents the first investigation into the application of ultrasonic vibration-assisted electrical discharge machining (UV-EDM) using graphite electrodes for external cylindrical surface machining—an essential surface in the production of tablet punches and sheet metal-forming dies. A custom ultrasonic horn was designed and fabricated using 90CrSi material to operate effectively at a resonant frequency of 20 kHz, ensuring stable vibration transmission throughout the machining process. A Box–Behnken experimental design was employed to explore the effects of five process parameters—vibration amplitude (A), pulse-on time (Ton), pulse-off time (Toff), discharge current (Ip), and servo voltage (SV)—on two key performance indicators: material removal rate (MRR) and surface roughness (Ra). The optimization process was conducted in two stages: single-objective analysis to maximize MRR while ensuring Ra < 4 µm, followed by a hybrid multi-objective approach combining NSGA-II and the Analytic Hierarchy Process (AHP). The optimal solution achieved a high MRR of 9.28 g/h while maintaining Ra below the critical surface finish threshold, thus meeting the practical requirements for punch surface quality. The findings confirm the effectiveness of the proposed horn design and hybrid optimization strategy, offering a new direction for enhancing productivity and surface integrity in cylindrical EDM applications using graphite electrodes. Full article

The increasing utilization of hard-to-cut materials in high-performance sectors such as aerospace and defense has pushed manufacturing systems to be flexible in processing large workpieces with a wide range of materials while also delivering high precision. Recent studies have highlighted the potential of integrating industrial robots (IRs) with electric discharge machining (EDM) to create a non-contact, low-force manufacturing platform, particularly suited for the accurate machining of hard-to-cut materials into complex and large-scale monolithic components. In response to this potential, a novel robotic EDM system has been developed. However, the manual programming and control of such a convoluted system present a significant challenge, often leading to inefficiencies and increased error rates, creating a scenario where the EDM process becomes unfeasible. To enhance the industrial applicability of this robotic EDM technology, this study focuses on a novel methodology to develop and validate a digital twin (DT) of the physical robotic EDM system. The digital twin functions as a virtual experimental environment for tool motion, effectively addressing the challenges posed by collisions and kinematic singularities inherent in the physical system, yet with proven 20-micron EDM gap accuracy. Furthermore, it facilitates a CNC-like, user-friendly offline programming framework for robotic EDM cutting path generation. Full article

This study investigates the surface modification of Ti-6Al-4V alloy through the electrical discharge machining (EDM) process to improve its suitability for orthopedic and dental implant applications. The analysis focused on evaluating the morphological, wettability, roughness, hardness, and biocompatibility properties of the modified surfaces. Samples were subjected to different dielectric fluids and polarities during EDM. Subsequently, optical microscopy, roughness measurements, Vickers microhardness, contact angle tests, and in vitro cytotoxicity assays were performed. The results demonstrated that EDM processing led to the formation of distinct layers on the sample surfaces, with surface roughness increasing under negative polarity by up to ~304% in Ra and 305% in Rz. Additionally, wettability measurements indicated that the modified surfaces presented a lower water contact angle, which suggests enhanced hydrophilicity. Moreover, the modified samples showed a significant increase in Vickers microhardness, with the highest value reaching 1520 HV in the recast layer, indicating improvements in the mechanical properties. According to ISO 10993-5, all treated samples were classified as non-cytotoxic, presenting RGR values above 75%, similar to the untreated Ti-6Al-4V alloy. Therefore, it is concluded that surface modification through the EDM process has the potential to enhance the properties and safety of biomedical implants made with this alloy. Full article

Modern utilities face unprecedented pressures as trends in digital transformation and democratized energy choice empower consumers to engage in peak shaving, flexible load management, and adopt grid automation and intelligence solutions. A powerful confluence of architectural, technological, and socio-economic forces is transforming the U.S. electricity market, triggering significant changes in electricity production, transmission, and consumption. Utilities are embracing digital twins and repurposed Utility 2.0 concepts—distributed energy resources, microgrids, innovative electricity market designs, real-time automated monitoring, smart meters, machine learning, artificial intelligence, and advanced data and predictive analytics—to foster operational flexibility and market efficiency. This analysis qualitatively evaluates how digitalization, Battery Energy Storage Systems (BESSs), and adaptive strategies to mitigate rebound effects collectively advance smart duck curve management. By leveraging digital platforms for real-time monitoring and predictive analytics, utilities can optimize energy flows and make data-driven decisions. BESS technologies capture surplus renewable energy during off-peak periods and discharge it when demand spikes, thereby smoothing grid fluctuations. This review explores the benefits of targeted digital transformation, BESSs, and managed rebound effects in mitigating the duck curve problem, ensuring that energy efficiency gains translate into actual savings. Furthermore, this integrated approach not only reduces energy wastage and lowers operational costs but also enhances grid resilience, establishing a robust framework for sustainable energy management in an evolving market landscape. Full article

The widespread integration of electric vehicles (EVs) into smart grid infrastructures necessitates intelligent and robust battery health diagnostics to ensure system resilience and performance longevity. While numerous studies have addressed the estimation of State of Health (SOH) and the prediction of remaining useful life (RUL) using machine and deep learning, most existing models fail to capture both short-term degradation trends and long-range contextual dependencies jointly. In this study, we introduce V2G-HealthNet, a novel hybrid deep learning framework that uniquely combines Long Short-Term Memory (LSTM) networks with Transformer-based attention mechanisms to model battery degradation under dynamic vehicle-to-grid (V2G) scenarios. Unlike prior approaches that treat SOH estimation in isolation, our method directly links health prediction to operational decisions by enabling SOH-informed adaptive load scheduling and predictive maintenance across EV fleets. Trained on over 3400 proxy charge-discharge cycles derived from 1 million telemetry samples, V2G-HealthNet achieved state-of-the-art performance (SOH RMSE: 0.015, MAE: 0.012, $R^2$: 0.97), outperforming leading baselines including XGBoost and Random Forest. For RUL prediction, the model maintained an MAE of 0.42 cycles over a five-cycle horizon. Importantly, deployment simulations revealed that V2G-HealthNet triggered maintenance alerts at least three cycles ahead of critical degradation thresholds and redistributed high-load tasks away from ageing batteries—capabilities not demonstrated in previous works. These findings establish V2G-HealthNet as a deployable, health-aware control layer for smart city electrification strategies. Full article

Tracking the state of the health of electrical insulation in high-power electric machines has always been a topic of great interest due to the high cost of downtime associated with unexpected failures. Over the years, there have been continuous efforts to develop and improve upon methods for testing and categorizing the health and expected lifetime of stator insulation. Methods such as partial discharge, surge, and dissipation factor testing are common examples. With the increasing use of high-specific-power electric machines in new applications such as traction and wind power generation, coupled with the increasing use of wide-bandgap semiconductor device-based inverters, some traditional methods for insulation health tracking may need adjustments or be combined with newer methods to remain accurate and useful. This paper outlines a review of the traditional insulation health tracking methods and newer methods and improvements that have been proposed to address the concerns and shortcomings of traditional methods. Full article

The ability to predict capacity retention is critical for ensuring the long-term reliability of supercapacitors in energy storage systems. This study presents a comprehensive machine learning framework that integrates both electrical cycling data and experimentally derived material and structural features to forecast the degradation behavior of commercial supercapacitors. A total of seven supercapacitor samples were tested under various current and voltage conditions, resulting in over 70,000 charge–discharge cycles across three case studies. In addition to electrical measurements, detailed physical and material characterizations were performed, including electrode dimension analysis, Scanning Electron Microscopy (SEM), Energy Dispersive X-ray Spectroscopy (EDS), and Thermogravimetric Analysis (TGA). Three machine learning models, Linear Regression (LR), Random Forest (RF), and Multi-Layer Perceptron (MLP), were trained using both cycler-only and combined cycler + material features. Results show that incorporating material features consistently improved prediction accuracy across all models. The MLP model exhibited the highest performance, achieving an R² of 0.976 on the training set and 0.941 on unseen data. Feature importance analysis confirmed that material descriptors such as porosity, thermal stability, and electrode thickness significantly contributed to model performance. This study demonstrates that combining electrical and material data offers a more holistic and physically informed approach to supercapacitor health prediction. The framework developed here provides a practical foundation for accurate and robust lifetime forecasting of commercial energy storage devices, highlighting the critical role of material-level insights in enhancing model generalization and reliability. Full article

The enduring manufacturing goals are increasingly shifting toward ultra-precision manufacturing and micro-nano fabrication, driven by the demand for sophisticated products. Unconventional machining processes such as electrochemical jet machining (ECJM), electrical discharge machining (EDM), electrochemical machining (ECM), abrasive water jet machining (AWJM), and laser beam machining (LBM) have been widely adopted as feasible alternatives to traditional methods, enabling the production of high-quality engineering components with specific characteristics. ECJM, a non-contact machining technology, employs electrodes on the nozzle and workpiece to establish an electrical circuit via the jet. As a prominent special machining technology, ECJM has demonstrated significant advantages, such as rapid, non-thermal, and stress-free machining capabilities, in past research. This review is dedicated to outline the research progress of ECJM, focusing on its fundamental concepts, material processing capabilities, technological advancements, and its variants (e.g., ultrasonic-, laser-, abrasive-, and magnetism-assisted ECJM) along with their applications. Special attention is given to the application of ECJM in the semiconductor and biomedical fields, where the demand for ultra-precision components is most pronounced. Furthermore, this review explores recent innovations in process optimization, significantly boosting machining efficiency and quality. This review not only provides a snapshot of the current status of ECJM technology, but also discusses the current challenges and possible future improvements of the technology. Full article