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This study aims to experimentally investigate the influence of considered process parameters, i.e. pulse on time, pulse off time, peak current and gap voltage, on tool wear rate (TWR) in electrical discharge machining (EDM) of iron (Fe)-based shape memory alloy (SMA) through designed experiments. The parametric optimization for TWR has also been attempted using the desirability approach and genetic algorithm (GA).

The response surface methodology (RSM) in the form of Box–Behnken design has been used to scheme out the experiments. The influence of considered process inputs has also been observed through variance analysis. The reliability and fitness of the developed mathematical model have been established with test results. Microstructure analysis of machined samples has also been evaluated and analyzed using a scanning electron microscope (SEM). SEM images revealed the surface characteristics such as micro-cracks, craters and voids on the tool electrode surface. SEM images provide information about the surface integrity and type of wear on the surface of the tool electrode.

The input parameters, namely, pulse on time and pulse off time, are major influential factors impacting the TWR. High TWR has been reported at large pulse on time and small pulse off time conditions whereas higher TWR is reported at high peak current input settings. The maximum and minimum TWR values obtained are 0.073 g/min and 0.017 g/min, respectively. The optimization with desirability approach and GA reveals the best parametric values for TWR i.e. 0.01581 g/min and 0.00875 g/min at parametric combination as pulse on time = 60.83 µs, pulse off time = 112.16 µs, peak current = 18.64 A and gap voltage = 59.55 V, and pulse on time = 60 µs, pulse off time = 120 µs, peak current = 12 A and gap voltage = 40 V, correspondingly.

Proposed work has no limitations.

SMAs have been well known for their superior and excellent properties, which make them an eligible candidate of paramount importance in real-life industrial applications such as orthopedic implants, actuators, micro tools, stents, coupling, sealing elements, aerospace components, defense instruments, manufacturing elements and bio-medical appliances. However, its effective and productive processing is still a challenge. Tool wear study while processing of SMAs in EDM process is an area which has been less investigated and of major concern for exploring the various properties of the tool and wear in it. Also, the developed mathematical model for TWR through the RSM approach will be helpful in industrial revelation.

In many of today’s manufacturing industries, such as automobile, aerospace, defence, die and mould making, medical and electrical discharge machining (EDM) has emerged as an effective material removal process. In this process, a series of discontinuous electric discharges is used for removing material from the workpiece in the form of craters generating a replica of the tool into the workpiece in a dielectric environment. Appropriate selection of the tool electrode material and combination of input parameters is an important requirement for performance enhancement of an EDM process. This paper aims to optimize an EDM process using single-valued neutrosophic grey relational analysis using Cu-multi-walled carbon nanotube (Cu-MWCNT) composite tool electrode.

This paper proposes the application of grey relational analysis (GRA) in a single-valued neutrosophic fuzzy environment to identify the optimal parametric intermix of an EDM process while considering Cu-MWCNT composite as the tool electrode material. Based on Taguchi’s L9 orthogonal array, nine experiments are conducted at varying combinations of four EDM parameters, i.e. pulse-on time, duty factor, discharge current and gap voltage, with subsequent measurement of two responses, i.e. material removal rate (MRR) and tool wear rate (TWR). The electrodeposition process is used to fabricate the Cu-MWCNT composite tool.

It is noticed that both the responses would be simultaneously optimized at higher levels of pulse-on time (38 µs) and duty factor (8), moderate level of discharge current (5 A) and lower level of gap voltage (30 V). During bi-objective optimization (maximization of MRR and minimization of TWR) of the said EDM process, the achieved values of MRR and TWR are 243.74 mm³/min and 0.001034 g/min, respectively.

Keeping in mind the type of response under consideration, their measured values for each of the EDM experiments are expressed in terms of linguistic variables which are subsequently converted into single-valued neutrosophic numbers. Integration of GRA with single-valued neutrosophic sets would help in optimizing the said EDM process with the Cu-MWCNT composite tool while simultaneously considering truth-membership, indeterminacy membership and falsity-membership degrees in a human-centric uncertain decision-making environment.

This paper aims to present the performance of deionized water in electrical discharge machining (EDM) during machining of Inconel 718, copper tool electrode and deionized water as dielectric. Three parameters, namely, pulse-on-time, pulse-off-time and discharge current were taken as control parameters with individual parameter having three levels. Influence of these control parameters on response such as tool wear rate (TWR), material removal rate (MRR) and surface roughness (Ra) is evaluated at various combinations of parametric levels. The results reveal deionized water can be effectively used as a sustainable dielectric and may substitute the hydrocarbon-based dielectric in electrical discharge machining. Also, the control parameters considered show significant impact on the process criteria. Super ranking method was adopted to achieve optimal integration of EDM control factors for obtaining higher MRR, lower TWR and Ra. Further, by applying analysis of variance test, discharge current is established as the dominant parameter during the machining process.

The experimentation was performed on Inconel 718 in SPARKONIX MOS, 35 A, ZNC EDM using deionized water as dielectric and copper tool as electrode. The dielectric circulatory system was developed without disturbing the existing dielectric circulation system. Figure 1 shows the EDM with newly developed dielectric system. The existing system consists of hydrocarbon-based dielectric, which has a number of drawbacks during the machining such as carbide deposition on the work material, which reduces removal of material from work material; carbon particle adhesion on tool, which results in inefficient discharge between the electrode; and the work material and production of CO and CH4 during machining, which makes the machining environment toxic. To overcome these drawbacks, a sustainable dielectric was adopted in present work. Trial experiments were conducted to select the ranges of parameters, namely, discharge current, pulse-on-time and pulse-off-time. The process characteristics were evaluated at different parametric combinations and the experimentation was designed as per Taguchi L9 orthogonal array. Table 1 shows the properties of Inconel 718. Table 2 shows the parameters considered with its ranges. Table 3 shows the experimental values. The difference of weight of work piece before and after was taken and divided by the machining time used for calculating the MWR. Similarly, the difference of weight of tool material before and after was taken and divided by machining time and is used for calculating TWR. Measurement of surface roughness was done using Talysurf surface roughness meter.

The experimentation was conducted at different parametric combination on Inconel 718 taking copper as electrode and deionized water as dielectric. The performance criteria was evaluated at considered parametric combination. The result shows that the EDM parameters have significant contribution on the performance criteria and deionized water can be effectively used as dielectric medium in EDM. The use of deionized water as dielectric will improve the process and sustainable green machining can be performed. Super ranking method has been implemented to achieve the best combination of control factors and it is obtained that the combination A1B1C3 (i.e. discharge current = 3 A, pulse-on-time = 1 µs and pulse-off-time = 3 µs) is best combination for obtaining the higher MRR and lower TWR and Ra. The contributing factor in the proposed research work is discharge current. Further, ANOVA was implemented to check the adequacy of these result. It was established that discharge current is the most influential factor followed by pulse-on-time and the least contributing factor as pulse-off-time. The findings of this paper may open the guidelines for researcher for performing research in the field of sustainable machining of difficult to cut materials such as Inconel 718 with sustainable dielectrics in engineering applications.

The paper is original in nature. The findings of this paper may open the guidelines for researcher for performing research in the field of sustainable machining.

AA2014 is a copper-based alloy and is typically used for production of complex machined components, given its better machinability. The purpose of this paper was to study the effects of variation in weight percentage of ceramic Al₂O₃ particulates during electrical discharge machining (EDM) of stir cast AA2014 composites. Scanning electron microscopy (SEM) examination was carried out to study characteristics of EDMed surface of Al₂O₃/AA2014 composites.

The effect of machining parameters on performance measures during sinker EDM of stir cast Al₂O₃/AA2014 composites was examined by “one factor at a time” (OFAT) method. The stir cast samples were obtained by using three levels of weight percentage of Al₂O₃ particulates, i.e. 0 Wt.%, 10 Wt.% and 20 Wt.% with density 1.87 g/cc, 2.35 g/cc and 2.98 g/cc respectively. Machining parameters varied were peak current (1-30 amp), discharge voltage (30-100 V), pulse on time (15-300 µs) and pulse off time (15-450 µs) to study their influence on material removal rate (MRR), tool wear rate (TWR) and surface roughness (SR).

MRR and SR decreased with an increase in weight percentage of ceramic Al₂O₃ particulates at the expense of TWR. This was attributed to increased microhardness for reinforced stir cast composites. However, microhardness of EDMed samples at fixed values of machining parameters, i.e. 9 amp current, 60 V voltage, 90 µs pulse off time and 90 µs pulse on time reduced by 58.34, 52.25 and 46.85 per cent for stir cast AA2014, 10 Wt.% Al₂O₃/AA2014 and 20 Wt.% Al₂O₃/AA2014, respectively. SEM and quantitative energy dispersive spectroscopy (EDS) analysis revealed ceramic Al₂O₃ particulate thermal spalling in 20 Wt.% Al₂O₃/AA2014 composite. This was because of increased particulate weight percentage leading to steep temperature gradients in between layers of base material and heat affected zone.

This work was an essential step to assess the machinability for material design of Al₂O₃ reinforced aluminium metal matrix composites (AMMCs). Experimental investigation on sinker EDM of high weight fraction of particulates in AA2014, i.e. 10 Wt.% Al₂O₃ and 20 Wt.% Al₂O₃, has not been reported in archival literature. The AMMCs were EDMed at variable peak currents, voltages, pulse on and pulse off times. The effects of process parameters on MRR, TWR and SR were analysed with comparisons made to show the effect of Al₂O₃ particulate contents.

The purpose of this paper is to predict the machining performance of electrical discharge machining of Ti-13Nb-13Zr (TNZ) alloy, a promising biomedical alloy, using artificial neural networks (ANN) models.

In the research, three major performance characteristics, i.e. the material removal rate (MRR), tool wear rate (TWR) and surface roughness (SR), were chosen for the study. The input parameters for machining were the voltage, current, pulse-on time and pulse-off time. For the ANN model, a two-layer feedforward network with sigmoid hidden neurons and linear output neurons were chosen. Levenberg–Marquardt backpropagation algorithm was used to train the neural networks.

The optimal ANN structure comprises four neurons in input layer, ten neurons in hidden layer and one neuron in the output layer (4–10-1). In predicting MRR, the 60–20-20 data split provides the lowest MSE (0.0021179) and highest R-value for training (0.99976). On the contrary, the 70–15-15 data split results in the best performance in predicting both TWR and SR. The model achieves the lowest MSE and highest R-value for training in predicting TWR as 1.17E-06 and 0.84488, respectively. Increasing the number of hidden neurons of the network further deteriorates the performance. In predicting SR, the authors find the best MSE and R-value as 0.86748 and 0.94024, respectively.

This is a novel approach in performance prediction of electrical discharge machining in terms of new workpiece material (TNZ alloys).

Machining of curved channels using electrical discharge machining (EDM) is a novel approach. In this study, an experimental setup was designed, developed and mounted on die-sinking EDM to manufacture curve channels in AISI P20 mold steel.

The effect of specific machining parameters such as peak current, pulse on time, duty factor and lift over material removal rate (MRR) and tool wear rate (TWR) were studied. Multi-objective optimization was performed using Taguchi technique and Jaya algorithm.

The experimental results revealed current and pulse on time to have the predominant effect over material removal and tool wear diagnostic parameters with contributions of 39.67, 32.04% and 43.05, 36.52%, respectively. The improvements in material removal and tool wear as per the various optimization techniques were 35.48 and 10.91%, respectively.

Thus, Taguchi method was used for effective optimization of the machining parameters. Further, nature-based Jaya algorithm was implemented for obtaining the optimum values of TWR and MRR.

The electric discharge machining (EDM) involves electrons discharged from the electrode and machining progresses due to the removal of the material from the component. This a thermal-based machining process primarily used for hard to machine components with conventional methods. This process is used to make intricate cavities and contours. The fabricated part is the replica of the tool material with high surface finish and good dimensional accuracy. This study aims to evaluate the comprehensive effect of process parameters on electric discharge machining of maraging steel.

Multiple criteria Decision making (MCDM) techniques are used to select the best parameters by comparing several responses to achieve the desired goal. There are different MCDM techniques available for optimization of machining parameters. In the current investigation, multi-objective optimization by data envelopment analysis based ranking (DEAR) approach was used for machining Maraging C300 grade steel.

The Taguchi L9 runs were planned with process parameters such as current (Amp), Tool diameter (mm) and Dielectric pressure (MPa). The effect of process parameters on the responses, namely, material removal rate (MRR), tool wear rate (TWR) and surface roughness (SR) were evaluated. High MRR is found at 15 A current, 14 mm tool diameter and dielectric pressure of 0.2 MPa. Optimum process parameters experiment showed reduced crack density.

An effort was made successfully to enhance the responses using the DEAR method and establish the decision making of selecting the optimal parameters by comparing the results obtained by machining maraging steel C300 grade.

This study aims to focus on experimenting the performance of aluminum (Al) powder mixed electric discharge machining (PMEDM) of two different materials viz plastic mould die steel (AISI P20) and nickel-based super alloy (Nimonic 75). This experimental study also focuses on using three different tool materials such as copper, brass and tungsten to analyze their influence on the process output. These materials find many uses in industrial as well as aerospace applications. The performance measures considered in this work are material removal rate (MRR), tool wear rate (TWR) and surface roughness (SR).

The experimental design used in this work is based on Taguchi’s L18 orthogonal array. Besides considering work and tool material as one of the process variables, other process variables are peak current (I_p), pulse on time (T_on) and concentration of powder (C_p). The analysis of variance (ANOVA) is performed on the experimental data to determine the significant variables that influence the output.

It is found that copper produced maximum MRR and brass tool exhibited higher TWR. However, the surface finish of the machined work piece was very much improved by using the brass tool. Though the performance of tungsten tool lies between the above two tool materials, it showed very little wear during EDM with or without the addition of Al powder.

The experimental investigation of PMEDM of nickel-based super alloy (Nimonic 75) has not been attempted before. Besides that, the study on the influence of tungsten tool on the performance of EDM is also very limited.

The purpose of this paper is to optimise the electro‐discharge machining (EDM) parameters and investigate feasibility of using direct metal laser sintering (DMLS) parts as EDM electrodes.

In this paper the effects of discharge current, pulse‐on‐time, flushing pressure are optimized for minimum tool wear rate (TWR), maximum metal removal rate (MRR) and minimum surface roughness (Ra). Taguchi‐based L₉ orthogonal array has been used for performing experiments on EDM machining of EN 24 steel using DMLS electrodes. The grey relational analysis combined with ANOVA techniques have been employed to determine the optimal level as well as their significance.

Experimental results have shown that the performance characteristics of the EDM process (TWR, MRR and surface roughness) using DMLS electrode can be quantified and controlled effectively by grey relational approach presented in the study. Current is found to be the most affective parameter in EDM machining using DMLS electrode. Excessive DMLS tool (electrode) wear was also reported, which limits the use of DMLS tool for EDM machining and it has been found out that porosity (which was about 20 per cent) was one of the primary cause.

This paper was focused on understanding the effects of important EDM parameters on three performance characteristics (TWR, MRR and surface roughness). While this study identifies that DMLS electrode wear rate is high and porosity could be one of the main cause, presently it does not cover the investigations on reducing the porosity level and its implications.

The DMLS material had shown huge potential to be used as EDM electrode. The current investigation established a structured experimental approach to understand the effects of EDM parameters on multi response characteristics. The results derived from this study helps to focus future research on two aspects including enriching the copper content and reducing the porosity level, thereby the benefits of lead time reduction in EDM electrode making could be realized.

The previous research attempts were not focussed on optimising the EDM machining process using rapid tooling electrodes. With the best of author's knowledge none of the researchers have reported these aspects especially for DMLS electrodes. Application of grey relational analysis for performance evaluation of rapid tooling‐based EDM electrodes (DMLS electrodes) appear to be completely new.

The purpose of this paper is to investigate the performance of powder-mixed electric discharge machining (PMEDM) using three different powders which are aluminium (Al), silicon carbide (SiC) and aluminium oxide (Al₂O₃). Besides that, the influence of different tool materials was also studied in this experimental investigation. Hence, the work material selected for this purpose was AISI P20 steel and tool materials were copper, brass and tungsten. The performance measures considered in this work were material removal rate (MRR), tool wear rate and radial over cut (ROC).

The process variables considered in this study were powder types, powder concentration, tool materials, peak current and pulse on time. The experimental design, based on Taguchi’s L₂₇ orthogonal array, was adopted to conduct experiments. Significant parameters were identified by performing the analysis of variance on the experimental data.

Based on the analysis of results, it was observed that copper tool combined with Al powder produced maximum MRR (58.35 mm³/min). Similarly, the Al₂O₃ powder combined with tungsten tool has resulted least ROC (0.04865 mm). It was also observed that wear rate of tungsten tool was very low (0.0145 mm³/min).

The experimental investigation of PMEDM involving three different powders (Al, SiC and Al₂O₃) was not attempted before. Moreover, the study of influence of different tool materials (Cu, brass and W) together with the different powders on the electric discharge machining performance was very limited.