Electrical Discharge Machining Research Articles

PERFORMANCE EVALUATION AND ECONOMIC ASSESSMENT IN ELECTRICAL DISCHARGE MACHINING OF SS316 USING GRAPHITE POWDER DISPERSED IN BIODEGRADABLE PALM OIL DIELECTRIC

In today’s rapidly evolving manufacturing industry, researchers and engineers are constantly seeking innovative methods to improve machining processes. One such area of interest is the use of electrical discharge machining (EDM) for difficult-to-machine metals like SS316. This research compares the performance and economic aspects of traditional EDM and powder-mixed EDM when machining SS316 using copper electrode. Graphite powder dispersed in biodegradable palm oil is investigated as a dielectric media with the aim of enhancing EDM performance. According to the findings, the analysis of the cutting performance of different machinability aspects revealed that using graphite powder mixed with palm oil as a dielectric in EDM outperformed conventional EDM relating to material removal, surface finish, surface hardness, electrode wear rate and hole overcut. The flushing condition and unique properties of graphite powder and palm oil, such as cooling, and thermal conductivity, contributed to the improved performance observed in EDM processes. These findings highlight the potential of graphite powder mixed with palm oil as a promising alternative dielectric in EDM, offering enhanced machining capabilities and improved surface quality. The total machining expenditure per part under PMEDM (INR 505.50) is lowered by 9.2% than expenditure under conventional EDM (INR 525). The surface microhardness of the machined component attained using graphite powder-mixed EDM is enhanced in comparison to that achieved using standard EDM due to the creation of a carbon-rich re-solidified layer.

A comprehensive review of parametric optimization of electrical discharge machining processes using multi-criteria decision-making techniques

Optimization of electrical discharge machining (EDM) processes is a critical issue due to complex material removal mechanism, presence of multiple input parameters and responses (outputs) and interactions among them and varying interest of different stakeholders with respect to relative importance assigned to the considered responses. Multi-criteria decision making (MCDM) techniques have become potent tools in solving parametric optimization problems of the EDM processes. In this paper, more than 130 research articles from SCOPUS database published during 2013–22 are reviewed extracting information with respect to experimental design plans employed, materials machined, dielectrics used, process parameters and responses considered and MCDM tools applied along with their integration with other mathematical techniques. A detailed analysis of those reviewed articles reveals that the past researchers have mostly preferred Taguchi’s L9 orthogonal array as the experimental design plan; EDM oil as the dielectric fluid; medium and high carbon steels as the work materials; peak current and pulse-on time as the input parameters; material removal rate, tool wear rate and surface roughness as the responses; and grey relational analysis as the MCDM tool during conducting and optimizing EDM operations. This review paper would act as a data repository to the future researchers in understanding the stochastic behaviour of EDM processes and providing guidance in setting the tentative operating levels of varying input parameters along with achievable response values. The extracted dataset can be treated as an input to any of the machine learning algorithms for subsequent development of appropriate prediction models. This review also outlines potential future research avenues, emphasizing advancements in EDM technology and the integration of innovative multi-criteria decision-making tools.

Near Dry Powder Mixed Electric Discharge Machining of AA7050 Hybrid Composites Utilizing Composite Tool Materials

Electric Discharge Machining (EDM) is a prominent technique for producing components with complex geometry and intricate shapes. However, productivity depends on the selection of optimal input variables. This research investigates EDM and Near-Dry EDM (NDEDM) on AA7050 hybrid composites, exploring the impact of tool materials, Al2O3 powder concentration, and dielectric fluids on Material Removal Rate (MRR) and Surface Roughness (Ra). NDEDM offers a higher MRR due to localized heating and energy concentration, while hydrocarbon oil EDM provides lower Ra due to cooling and lubrication. Tool material properties, thermal conductivity and bonding strength, affect MRR and TWR. Aluminum composite tools via stir casting display a strong bonding interface, leading to efficient energy transfer and higher MRR, while copper composite tools via friction stir processing exhibit weaker bonding, potentially reducing MRR. Hydrocarbon oil significantly lowers TWR due to effective lubrication and cooling, while near dry EDM results in slightly higher TWR. Al2O3 powder enhances MRR, with an optimal concentration of 1 g/l, while higher concentrations hinder MRR. The presence of Al2O3 powder enhanced the surface quality by reducing the size of the recast layers and producing a smoother surface. Globules, craters, microcracks, pock marks and re-casted layers are the noticeable features spotted on the surface. The results were optimized using the TOPSIS optimization technique and process parameters 32 A current, 30 µs Ton, 3 mm gap distance machined under 3 g/l Al2O3 incorporated hydrocarbon oil dielectric medium using copper composites tool proffers optimal machining performance.

Comprehensive Analysis of Heavy Metal Aerosol Emissions and Health Risk from the Electrical Discharge Machining Process: A Control and Mitigation Approach for Green Manufacturing

Comprehensive Analysis of Heavy Metal Aerosol Emissions and Health Risk from the Electrical Discharge Machining Process: A Control and Mitigation Approach for Green Manufacturing

Towards artificial intelligence empowered performance enhancement of EDM process using nano-graphene mixed bio-dielectric supporting the carbon neutrality and sustainable development

The growing population with every passing day sets an alarming situation with respect to the conservation climate protocols. The increasing needs of society also demand a significant enhancement in the manufacturing capacity to augment the situation. However, it's a stringent requirement of the hour to propose sustainable and clean manufacturing processes to realize the goal of carbon neutrality to support a healthy life on the earth. Specifically, the processes that are energy intensive like electric discharge machining (EDM) are of serious concern regarding sustainability viewpoint. The role of the said process cannot be essentially eliminated as advent of new materials of superior characteristics demand the application of EDM for accurate cutting of intricate profiles. Nevertheless, the commonly used oil-based dielectric (kerosene) in EDM releases aerosol, deposit particles, oxides of carbon (CO2 & CO), thus contributing to the environmental contamination. It is pertinent to mention that industries are compelled to tune their processes to achieve the goals of Net-Zero. Therefore, this study thoroughly investigates the potential of nano-graphene mixed rice bran oil to make the EDM process cleaner and sustainable which has never been investigated so far. Moreover, the process has been successfully modeled using artificial neural network (ANN) and optimized by non-dominated sorting genetic algorithm-II (NSGA-II) which is another novel aspect of this study as it eradicates the need of extensive experimentation. Experimentation has been performed via Taguchi's experimental strategy followed by a detailed explanation of the findings based on process physics. In comparison to the traditional dielectric an improvement of 98.8% in material removal rate (MRR) and 93.9% reduction in specific energy consumption (SEC) are realized if the said novel combination is applied without compromising the quality. CO2 emissions determined for both rice bran oil and kerosene oil have revealed that rice bran oil provides 99.96% lesser CO2 emission in comparison to its counterpart.

A study on die-sinking electrical discharge machining behaviour of friction stir processed AA5052/WS2 surface composite

A study on die-sinking electrical discharge machining behaviour of friction stir processed AA5052/WS₂ surface composite

Multi-objective optimization of wire electrical discharge machining process using multi-attribute decision making techniques and regression analysis

Wire electrical discharge machining (WEDM) is one of the most important non-traditional machining methods that is widely used in various industries. The present research work is concerned with the influences of process variables on quality of machined specimen obtained from WEDM process. The process parameters to manufacture mold structure included wire feed speed, wire tension and generator power, and in the current research, the effects of these variables on the aim factors, namely dimensional accuracy, hardness and roughness of product surface have been investigated, simultaneously. In order to obtain the optimal experiment, the multi-objective optimization with discrete solution area has been employed. Method based on the removal effects of criteria (MEREC) and weighted aggregates sum product assessment (WASPAS) techniques have been used with the aim of weighting the objective functions and discovering the best practical experiment. In the following, the regression analysis has been employed to study the effects of variables on response factors. A good correlation between the results gained from two analysis methods was observed. Based on MEREC-WASPAS hybrid technique, the weights of roughness, hardness and dimensional accuracy of machined part were calculated to about 89%, 9% and 2%, respectively. In the selected optimal experiment, the amount of wire feed speed, wire tension and generator power variables were considered to, in turn, 2 cm/s, 2.5 kg, and 10%.

Nanopowder-mixed EDM on biodegradable Mg alloy AZ31B for improved surface characteristics and biocompatibility

Powder-mixed electrical discharge machining (PM-EDM) was conducted on biodegradable magnesium (Mg) alloys for biomedical applications. The Mg alloy AZ31B has shown outstanding mechanical properties, similar to human bone, and is lightweight. Efforts were made to enhance the EDM capabilities by a PM-EDM process using nanopowder mixed in the dielectric medium and copper tool electrode amid the machining of Mg alloy AZ31B, followed by investigating surface modification by minimum machining time, morphology, chemistry, topography and wettability of the surfaces. The surface produced by nano PM-EDM operation exhibits minimal cracks and craters, an acceptable range of recast layer thickness (2.4 − 2.7μm), satisfactory corrosion resistance and surface roughness (Ra = 2.87μm) as well as suitable hydrophobic behaviour (CA122q) and bioactivity with Ringer’s solution, suggesting an improvement in the degradation rate. Thus, PM-EDM-induced surface modification suggests the suitability of Mg alloy AZ31B as a biodegradable implant in adults and children.

Study on WEDM process parameters on machining of SS304

Wire Electrical Discharge Machining (WEDM) is well suited advanced machining process for cutting complex and intricate geometries of almost all materials having good electrical conductivity. Stainless steel SS304 (SS304) is ubiquitously preferred in various industries not limited to medical, semiconductors, electronic, Tool and die industries, and aerospace industries. Processing of stainless steel via traditional route is tedious as it has less thermal conductivity and more affinity towards built up edge formation, it further leads to poor surface conditions and extreme tool wear. In the current work, an attempt is made to study the parametric effect on surface characteristics and material removal rate of SS304 in WEDM where pulse timings like on time and off time along with current and wire feed were taken as input factors. The surface roughness and material removal rate were considered as output factors. The experimental results were evaluated through Taguchi methodology. Besides the surface morphology of the material after WEDM machining was investigated.

Permanent helical micro-undulators for x-ray free electron lasers

Helical Halbach undulators, formed by four alternately axially and radially magnetized rare-earth helices, produce a stronger field than two planar Halbach undulators with perpendicular polarization. Hybrid undulators of two longitudinally premagnetized rare-earth helices and two initially unmagnetized steel (or vanadium permendur) helices produce an equal or higher field and are easier to implement. We made sure that the required helices with a period of down to 3 mm or even less could be manufactured with high precision using Wire Electric Discharge Machining. According to the simulation results, when using identical electron bunches with moderate initial spreads in energy and angles, the gain in x-ray free electron laser (XFEL) power from replacing planar micro-undulators with the helical micro-undulators under consideration can be significantly greater than predicted by the simplest 1D theory with a cold electron beam. This makes such micro-undulators promising for creating compact XFELs of various types, both based on modern linear accelerators and plasma wakefield accelerators.

Effect of element infiltration on radial composition/structure and magnetic/electrical properties of Fe-B amorphous powders prepared by spark erosion

Before confirming spark erosion as an effective technique for producing Fe-based amorphous powders, it is crucial to thoroughly investigate their magnetic/electrical properties and the impact of different elements infiltration on composition, microstructure, and performances. Limited research has been conducted on the magnetic and electrical properties of spark-eroded Fe-based amorphous powders, and there is a dearth of studies on the influence of different elements' infiltration. In this work, a series of binary Fe83B17 amorphous powders were synthesized using water (as a source of O), ethanol (as a source of C and O), and silicone oil (as a source of O, Si, and C). The elements O, C, and Si exhibit different concentration distributions along the radial direction in the synthesized powders. The depth of O infiltration varies between 10 to 35nm, C from 190 to 280nm, and Si barely diffuses inward. For powders prepared in water (WF), the formation of nanoclusters throughout the particle leads to an improved saturation magnetization. Besides, the infiltrated mass ratio of O is merely 1.38wt.% and is predominantly concentrated in the surface of WF powder, resulting in a significant increase in resistivity. The resistivity of WF powder is approximately four orders of magnitude higher compared to powders prepared in ethanol (EF) and silicone oil (SF). Nanoclusters are also observed in EF and SF powders, where the significant infiltration of non-magnetic C leading to a reduction in the saturation magnetization of both EF and SF powders. Inside EF powder, a remarkable peak of C content appears at ~30nm away from the powder surface in the radial direction, accompanied by an increase in structural order. This suggests a synchronous relationship between the variations of structural order in the radial direction and the concentration of the infiltration element. These findings demonstrate the practical feasibility of tuning composition and microstructure via dielectrics to develop high-performance Fe-based amorphous powders.

Research on N-type silicon crystal processing by liquid-immersion wire electrical discharge machining

Passivation at the input end of N-type silicon crystals leads to a decrease in the efficiency of wire electrical discharge machining (WEDM). In response to this problem, this paper proposes a method for the liquid-immersion WEDM of N-type silicon crystals. An equivalent circuit model for the WEDM of N-type silicon crystals was established, the volt-ampere characteristics of N-type silicon crystals immersed in different liquids at the incoming end were studied, and an experimental platform for the liquid-immersion WEDM of N-type silicon crystals was established. The experimental results demonstrate that immersion in deionized water can effectively suppress the passivation phenomenon at the input end of N-type silicon crystals, thus improving the chip removal conditions between electrodes. Compared to open-loop control machining, the machining efficiency was found to increase by 42.7 % and the surface roughness was decreased by 32.8 %. After continuous machining for 1 h, the current in the machining circuit did not significantly decrease, and the machining efficiency remained unchanged.

Comparative Life Cycle Assessment of a centrifugal compressor impeller: Hybrid technology production versus welding production

Sustainable development is one of the main objectives in the turbomachinery sector to achieve the climate neutrality targets set by 2050 protocols. The manufacturing industry consumes an average of 23% of global energy, significantly contributing to the generation of greenhouse gases and other pollutants. Therefore, it is crucial to research technological solutions aimed at reducing consumption for the benefit of the environmental sustainability of the products manufactured. This study presents a comparative environmental impact assessment using the Life Cycle Assessment (LCA) methodology to evaluate the sustainability of two technological alternatives used to produce centrifugal impellers in the industrial sector. The first process analyzed deals with making an impeller by welding the shroud to the impeller body. The second process involves producing the component from a semi-finished product, machined through milling and Electrical Discharge Machining (hybrid solution). The study has been conducted at Nuovo Pignone company. It is based on primary inventory data collected through specific measurement campaigns and modeled using the environmental database Ecoinvent v3.9. The results were produced using the ILCD Midpoint method, and four different impact categories were investigated: Climate Change, Human Toxicity, Cancer Effects, and Freshwater Ecotoxicity. The results demonstrate that the solution produced through hybrid manufacturing is generally less impactful compared to the welded one. The insight implemented in the work is functional in drafting suggestions for manufacturers and customers to reduce the environmental burdens of the examined product.

Modeling and optimization of energy consumption in wire cut electrical discharge machining

WEDM (Wire Electrical Discharge Machining) is widely implemented in the manufacture of dies and challenging materials due to its use of spark discharge to shape a workpiece. However, existing research focuses primarily on enhancing processing efficiency, accuracy, and quality, with limited investigation into energy conservation and emissions reduction during processing. This paper constructs an energy consumption model to forecast energy usage during electric machining and proposes a low energy consumption optimization model founded on processing parameters. A case study revealed that the energy consumption model is 98.06 % accurate throughout the processing process and at least 92 % precise in predicting energy consumption for a single process. Upon utilizing the low energy consumption optimization model, the results show a reduction in energy consumption and carbon emissions by 11.5 % and 5.74 %, respectively, compared to traditional methods. The models presented in this paper can enhance EDM machining efficiency while concurrently minimizing energy consumption and carbon emissions, thereby supporting the adoption of energy-saving and efficient practices in electric machining.

Optimization of Process Parameters of Wire Electric Discharge Machining Process for Machining AISI A2 Tool Steel

In this investigation, AISI A2 tool steel is considered as the workpiece material, which is typically used to manufacture blanking tools, punches die etc., due to its good toughness and wear resistance. In this work, the effect of controlling parameters of the Wire Electric Discharge Machining (WEDM) process is investigated. Molybdenum tool electrodes of 0.18mm diameter and de-ionized water dielectric medium are utilized. Peak current, on-time, off-time and voltage are considered as the controlling parameters. Surface roughness average and material erosion rate are considered as the response parameters. The type of design of experiments considered for this work is Taguchi’s L27 orthogonal array. Analysis of variance indicates the percentage contribution of each machining parameter on response parameters. The optimum combination of machining parameters yields a minimum surface roughness of 2.87 μm and the highest material removal rate obtained in this work is 774 mm3/hr.

Helicopter Planet Gear Rim Crack Diagnosis and Trending Using Cepstrum Editing Enhanced with Deconvolution.

Detecting gear rim fatigue cracks using vibration signal analysis is often a challenging task, which typically requires a series of signal processing steps to detect and enhance fault features. This task becomes even harder in helicopter planetary gearboxes due to the complex interactions between different gear sets and the presence of vibration from sources other than the planetary gear set. In this paper, we propose an effectual processing algorithm to isolate and enhance rim crack features and to trend crack growth in planet gears. The algorithm is based on using cepstrum editing (or liftering) of the hunting-tooth synchronous averaged signals (angular domain) to extract harmonics and sidebands of the planet gears and low-pass filtering and minimum entropy deconvolution (MED) to enhance extracted fault features. The algorithm has been successfully applied to a vibration dataset collected from a planet gear rim crack propagation test undertaken in the Helicopter Transmission Test Facility (HTTF) at DSTG Melbourne. In this test, a seeded notch generated by an electric discharge machine (EDM) was used to initiate a fatigue crack that propagated through the gear rim body over 94 load cycles. The proposed algorithm demonstrated a successful isolation of incipient fault features and provided a reliable trending capability to monitor crack progression. Results of a comparative analysis showed that the proposed algorithm outperformed the traditional signal processing approach.

Design and test of a quasi-zero stiffness isolator with machinable springs

Quasi-zero stiffness (QZS) vibration isolators are widely used for vibration isolation due to their high performance, but their low load-carrying capacity limits their applications. In this paper, a heavy-load-bearing QZS vibration isolator consisting of three compression springs is investigated for isolating ultra-low frequency vibrations. From the theoretical aspect, parameter optimization considering both load mass and isolation performance metric (IPM) is implemented, and a parameter guidance diagram is proposed for parameter design. From the practical point of view, two innovative springs are designed and fabricated by wire-electrical discharge machining (W-EDM), referring to the idea of compliant mechanism. These creative designs are endowed with precise stiffness, large compression and buckling avoidance, which further improve the system properties. Finally, a prototype is made and experiments are carried out to verify the effectiveness of the design. Compared with linear isolators, the experimental results of QZS isolator show better isolation performances in a wider frequency range.

Performance analysis of EDM parameters on silicon nitride-titanium nitride composite for improving geometrical tolerances: an integrated GRA-RSM approach

ABSTRACT In the modern context, ceramic composites are being developed not just for increased strength and hardness but also for the ability to manufacture difficult to machine geometries that can use Electrical Discharge Machining. Sparking parameters such as current (I), pulse on time (Ton), pulse off time (Toff), and dielectric flushing pressure (P) are improved with multi-response factors such as material removal rate, Electrode Wear Rate, and Geometrical Errors. The results of the confirmation experiments indicate that machining time was decreased from 12.517 to 10.121 min, Material Removal Rate was increased from 0.0183 to 0.0215 g/min, Electrode Wear Rate was reduced from 0.0024 to 0.0002 g/min, circularity was decreased from 0.175 mm to 0.098 mm, cylindricality was decreased from 0.761 to 0.541 mm, and perpendicularity was decreased from 1.071 to 0.085 mm. By optimizing the data to its optimum level, the gray relational grade has been enhanced to 0.16464. Consequently, the experimental findings suggest that gray relational analysis can be effectively employed to optimize the machining settings of Spark Electrical Discharge Machining for various performance aspects.

Comprehensive stratified K-fold machine learning for surface roughness prediction with 3D printed electrodes: Analyzing dielectric variability and heat treatment effects

Electrical discharge machining (EDM) is popular for its accuracy in cutting difficult materials. Material removal rate (MRR), electrode wear ratio (EWR) and surface roughness (SR) are deemed as the success indicators in EDM. The positive attributes of sustainable manufacturing make it a suitable choice for diverse applications. EDM electrodes used in this work are prepared by 3D printing and further subjected to deep cryogenic treatment (DCT) to investigate the influence of heat treatment on EDM performance. In the second instance, the experiments are conducted with different dielectric fluids viz. conventional dielectric (CD) and lemon peel biodiesel dielectric (LPD) to analyze the impact of dielectric change. When the dielectric is changed to lemon peel biodiesel, the roughness of the electro-discharged machined surface is reduced to a maximum of about 33%. A further improvement of about 11% is possible in the surface finish of machined surfaces when the EDM electrodes are subjected to DCT under an identical operating condition. LPD as dielectric also helps in attaining the primary objective of green manufacturing by reducing specific evaporative emissions. During the EDM process, LPD adoption causes a reduction of 96% in nitrogen oxide (NO) emissions and 86% in hydrocarbon (HC) emissions. A modified adaptive neuro fuzzy inference system (ANFIS) with a stratified k-fold machine learning approach with a prediction accuracy of about 95% is also developed in this work to envisage the surface roughness of electro-discharge machined surfaces.

Comparative performance evaluation and economic assessment between traditional and vibration assisted EDM during machining of Inconel 718

Nickel based superalloy, Inconel 718 offers extensive applications in aerospace and gas turbine industries because of its outstanding properties, enabling high performance in various critical components. Despite the wide application of electric discharge machining (EDM) for difficult-to-cut conductive materials, the process becomes less productive due to unstable arcing and inadequate debris flushing during machining, leading to reduced operational efficiency. This research focuses on comparative performance evaluation and economic assessment between conventional-EDM and vibration assisted EDM (VA-EDM) while machining Inconel 718 with copper electrode. The workpiece was excited to vibrate with low frequency by employing unbalanced mass attached in a rotary motor with the aim of enhancing EDM performance. Material removal rate MRR, surface integrity (surface crack density SCD, surface roughness Ra, white (recast) layer thickness WLT) and tool wear rate TWR are analyzed to determine machining performance in comprehensive analysis. The results of the performance comparison show that vibration assisted is superior to the conventional-EDM process concerning enhanced material removal rate, and surface morphology with minimum surface crack density, tool wear rate and white layer thickness. The mean value ratios for Ra EDM/ Ra VA-EDM, TWR EDM/ TWR VA-EDM, WLT EDM/ WLT VA-EDM, and SCD EDM/ SCD VA-EDM are 1.35, 1.52, 1.21, and 1.74, respectively. Additionally, the surface integrity was enhanced using vibration-assisted EDM because it (a) increases the plasma flushing efficiency and the gap pressure gradient; (b) raises the energy of electrically charged plasma particles; (c) increases the effective flow of dielectric in the gap; (d) minimizes the number of ineffective pulses (short-circuiting and arching) on the surface; and (e) improves the dielectric flushing conditions in sparking gap. According to the findings, the overall machining expenditure per part under VA-EDM in Indian rupees (INR) 534 is lowered by 8% than expenditure under conventional-EDM (INR 576.60). The outcomes of this research have the potential to benefit numerous industries that deal with difficult-to-cut materials.