Optimization Of Electrical Discharge Machining Research Articles

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.

Exploring the intricacies of machine learning-based optimization of electric discharge machining on squeeze cast TiB2/AA6061 composites: Insights from morphological, and microstructural aspects in the surface structure analysis of recast layer formation and worn-out analysis

Aluminium (Al) Alloy-6061/TiB2 was developed with Squeeze casting while varying composite quantities with titanium diboride (TiB2). The metallographic structure of the composite was investigated using EDS and scanning electron microscopy (SEM). The machining of developed composite is challenging with the conventional methods due to higher tool wear and surface roughness (Ra). This study analyses the output responses, and machinability of TiB2 of 15% volume reinforced AA6061 composites using Electrical Discharge Machining (EDM). The EDM operation variables such as current, pulse on time, and voltage gap were utilized to examine material removal rate (MRR), tool wear rate (TWR) and Ra employing Box–Behnken design of experiments. The MRR, TWR and Ra data acquired from various experiments were optimized considering single and multiple objectives by a hybrid approach. Machine learning (ML) was applied to predict responses using linear regression (LR), decision tree (DT) and random forest (RF). The samples' recast layer was examined using SEM, and the thickness of the recast layer was also documented. The Al6061 alloy's SEM micrographs reveal deep equiaxed dimples on its fracture surface, indicating a high level of plastic deformation before failure. TiB2 is evenly distributed as darker particles in the Al-matrix alloy with no directional-orientation. The Al6061-15 wt% TiB2 composites' fracture surfaces display deep dimples, ductile-fracture for Al6061 alloy due to higher plastic deformation, and cleavage facets in some areas. The aluminum alloy's wear pattern depicts long, extensive ploughing grooves with short cracks parallel to the sliding direction at the grooves' bottom. SEM micrographs in recast-layer formation have reported that molten metal ligaments break into droplets during flushing, exposing fresh surfaces. The aluminum alloy's groove width is larger compared to the composites. Thus, it can be observed that the wear tracks on the base Al6061 alloy are larger and deeper in comparison to the Al- 15 wt% TiB2 surface composites due to the presence of hard TiB2 particles. Also, the recast layer may be advantageous due to its wear-resistant properties. TiB2 has importance in an industry where components slide with each other it is used in disc brakes in automobiles. Compared to industry standards, single-objective optimization considerably enhanced the outcomes: MRR increased by 10.61%, TWR enriched by 25.91%, and Ra increased by 17.60%. According to an ANOVA, peak current contributed 87.88%, 98.18%, and 95.80% respectively to MRR, TWR, and Ra. It is requisite to investigate multi-objective optimization for EDM of Al6061-15 wt% TiB2 composites' owing to the diverse optimal factor combinations for responses. Ra was slightly increased by 1.14% due to simultaneous optimization utilizing hybrid techniques with Equal and Entropy weights, which diminished TWR by 11.74% and increased MRR by 11.66%. ML, particularly DT, improves machining performance by assisting productivity, durability, and cost-efficiency under changeable circumstances and raising forthcoming study possibilities.

Parametric optimization of electric discharge machining of Ni 55.65Ti based shape memory alloy using NSGA II with TOPSIS

Nickel–Titanium based shape memory alloys (SMAs) are a unique category of smart materials that possess remarkable properties like super-elasticity, shape memory effect, high wear and corrosion resistance, thereby rendering them appropriate for uses such as composite structures, aerospace, and biomedical uses etc. These distinctive features make these alloys ‘difficult in machining’ using the traditional approach of machining process. In the present investigation, machinability aspects of Ni55.65Ti-SMAs using non-traditional electric discharge machining process has been studied. The effect of input process parameters i.e., pulse time ON, duty factor, peak current on the surface roughness and material removal rate has been investigated. The output responses viz. Minimum surface roughness along with maximum material removal rate having values of 6.828 μm and 4.552 mm3/sec respectively were achieved. Furthermore, empirical modelling and ANOVA study have been executed based on the full factorial design analysis. The non-dominated sorting genetic algorithm IIalgorithm IIalg2 commonly known as NSGA II, used crowding distance approach for multi-objective process optimization. The results obtained with NSGA-II were again ranked using Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) approach to give a more thorough understanding of the solution space and opt for the best possible solution.

Development of entropy embedded COPRAS-ARAS hybrid MCDM model for optimizing EDM parameters while machining high carbon chromium steel plate

The purpose of this paper is to discuss the optimization of electrical discharge machining (EDM) parameters in a green production environment. The primary goal of this work is to create a novel hybrid multi-criteria decision making (MCDM) model that combines COPRAS and ARAS to determine the best input EDM parameters for machining high carbon chromium tool steel plate. To carry out the selection process, a total of nine tests were carried out using four input parameters, including dielectric level, peak current, flushing pressure, and pulse duration at three different levels of magnitude, and the readings of the respective five output parameters were measured. The weights of these five criterions are evaluated using entropy, while COPRAS-ARAS hybrid model is implemented to choose the best alternative experiment among these nine availabilities. Experiment 5 is found to be the best choice, with the machining parameters 261 µs pulse length, 0.3 kg/cm2 flushing pressure, 4.5 Å current, and 80 mm dielectric level being the optimal input values. The primary result demonstrates that the developed hybrid model is sufficiently accurate and provides the best choice when compared to six existing MCDM tools. Finally, sensitivity analysis is used to justify the hybrid model’s stability and consistency.

Optimization of Electric Discharge Machining of Al/Al2O3 Metal Matrix Composites using MOPSO

In this article, Response Surface Methodology (RSM) and Multi-objective Particle Swarm Optimization (MOPSO) were used to optimize the output response of Material Removal Rate (MRR) and Surface Roughness(SR) of die-sinking Electrical discharge machining (EDM). An aluminum based metal matrix composites, reinforced with alumina, prepared by stir casting, was used for machining on EDM by Copper (Cu) and Titanium (Ti) tool. Box- Behnken Design (BBD) approach of RSM was used to design the experiment by considering four input factors at three levels. This developed model for multi-objective optimization by MOPSO and an RSM-based multi-objective optimization was also designed for input parameters. And it was found that the MOPSO technique was easy and valuable for parametric optimization of EDM. From MOPSO, optimized input parameters for machining of AMMC using Cu tool are current 4A, Voltage 60V, pulse on-time 100 µs, and duty factor 6. From MOPSO, optimized input parameters for machining of AMMC using Ti tool are current 4.241658A, Voltage 60V, pulse on-time 100 µs, and duty factor 4. The confirmatory test found that MRR and SR decreased by 63.86 % and 53.083% for the Cu tool, respectively, for MOPSO compared to RSM optimize value.

Multiobjective optimization of electric discharge machining of an Al–SiCp composite using the Taguchi–PCA method as well as the firefly and cuckoo search algorithms

Electric discharge machining (EDM) processes are extensively used in industries to machine materials and geometries that are complex and are not machinable by conventional methods. In our study, we focused on identifying the optimal process parameters for EDM during the machining of an aluminum alloy 6061 (matrix) –10% silicon carbide (particle) composite. The novel aspect of this work is the use of a copper electrode with different geometries (circular, triangular, square) for machining, together with input variables such as discharge current density (A) as well as pulse on- and off-timing (Ton and Toff). We used the L27 (313) Taguchi orthogonal array for our experimental layout and the responses we measured were recast layer thickness (RCT), electrode tool wear rate (TWR), and material removal rate (MRR). Taguchi’s approach of signal-to-noise (S:N) ratio was integrated with principal component analysis (PCA) for multicriterion optimization. Also, the nature inspired cuckoo search (CS) and firefly (FA) algorithms were used to identify the optimal conditions and to predict the outputs for maximum MRR and minimum TWR and RCT. From S:N + PC analyses, the optimal conditions we identified were: circle (12 A, 65 μs, 2 μs); triangle (12 A, 95 μs, 6 μs); and square (12 A, 65 μs, 8 μs). Under all of the conditions, the influence of discharge current was the most significant. Metallurgical examination conducted through micrographs of the machined surface clearly supported the predicted results. The optimized conditions we identified are appropriate for use in the automobile and aerospace industries to obtain holes of specific geometries with good surface integrity and reduced wear of tools.

Machinability Analysis and Optimization of Electrical Discharge Machining in AA6061-T6/15wt.% SiC Composite by the Multi-criteria Decision-Making Approach

Machinability Analysis and Optimization of Electrical Discharge Machining in AA6061-T6/15wt.% SiC Composite by the Multi-criteria Decision-Making Approach

Examination and Optimization of Machining Parameters in Electrical Discharge Machining of UNS T30407 Steel

Electrical discharge machining (EDM) is a non-conventional process utilized for machining electrically conductive materials which cannot be machined by conventional processes. This paper is focused on modeling and optimization in electrical discharge machining (EDM) of UNS T30407 steel. The variables such as pulse on time (Ton), pulse off time (Toff), and current will influence the material removal rate (MRR) and surface roughness (Ra) which were taken as response variables. Experimental data were collected as per the central composite design (CCD) matrix of response surface methodology (RSM) approach and mathematical models have been developed for both the response variables. The analysis of variance (ANOVA) was employed to determine the significant machining parameters and optimize the input parameters for both the responses (MRR and Ra). The statistical adequacy of the models is tested with the help of ANOVA. The optimum parametric combination for maximum MRR and smaller Ra (highest surface finish) was found. The surface characteristics of the machined surface were investigated by a scanning electron microscope (SEM).

Thermal Modelling and Multi Decision Making Optimization of EDM of Non Conductive SiC-CNT Ceramic Composite Used for Li-ion Battery and Sensor

In this research work the thermal modeling of a nonconductive Silicon carbide Ceramic matrix composite (CMC) machined by Die Sinking Electric Discharge Machining (EDM) has been done. Though SiC is a non-conductive material but the presence of CNT makes it a conductive material which can be machined with EDM. The modeling procedure carried out by considering some realistic approach like Gaussian heat Flux, Specific Discharge Energy, Variable Latent heat etc. For this analysis a 2D continuum has been designed as work domain. By simulating the work domain model by a Finite Element Analysis (FEA) Software (COMSOL), material removal rate (MRR) has been estimated with variable thermal properties. Parametric analysis of effect of Variable Specific heat on MRR by considering different current, Voltage and Pulse-On time has been performed. The effect of different input parameters (peak current and Pulse-on time) on Crater geometry has been done. A new concept of Specific discharge energy has been introduced during modelling to make it a more realistic model which can also be used as electrode support for electrochemical energy devices as Polymer Electrolyte Membrane Fuel Cells on Li-ion battery. Desirability analysis has been done to get an optimize set of input parameters for I= 3A, V=30V, Ton= 75 µs for machining ceramic matrix composite by EDM. The optimized MRR at this setting is 7.25 mm3/min whereas PFE is 87%. The experimental analysis has been also performed to strengthen the thermal and mathematical modelling.

Hybrid ANFIS-Rao algorithm for surface roughness modelling and optimization in electrical discharge machining

Advanced modeling and optimization techniques are imperative today to deal with complex machining processes like electric discharge machining (EDM). In the present research, Titanium alloy has been machined by considering different electrical input parameters to evaluate one of the important surface integrity (SI) parameter that is surface roughness Ra. Firstly, the response surface methodology (RSM) has been adopted for experimental design and for generating training data set. The artificial neural network (ANN) model has been developed and optimized for Ra with the same training data set. Finally, an adaptive neuro-fuzzy inference system (ANFIS) model has been developed for Ra. Optimization of the developed ANFIS model has been done by applying the latest optimization techniques Rao algorithm and the Jaya algorithm. Different statistical parameters such as the mean square error (MSE), the mean absolute error (MAE), the root mean square error (RMSE), the mean bias error (MBE) and the mean absolute percentage error (MAPE) elucidate that the ANFIS model is better than the ANN model. Both the optimization algorithms results in considerable improvement in the SI of the machined surface. Comparing the Rao algorithm and Jaya algorithm for optimization, it has been found that the Rao algorithm performs better than the Jaya algorithm.

Fuzzy based multi-response optimization: a case study on EDM machining process

The current study challenges the multi-objective optimization of electric discharge machining (EDM) parameters. EDM is used for creating profiles by machining of workpiece that are difficult to machine by conventional method. In the current work four responses such as material removal rate (production rate), tool wear rate, surface roughness (quality) and circularity (profile) are collectively investigated with varying controlling parameters. The human decision for best combination of controlling parameters for highest performance has uncertainties, which results in inferior solution. The multiple responses along with uncertainties and impreciseness can be addressed by combining a neuro-fuzzy system with particle swarm optimization (PSO). To illustrate the superiority of the proposed approach a set of experiment have been conducted in EDM process using AISI D2 tool steel as workpiece and brass tool. The experimental plan was made according to the Box-Behnken response surface methodology design with four process parameters namely discharge current, pulse-on-time, duty factor, and flushing pressure. The four response parameters such as material removal rate, tool wear rate, surface roughness, and circularity of machined components were optimized simultaneously. One unique Multi-response Performance Characteristic Index was obtained by combining the four responses using the proposed neuro-fuzzy technique. A regression model was developed on single response and optimized by PSO to obtain the optimal parameter setting. An experiment was conducted on optimal parameter to test the optimum performance. It is observed that the EDM responses were affected significantly by discharge current and pulse-on-time. The increase in pulse-on-time leads to larger surface cracks and more micro-pores on the machined surface.Article HighlightsRSM was proven to be an effective statistical tool for reducing the experimental runs, and also establishes the relation between multiple inputs and single output.The neuro-fuzzy system combined with PSO results a suitable model to convert multiple response into an equivalent single response.The presented approach can be a practical method for situations where multiple conflicting objectives are needed to be optimized at the same time.

Comparison of EDM and ECM machined AISI 304 steel: Surface roughness, hardness and morphological characteristics

In past two decades electro chemical machining (ECM) and electric discharge machining (EDM) processes have been widely explored for machining of different grades of steel (including AISI 304). Although there is difference in material removal mechanism of EDM (localized heating and vaporization being thermal process) and ECM (ionic dissolution being electrochemical energy based process), yet the user needs broad data base for comparison and selection of particular process. But hitherto little has been reported on relative comparison of EDM and ECM processed AISI steel surface from surface roughness (Ra), surface hardness (HV) and morphological characteristics view point. This paper reports the comparison of AISI 304 steel machined surface by EDM and ECM. The comparison has been made after process optimization of EDM and ECM separately (using Taguchi approach). The results of study suggest that ECM is better process than EDM from Ra view point (3.10 µm for ECM and 6.41 µm for EDM) and porosity% view point, whereas from HV viewpoint no significant difference was observed.

Multi-object optimization of EDM by Taguchi-DEAR method using AlCrNi coated electrode

In electrical discharge machining (EDM), the productivity and machined surface quality is directly influenced by the properties of the electrode surface material layer used. In the present study, a research attempt was made to find the optimal technological parameters in EDM process with AlCrNi coated electrode using Taguchi-Data Envelopment Analysis based Ranking (DEAR) based multi-response optimization approach. From the experimental investigation, the optimal technological parameter combination in EDM using AlCrNi coated electrode was found as peak current (40 A), voltage (55 V), and pulse-on-time (1000 μs) on machining Ti-6Al-4V titanium alloy. The current was found as more principal factor in EDM process with coated tool due to the electrical conductance of the tool coating. The better surface topography with lower surface roughness and micro cracks can be obtained on the machined specimens with proposed parameter combination.

Two-step optimization of electric discharge machining using neural network based approach and TOPSIS

The process performance of Electric Discharge Machining (EDM) process directly depends on the combination of input parameters. In this work, a two-step optimization has been performed to find the optimal combination of process parameters. In the first step, a neural network based multi-objective optimization technique is proposed and employed. The proposed approach generates the total possible combinations of input parameters taking the number and levels of each parameter into account. The performance measures for each combination is obtained by a trained neural network model of EDM process. Then, the optimal solution(s) are found following the concept of dominance. By employing the proposed approach, 24 non-dominated combinations of input parameters are obtained in this work. In the second step of optimization, Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) has been employed to award rank to each non-dominated solutions. Prior to that, experiments have been designed by Taguchi’s L36 orthogonal array and the experimental data has been used to train Radial Basis Function Neural Network (RBFNN) model.

Analysis, predictive modelling and multi-response optimization in electrical discharge machining of Al-22%SiC metal matrix composite for minimization of surface roughness and hole overcut

Due to the widespread engineering applications of metal matrix composites especially in automotive, aerospace, military, and electricity industries; the achievement of desired shape and contour of the machined end product with intricate geometry and dimensions that are very challenging task. This experimental investigation deals with electrical discharge machining of newly engineered metal matrix composite of aluminum reinforced with 22 wt.% of silicon carbide particles (Al-22%SiC MMC) using a brass electrode to analyze the machined part quality concerning surface roughness and overcut. Forty-six sets of experimental trials are conducted by considering five machining parameters (discharge current, gap voltage, pulse-on-time, pulse-off-time and flushing pressure) based on Box-Behnken's design of experiments (BBDOEs). This article demonstrates the methodology for predictive modeling and multi-response optimization of machining accuracy and surface quality to enhance the hole quality in Al-SiC based MMC, employing response surface methodology (RSM) and desirability function approach (DFA). Finally, a novel approach has been proposed for economic analysis which estimated the total machining cost per part of rupees 211.08 during EDM of Al-SiC MMC under optimum machining conditions. Thereafter, under the influence of discharge current several observations are performed on machined surface morphology and hole characteristics by scanning electron microscope to establish the process. The result shows that discharge current has the significant contribution (38.16% for Ra, 37.12% in case of OC) in degradation of surface finish as well as the dimensional deviation of hole diameter, especially overcut. The machining data generated for the Al-SiC MMC will be useful for the industry.

Multi-objective Optimization of Electrical Discharge Machining of Nimonic 75 using Teaching Learning Based Optimization (TLBO) Algorithm

Superalloys have been widely used in aircraft, space and power plant industry because of their high performance, resistance to corrosion and oxidation at elevated temperature. Processing of superalloys by conventional machining process becomes difficult due to high tool wear and poor quality. In present paper, machining of Nimonic 75 (Ni-based Superalloy) is carried out using Electrical Discharge Machining (EDM) process. Experiments were carried out using Taguchi’s L18 (2^1×3^5) orthogonal array. The multi-objective optimization of performance measures namely material removal rate (MRR) and surface roughness (SR) have been carried out using Teaching Learning Based Optimization (TLBO) algorithm. Initially empirical models of MRR and SR were generated using Regression Method. The optimal parametric combination for copper tool electrode as peak current 6 amp, gap voltage 50 volt, pulse on time 200 µs, pulse off time 90 µs and lift time 2 sec and for brass tool electrode as peak current 12 amp, gap voltage 40 volt, pulse on time 120 µs, pulse off time 15 µs and lift time 2 sec are recommended according to TLBO algorithm. Confirmatory tests were conducted to validate the results obtained by TLBO method.

Modeling and Optimization of EDM of Metal Matrix Composite using Black Hole Algorithm

Many advanced materials have been developed in the recent past to meet the present day technological demands. Aluminum-boron-carbide (Al-B4C) metal matrix composite (MMCs) is such a material slowly gaining popularity among researchers. The advanced machining processes (AMPs) are best manufacturing method to shape these types of innovative materials. The experimental investigations on Al-B4C MMC using one such AMP known as electrical discharge machining (EDM) have been carried out in the present work. Important electrical parameters of EDM have been considered as input control factors to evaluate two of the most important responses. Four evolutionary optimization techniques; black hole, differential evolution, shuffled frog leaping algorithm and coordinated aggregation based particle swarm optimization is applied to get best out of the process. Finally all the evolutionary optimization techniques have been compared for their performances.

Multi-objective optimization of process parameter in EDM using low-frequency vibration of workpiece assigned for SKD61

Integrated vibration in electrical discharge machining (EDM) plays a key role in achieving high efficiency. High levels of variables can be employed in this approach due to integration. However, simultaneous optimization of the EDM parameters to achieve multi-objectives is still very complex and challenging. Studies on integrated vibration are still in a preliminary stage. This report addresses multi-objective optimization in EDM for SKD61 die steel using low-frequency vibration. MOORA (Multi-objective optimization based on ratio analysis) was chosen to resolve this multi-objective optimization problem. The material removal rate (MRR), tool wear rate (TWR) and surface roughness (SR) were selected as performance measures in the EDM process. An analytical hierarchical process (AHP) was used to determine the weight value of the quality indicators. The results indicate that low-frequency vibrations significantly improve machining efficiency. When the frequency of the vibrations increased, MRR increased significantly such that MRRMAX = 64.48%. TWR and SR are smaller and their increase are given as TWRMAX = 20.3% and SRMAX = 18.47%. MOORA has been identified as a suitable alternative to multi-objective optimization in an EDM process using low-frequency vibrations for an assigned workpiece. The optimum parameters required to achieve the multi-objective were Ton = 25 μs, I = 8 A, Tof = 5.5 μs and F = 512 Hz, at the resultant quality criteria of MRR = 9.564 mm3/min, TWR = 1.944 mm3/min and SR = 3.24 µm with a maximum error of 8.24%.

Parametric optimization of EDM process for Inconel 825 using GRA and PCA approach

Parametric optimization of Electric Discharge Machining (EDM) assumes more challenge while machining newer materials for the best yield particularly used in the field of medical, marines and aerospace. In most cases, one methodology/approach does not provide satisfactory results as EDM is a very difficult process. Combination of two or more techniques has been quite successful to solve multi-objective optimization problems. This paper employs a combination of grey relational analysis (GRA) and principal component analysis (PCA) as a multi-objective optimization technique to optimize the process input parameters during EDM of Inconel 825. Experiments were designed by Taguchi’s L36 orthogonal array (OA) for seven input parameters and three performance measures. The optimal combination of EDM parameters is determined using GRA. Then, PCA is adopted to find out the weighting values corresponding to various performance measures. Consideration of two different dielectric fluids, work piece material i.e. Inconel 825 and three different electrode materials is the novelty of this work. Finally, the results are validated by running confirmatory tests. A significant enhancement in performance characteristics has been obtained and reported.

Multi-characteristics optimization in EDM of NiTi alloy, NiCu alloy and BeCu alloy using Taguchi’s approach and utility concept

The foremost aim of Electrical Discharge Machining (EDM) users and manufactures is to obtain a better process stability, maximum productivity, precise and accurate machining of the component with minimum tool wear. For achieving better efficiency in EDM, selection and setting of input parameter is a crucial step. This study investigated the effect of input parameters such as workpiece electrical conductivity, gap current, gap voltage, pulse-on-time and pulse-off-time on the responses namely material removal rate (MRR) and tool wear rate (TWR). Experiments were designed and performed as per the Taguchi’s L18 (61 × 34) array. Taguchi’s approach and utility concept were employed for optimizing conflicting responses. Obtained results showed that the overall utility was significantly affected by gap current, gap voltage, pulse-on-time and pulse-off-time. Thus the obtained optimal values of MRR and TWR are 9.157 mm3/min and 0.128 mm3/min respectively.