An insight on Powder Mixed Electric Discharge Machining: A state of the art review

Srivastava, Siddharth; Vishnoi, Mohit; Gangadhar, Mamatha Theetha; Kukshal, Vikas

doi:10.1177/09544054221111896

Cited by 21 publications

(10 citation statements)

References 203 publications

(313 reference statements)

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“…Micro cracks developed at higher pulse on time and peak current values due to an increase in discharge energy [20]. The increment in impulsive forces and discharge energy, which is a function of current and pulse on time, results in the removal of more material, thereby generating larger craters.…”

Section: Parameters Affecting the Surface Crack Densitymentioning

confidence: 99%

Enhancing Electrical Discharge Machining Performance by Mixing Nano Chromium Trioxide Powder with Soybean Dielectric to Machine Inconel 718 Alloy

Ghulam,

Ibrahim

2024

Adv. Sci. Technol. Res. J.

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Inconel 718 super alloy is suitable for components exposed to high temperatures and demanding high strength; it is one of the hardest alloys to machine by conventional processes due to its properties. Nano Powder mixed electrical discharge machining is one of the most sophisticated processes to produce precise three dimensional complicated forms of hard metals through a thermo-physical process, so it is suitable for machining Inconel 718. This study reports an experimental investigation to improve the machining performance of Inconel 718 super alloy by adding nano chromium oxide powder particles to biodegradable and renewable soybean oil, which is used as a dielectric fluid to preserve the environment, with a magnetic field to assist in improving the process performance. The effects of machining parameters, namely peak current, pulse on time, powder concentration, and magnetic field on the responses in terms of white layer thickness, heat affected zone, surface roughness, material removal rate, and surface crack density were investigated. The observed results manifested that the addition of nano chromium oxide particles to dielectric fluid enhances the process performance. The white layer thickness and heat affected zone improved by 43.93% and 48.82%, respectively. The enhancements in measured surface roughness and material removal rate were 51.76% and 20.62%, respectively. Micrographs of scanning electron microscope verifies that the number of cracks on the machined surface with 4 g/l of nano Cr 2 O 3 powder addition was reduced by half, and surface crack density improved by 10.31%, in comparison to machining without powder addition. It is observed that the current had the largest effect on the responses, followed by powder concentration, pulse on time, and magnetic field.

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Section: Parameters Affecting the Surface Crack Densitymentioning

confidence: 99%

Enhancing Electrical Discharge Machining Performance by Mixing Nano Chromium Trioxide Powder with Soybean Dielectric to Machine Inconel 718 Alloy

Ghulam,

Ibrahim

2024

Adv. Sci. Technol. Res. J.

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“…It can be observed that MRR and SR values were improved by using both nanopowders. This was because the addition of nanopowders enlarged the thermal conductivity of the dielectric, increased the discharge gap, decreased the breakdown strength, and enhanced the spark difference [60][61][62]. It also facilitated the proper flushing of eroded particles [63].…”

Section: Optimizationmentioning

confidence: 99%

Experimental Investigations of Using Aluminum Oxide (Al2O3) and Nano-Graphene Powder in the Electrical Discharge Machining of Titanium Alloy

Chaudhari,

Khanna,

Patel

et al. 2023

Micromachines

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In the present study, a comprehensive parametric analysis was carried out using the electrical discharge machining of Ti6Al4V, using pulse-on time, current, and pulse-off time as input factors with output measures of surface roughness and material removal rate. The present study also used two different nanopowders, namely alumina and nano-graphene, to analyze their effect on output measures and surface defects. All the experimental runs were performed using Taguchi’s array at three levels. Analysis of variance was employed to study the statistical significance. Empirical relations were generated through Minitab. The regression model term was observed to be significant for both the output responses, which suggested that the generated regressions were adequate. Among the input factors, pulse-off time and current were found to have a vital role in the change in material removal rate, while pulse-on time was observed as a vital input parameter. For surface quality, pulse-on time and pulse-off time were recognized to be influential parameters, while current was observed to be an insignificant factor. Teaching–learning-based optimization was used for the optimization of output responses. The influence of alumina and nano-graphene powder was investigated at optimal process parameters. The machining performance was significantly improved by using both powder-mixed electrical discharge machining as compared to the conventional method. Due to the higher conductivity of nano-graphene powder, it showed a larger improvement as compared to alumina powder. Lastly, scanning electron microscopy was operated to investigate the impact of alumina and graphene powder on surface morphology. The machined surface obtained for the conventional process depicted more surface defects than the powder-mixed process, which is key in aeronautical applications.

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“…It was pointed out from the experimental results that vegetable and bio-oil-based dielectrics have great potential to replace traditional dielectrics, thus providing a more sustainable manufacturing process in the future. Tapas 17 checked the Feasibility of jatropha oil as a dielectric fluid. Dissolved gas analysis proved that transesterified Jatropha oil provides sustainable and biodegradable dielectrics for EDM.…”

Section: Introductionmentioning

confidence: 99%

Surface roughness prediction of AISI D2 tool steel during powder mixed EDM using supervised machine learning

Kaigude,

Khedkar,

Jatti

et al. 2024

Sci Rep

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Surface integrity is one of the key elements used to judge the quality of machined surfaces, and surface roughness is one such quality parameter that determines the pass level of the machined product. In the present study, AISI D2 steel was machined with electric discharge at different process parameters using Jatropha and EDM oil. Titanium dioxide (TiO2) nanopowder was added to the dielectric to improve surface integrity. Experiments were performed using the one variable at a time (OVAT) approach for EDM oil and Jatropha oil as dielectric media. From the experimental results, it was observed that response trends of surface roughness (SR) using Jatropha oil are similar to those of commercially available EDM oil, which proves that Jatropha oil is a technically and operationally feasible dielectric and can be efficiently replaced as dielectric fluid in the EDM process. The lowest value of S.R. (i.e., 4.5 microns) for EDM and Jatropha oil was achieved at current = 9 A, Ton = 30 μs, Toff = 12 μs, and Gap voltage = 50 V. As the values of current and pulse on time increase, the S.R. also increases. Current and pulse-on-time were the most significant parameters affecting S.R. Machine learning methods like linear regression, decision trees, and random forests were used to predict the surface roughness. Random forest modeling is highly accurate, with an R2 value of 0.89 and an MSE of 1.36% among all methods. Random forest models have better predictive capabilities and may be one of the best options for modeling complex EDM processes.

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An insight on Powder Mixed Electric Discharge Machining: A state of the art review

Cited by 21 publications

References 203 publications

Enhancing Electrical Discharge Machining Performance by Mixing Nano Chromium Trioxide Powder with Soybean Dielectric to Machine Inconel 718 Alloy

Enhancing Electrical Discharge Machining Performance by Mixing Nano Chromium Trioxide Powder with Soybean Dielectric to Machine Inconel 718 Alloy

Experimental Investigations of Using Aluminum Oxide (Al2O3) and Nano-Graphene Powder in the Electrical Discharge Machining of Titanium Alloy

Surface roughness prediction of AISI D2 tool steel during powder mixed EDM using supervised machine learning

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