Wire Electrical Discharge Machining Process Parameters Research Articles

Investigation of machining rate and surface roughness in wire EDM of Al6063/WC/ZrO2 composite using response surface methodology

The aim of the current work is to explore the machinability behavior of Al6063/WC/ZrO2 metal matrix composites (MMC) produced through a stir casting process through wire electrical discharge machining (WEDM) process. In order to examine the effects of process parameters such as voltage (V), pulse on time (Ton) and pulse off time (Toff) on material removal rate (MRR) and surface roughness (SR), the experiments were conducted by adapting Response Surface Methodology (RSM) in conjunction with the central composite design (CCD). A second-order regression model has been developed to predict the response parameters and an analysis of variance (ANOVA) was performed to validate the significance of the models. Using the desirability function approach, the parameters were set for the highest MRR and the minimum SR value. The prediction was within a tolerable average error range. A total of 19 sets of tests were developed to obtain six possible solutions. The most suitable solution among these six has been found by a confirmatory experiment. The results indicate that voltage and its interactions have significantly affected both the responses than Ton and Toff. Overall, it can be said that the study of the Al6063/WC/ZrO2 MMCs using WEDM process parameters demonstrated improved casting and machining qualities.

Investigation of Al-7075/Al2O3/SiC by swirl casting and WEDM

The research, engineering, military, automotive, sports, and telecommunications industries are just a few of the many that make heavy use of aluminium metal matrix composites. The unique synergistic qualities of these composites, such as reduced weight density, improved performance, and thermal conductivity, make them highly desired. Because of its reputation for small but noticeable structural improvements, Al-7075 aluminium has seen extensive use in the aerospace industry. This research followed international criteria for evaluating specimens in terms of positional stiffness, deformation, impact resistance, and wear. It used the stir casting process to add SiC/Al2O3 to Al-7075 in percentages ranging from 2% to 8%. Hybrid Al-HMCs are the product of an aluminium metal matrix composite that was able to include 8% SiC/Al2O3via the stirring process. Because it combined aluminium oxide with silicon carbide, this Al-HMC showed improved mechanical qualities and stiffness. Aluminium metal composites with increasing amounts of Al2O3/ SiC showed reduced fatigue and frictional strengths. The effect on hardness remained small even when the MMC's SiC/Al2O3 proportions grew significantly. To further investigate the Wire Electrical Discharge Machining (WEDM) process, researchers used the L9 Arrays Design of Experiments (DOE) technique to incorporate new process variables. Our objective was to enhance the rate of material removal from Al-7075/SiC/Al2O3 while simultaneously improving the marginal surface roughness. Finding the best WEDM process parameters, including the ideal percentages of SiC/Al2O3, to achieve the desired surface roughness while minimising material removal rate was the goal of this investigation. To produce Al- 7075/SiC/Al2O3 composite materials with varying additive percentages in a production environment, this study will determine the best and most accurate WEDM process variables to use.

Wire Electrical Discharge Machining of AISI304 and AISI316 Alloys: A Comparative Assessment of Machining Responses, Empirical Modeling and Multi-Objective Optimization

This research investigates the multi-response of both material removal rate (MRR) and surface roughness (Ra) for the wire electrical discharge machining (WEDM) of two stainless steel alloys: AISI 304 and AISI 316. Experimental results are utilized to compare the machining responses obtained for AISI 316 with those obtained for AISI 304, as previously reported in the literature. The experimental work is conducted through a full factorial experimental design of five running parameters with different levels: applied voltage, transverse feed, pulse-on/pulse-off times and current intensity. The machined workpieces are analyzed using an image processing technique in order to evaluate the size of cut slots to allow the calculation of the MRR. Followed by the characterization of the surface roughness along the side walls of the slots. Different mathematical regression techniques were developed to represent the multi-response of both materials using the MATLAB regression toolbox. It was found that WEDM process parameters have a fuzzy influence on the responses of both material models. This allowed for multi-objective optimization of the regression models using four different techniques: multi-objective genetic algorithm (MOGA), multi-objective pareto search algorithm (MOPSA), weighted value grey wolf optimizer (WVGWO) and osprey optimization algorithm (OOA). The optimization results reveal that the optimal WEDM parameters of each response are inconsistent to the others. Hence, the optimal results are considered a compromise between the best results of different responses. Noteworthily, the multi-objective pareto search algorithm outperformed the other candidates. Eventually, the optimal results of both materials share the high voltage, high transverse feed rate and low pulse-off time parameters; however, AISI 304 requires low pulse-on time and current intensity levels while AISI 316 optimal results entail higher pulse-on time and current levels.

Integrating experimental modeling techniques with the Pareto search algorithm for multiobjective optimization in the WEDM of Inconel 718

Inconel 718 is a superalloy with a high nickel content that is widely used in applications requiring solid mechanical behavior and resistance to oxidation and corrosion at high temperatures. This alloy has numerous applications in manufacturing steam turbine and jet aircraft interiors, aviation sector manifolds, and rotary spindles. It can be classified as a difficult-to-cut material unsuitable for traditional machining. The purpose of this paper is to develop prediction models for a wire electrical discharge machining (WEDM) process using response surface methodology (RSM), artificial neural networks (ANN), and adaptive neuro-fuzzy inference systems (ANFIS) and to determine which model is better at making accurate predictions. Pulse_ontime, pulse_offtime, servo voltage, flushing pressure, and wire feed were considered the main factors affecting volumetric material removal rate (VMRR) and arithmetic surface roughness (Ra), which were evaluated as WEDM performance characteristics. I-optimal design made with a computer algorithm was employed to develop experimental models. The results reveal that the wire feed and pulse_ontime were the most vital factors influencing VMRR, respectively, and the most significant factor influencing Ra is the pulse_ontime. The total percentage error of the three models demonstrated that the ANN and ANFIS models are more reliable and accurate than the RSM mathematical model. Finally, multiobjective optimization using the Pareto search algorithm was used to optimize mathematical, ANN, and ANFIS models to determine the optimum WEDM process parameters for machining Inconel 718 superalloy.

Optimization and Microstructural Studies on the Machining of Inconel 600 in WEDM Using Untreated and Cryogenically Treated Zinc Electrodes.

Any industry that manufactures dies, punches, molds, and machine components from difficult-to-cut materials, such as Inconel, titanium, and other super alloys, largely relies on wire electrical discharge machining (WEDM). In the current study, the effect of the WEDM process parameters on Inconel 600 alloy with untreated zinc and cryogenically treated zinc electrodes was investigated. The controllable parameters included the current (IP), pulse-on time (Ton), and pulse-off time (Toff), whereas the wire diameter, workpiece diameter, dielectric fluid flow rate, wire feed rate, and cable tension were held constant throughout the experiments. The significance of these parameters on the material removal rate (MRR) and surface roughness (Ra) was established using the analysis of the variance. The experimental data acquired using the Taguchi analysis were used to analyze the level of influence of each process parameter on a particular performance characteristic. Their interactions with the pulse-off time were identified as the most influential process parameter on the MRR and Ra in both cases. Furthermore, a microstructural analysis was also performed via scanning electron microscopy (SEM) to examine the recast layer thickness, micropores, cracks, depth of metal, pitching of metal, and electrode droplets over the workpiece surface. In addition, energy-dispersive X-ray spectroscopy (EDS) was also carried out for the quantitative and semi-quantitative analyses of the work surface and electrodes after machining.

Parametric modelling of a wire electrical discharge machining process using path analysis approach

ABSTRACT This paper studies the application of a path analysis approach to model a wire electrical discharge machining (WEDM) process for investigating the relationships between its different process parameters and responses. It also estimates the contributive importance of the considered WEDM process parameters on each of the responses and identifies those parameters having the maximal influence on the responses. Four WEDM process parameters, i.e. pulse-on time, pulse-off time, spark gap voltage and wire feed are considered here as the independent variables, while three responses, i.e. cutting speed, gap current and surface roughness are treated as the dependent variables. This paper also proposes a new approach for modelling of the WEDM process, where one of the responses (gap current) is postulated to have an influence on the other two responses. Four different path analysis-based models are constructed with different sets of hypothesized relations. Based on the path analysis results, pulse-off time is identified as the most influential parameter for cutting speed, while pulse-on time is the most important parameter for both gap current and surface roughness. The calculated goodness-of-fit indices indicate the fitness with experimental data for all the four developed models while comparing their relative performance.

Revealing the WEDM Process Parameters for the Machining of Pure and Heat-Treated Titanium (Ti-6Al-4V) Alloy.

Ti-6Al-4V is an alloy that has a high strength-to-weight ratio. It is known as an alpha-beta titanium alloy with excellent corrosion resistance. This alloy has a wide range of applications, e.g., in the aerospace and biomedical industries. Examples of alpha stabilizers are aluminum, oxygen, nitrogen, and carbon, which are added to titanium. Examples of beta stabilizers are titanium–iron, titanium–chromium, and titanium–manganese. Despite the exceptional properties, the processing of this titanium alloy is challenging when using conventional methods as it is quite a hard and tough material. Nonconventional methods are required to create intricate and complex geometries, which are difficult with the traditional methods. The present study focused on machining Ti-6Al-4V using wire electrical discharge machining (WEDM) and conducting numerous experiments to establish the machining parameters. The optimal setting of the machining parameters was predicted using a multiresponse optimization technique. Experiments were planned using the response surface methodology (RSM) technique and analysis of variance (ANOVA) was used to determine the significance and contribution of the input parameters to changes in the output characteristics (cutting speed and surface roughness). The cutting speed obtained during the processing of the annealed titanium alloy using WEDM was quite large as compared to the cutting speed obtained in the case of processing the pure, quenched, and hardened titanium alloys using WEDM. The maximum cutting speed obtained while processing the annealed titanium alloy was 1.75 mm/min.

Parametric optimization of wire-EDM machining of nimonic 80a using response surface methodology

Wire electrical discharge machining (WEDM) can machine hard materials with ease. Nimonic 80A is hard and high temperature resistant material which is widely used in the power plant boiler tubes, gas turbines, exhaust valves. This work involves in developing a models for correlating the dependency of various WEDM process parameters of Nimonic 80A like gap voltage, duty factor and feed rate on response factors like rate of material removal (MRR), wire wear ratio and surface roughness (SR). This experiment was carried out on the basis of Response Surface Methodology (RSM) and Grey Relational Analysis (GRA). Using the design of experiments, 20 experiments were conducted. The duty factor of 0.8771, gap voltage 17V and wire feed rate of 17m/min was found to be the optimized process parameter while machining Nimonic 80A.

Parametric Study of Wire-EDM Process in Al-Mg-MoS2 Composite Using NSGA-II and MOPSO Algorithms

Al-Mg-based composite is used in producing a variety of components. To improve the machinability of the composite, MoS2 is added. For characterizing the machining of the Al-Mg-based composite, different wt.% (2, 4, and 6) of MoS2 are added as reinforcement. Wire Electrical Discharge Machining (WEDM) process is performed to analyze the kerf width and surface roughness. Due to the complex nature of the WEDM process, the necessity for its optimization through the use of innovative methods is well-proven in the process of research. Evolutionary algorithms, specifically genetic algorithm based on NSGA-II and Multiple Objective Particle Swarm Optimization (MOPSO), are used for optimizing kerf width and surface roughness. For assessing the impact of current, pulse on time, and gap voltage on kerf width and surface roughness, an analysis of the selected WEDM process parameters is performed. MOPSO takes lesser iterations as compared to NSGA-II in giving nearly the same optimal fronts for achieving low kerf width and surface roughness. The 10–12 A of current, 50–57 µs of pulse on time, and 30–33 V of gap voltage are used for the WEDM process based on the Pareto-optimal solutions and better performance is achieved on the samples. In addition, the supplementary DOE method is applied to determine the relationship between factors affecting a process and the response. The analysis revealed that current has played a major part in the governance of kerf width and surface roughness over pulse on time and gap voltage for Al-Mg-MoS2 composite.

Application of Type-2 Fuzzy AHP-ARAS for Selecting Optimal WEDM Parameters

Machining of the nickel-based alloy is very demanding due to its extreme mechanical properties, for example, higher fatigue strength, better corrosion and creep resistance feature, substantial work hardening capability, and appreciable tensile and shear strength. Owing to these properties, the selection of machining parameters is a major challenge for modern machining industries. Therefore, the present experimental work is carried out to select the best parametric combination of the wire electrical discharge machining (WEDM) machine for reducing machining cost and human effort. The Trapezoidal Interval Type-2 fuzzy number (T2FS) integrated Analytical Hierarchy Process (AHP)-based Additive Ratio Assessment (ARAS) method is used for selecting the best WEDM process parameters of Inconel-800 superalloy. Finally, the results were compared with some existing multi-criteria decision-making methods to confirm the validity of the adopted method. The comparison shows that Type-2 Fuzzy AHP-ARAS synergy can help to formulate the problem and facilitate the assessment and ranking of WEDM process parameters when multiple criteria are jointly considered.

Effect of WEDM Process Parameters on Surface Morphology of Nitinol Shape Memory Alloy.

Nickel–titanium shape memory alloys (SMAs) have started becoming popular owing to their unique ability to memorize or regain their original shape from the plastically deformed condition by means of heating or magnetic or mechanical loading. Nickel–titanium alloys, commonly known as nitinol, have been widely used in actuators, microelectromechanical system (MEMS) devices, and many other applications, including in the biomedical, aerospace, and automotive fields. However, nitinol is a difficult-to-cut material because of its versatile specific properties such as the shape memory effect, superelasticity, high specific strength, high wear and corrosion resistance, and severe strain hardening. There are several challenges faced when machining nitinol SMA with conventional machining techniques. Noncontact operation of the wire electrical discharge machining (WEDM) process between the tool (wire) and workpiece significantly eliminates the problems of conventional machining processes. The WEDM process consists of multiple input parameters that should be controlled to obtain great surface quality. In this study, the effect of WEDM process parameters on the surface morphology of nitinol SMA was studied using 3D surface analysis, scanning electron microscopy (SEM), and energy-dispersive X-ray (EDX) analysis. 3D surface analysis results indicated a higher value of surface roughness (SR) on the top of the work surface and a lower SR on the bottom portion of the work surface. The surface morphology of the machined sample obtained at optimized parameters showed a reduction in microcracks, micropores, and globules in comparison with the machined surface obtained at a high discharge energy level. EDX analysis indicated a machined surface free of molybdenum (tool electrode).

Multi-parametric Optimization of WEDM Using Artificial Neural Network (ANN)-Based PCA for Al/SiCp MMC

Wire electrical discharge machining (WEDM) process optimization is essential when any novel material is discovered. The WEDM process has numerous variables which affect multiple responses. Therefore, multi-response optimization needs to be performed with the help of advanced optimization techniques. Multi-parametric optimization of the WEDM for processing aluminium silicate composite with 15–20% silicate (designated as Al/SiCp) was examined in the present work. A composite principal component was calculated using principal component analysis for multi-parametric optimization. The artificial neural network was employed for enhancing the performance of the process. Analysis of variance was performed to realize the influence of WEDM process parameters on the overall WEDM effectiveness. The WEDM response characteristics such as finish part roughness (Ra), material removal rate and kerf width were considered for this work. From the experimental findings, it is observed that the parameters, viz. the % composition of silicate, the pulse off time (POFF) and current (IP), are the most critical process parameters. The parameters obtained through the present analysis were silicate composition 15%, pulse on time 112 μs, POFF 56 μs, IP-3 A, wire feed rate 4 m/min, wire tension 10 kg and fluid pressure 13 kg/cm3.

Multi objective optimization of wire-electrical discharge machining of stellite using Taguchi – Grey approach

Wire Electrical Discharge Machining (WEDM) is one of advanced method of machining conceived from the concept of Electrical Discharge Machining. Unconventional method of material removal processes have been developed to overcome the difficulties in making of complex shapes in any electrically conductive work material by conventional machining methods. Stellite is a difficult to machine superalloy and extensively used in wear environments where its low friction coefficient minimizes the risk of seizing and galling. It possess good strength and lower thermal diffusion results in decreased tool life and poor machinability by conventional methods of machining. The present work details the multi objective optimization of WEDM process parameters by Taguchi-Grey approach. The experimental runs are planned by Taguchi’s design approach and Taguchi’s approach single objective optimization technique was employed for attaining the best possible process parameters combination for obtaining better machining performance. The experiments have been performed by considering the process parameters such as pulse on time, pulse off time and wire tension at three various levels. The ascendency of process variables are determined by ANOVA analysis. Taguchi - Grey methodology has been adopted for the WEDM process and optimum process parameters are determined for attaining improved multi performance characteristics.

Multi-response optimization of WEDM process parameters during the fabrication of microchannels for industrial applications

The dissipation of heat from integrated circuits and other electronic devices are transfer by devices known as microchannels. Microchannels can be fabricated by various traditional and modern machining methods. Among the traditional and modern machining methods, the wire electrical discharge machining (WEDM) has been recently explored as a good alternative machining process. WEDM has tremendous potential on account of the versatility of its applications. In this study, the effect of WEDM parameters such as peak current (Ip), pulse on time (Ton) and pulse off time (Toff) on the surface roughness (Ra) and material removal rate (MRR) is evaluated during the fabrication of microchannels. Response surface methodology orthogonal array has been used to carry out the experimental runs using brass wire (diameter 0.25 mm) as electrode. Single-response optimization through Genetic algorithm (GA) for maximum MRR = 7.0964261800mm3/min and minimum Ra = 3.35966619 µm have found for fabrication of brass microchannel are Ip = 200.104A, Ton = 118.4 µs, Toff = 51.81 µs, and Ip = 200.36A, Ton = 118.4 µs, Toff = 45.003 µs respectively. The confirmation results show the goodness of the developed models. A set of non-dominated preto-optimal solutions has been generated for multi-response optimization through GA. The best solution has been found for maximum MRR and minimum Ra are Ip = 178.3805A, Ton = 108.8105 µs, and Toff = 44.9989 µs. It is the multi-objective optimal solution to the fabrication of brass microchannel.

Milling Microchannels in Monel 400 Alloy by Wire EDM: An Experimental Analysis

This paper presents the results of an investigation on the capacity of wire electrical discharge machining (WEDM) to produce microchannels in the Nickel-based alloy, Monel 400. The main objective of the current study is to produce microchannels with desired/target geometry and acceptable surface quality. Square cross-sectional microchannels with dimensions of 500 × 500 µm were investigated. Experiments were conducted based on the one-factor-at-a-time approach for the key input WEDM process parameters, namely pulse-on time (TON), pulse-off time (TOFF), average gap voltage (VGAP), wire feed (WF), and dielectric flow rate (FR). Dimensional accuracy, machining speed, surface roughness, surface morphology, microhardness, and microstructure were analyzed to evaluate the microchannels. The minimum errors of 6% and 3% were observed in the width and depth of the microchannels, respectively. Furthermore, microchannels with enhanced surface integrity could be produced exhibiting smooth surface morphology and shallow recast layer (~0–2.55 µm).

Exploration on WEDM process parameters effect on LM13 alloy and LM13/SiC composites using Taguchi method

In manufacturing sectors and the automobile field’s aluminium and aluminium based composites usage in various range of applications owing to their higher physical and mechanical properties. Wire electrical discharge machining process is a highly complex, time varying & stochastic process. The process output is affected by input variables. Therefore a suitable selection of input variables for the wire electrical discharge machining (WEDM) process relies heavily on the operator's technology & experience due to wide range of demand. Metal removal rate (MRR) & surface finish (Ra) are sought to obtain precision work. Using Taguchi's parameter design, significant machining parameters affecting the performance measures are identified as pulse off time (Toff), pulse on time (Ton) and input current (amps). In the present study, first experimentation on LM13 aluminium alloy and LM13/SiC composites has been fabricated by squeeze casting process under optimal parametric condition to attain better hardness and tensile strength. In second phase of examination the machinability study were carried out of LM13 alloy and LM13/SiC composites through WEDM techniques input process variables such as pulse on time (Ton, 30–40 µs), pulse off time (Toff, 4–10 µs) and peak current (Ip, 1–3 Amp) on response of material removal rate (MRR) and surface roughness (Ra). The L9 OA Taguchi method was implemented for calculating the machining characteristics. The effect of each control factor on the performance measure is studied individually using the plots of signal to noise (S/N) ratio. The study demonstrates that the WEDM process parameters can be adjusted so as to achieve better metal removal rate and surface finish. Therefore, the optimal set of WEDM parameters is suggested for all manufacturing sectors.

MOGA approach in WEDM of advanced aluminium alloy

An experimental study on Wire Electrical Discharge Machining (WEDM) of 7075 aluminium (Al 7075) alloy has been carried out for the selection of the optimal machining parameter. Based on the Taguchi Methodology (L16 orthogonal array), number of experiments have been performed by considering pulse-on time, pulse-off time and wire tension as control parameters. The cutting speed and surface roughness have been considered responses. A mathematical model has been employed in order to predict the response parameters as the function of different control factors using non-linear regression analysis and the model has been verified by verification experiments. Finally, Multi objective genetic algorithm (MOGA) has been used to optimize the WEDM process parameters for this alloy. The present study demonstrates extremely useful for maximizing the productivity with desired surface finish.

Investigation of WEDM process parameters of Al–SiC–B4C composites using response surface methodology

Aluminum based composites are widely used in various engineering applications. Though the processing of these materials is easy, it requires advanced machining techniques to cut the same as it becomes harder after the addition of ceramic reinforcements. The wire electrical discharge machining (WEDM) process is employed for cutting of Al–SiC–B4C hybrid metal matrix composites with the varying machining conditions. The machining factors, namely current (12A–20A), pulse on time (100μs–120 μs), the wire feed rate (6 mm/min – 10 mm/min) and the content of B4C (weight percentage) in the composite are considered for the performance evaluation. A response surface methodology (RSM), the multi-objective optimization technique is adopted for determining the influence of parameters on the machining features like kerf width and cutting speed. The experimental analysis revealed the correlation between the input and output characteristics. The minimum kerf width of 0.271 mm is attained at a wire feed rate of 10 mm/min and current of 12 A. The maximum cutting speed of 4.76 mm/min is achieved at a pulse on time of 110 μs and a wire feed rate of 8 mm/min. The optimum machining condition obtained through RSM method is current of 20 A, pulse on time of 108.6 μs, wire feed rate of 10 mm/min and 5.65% of the B4C content in the composites.

Modeling and Optimization of Fractal Dimension in Wire Electrical Discharge Machining of EN 31 Steel Using the ANN-GA Approach.

To achieve enhanced surface characteristics in wire electrical discharge machining (WEDM), the present work reports the use of an artificial neural network (ANN) combined with a genetic algorithm (GA) for the correlation and optimization of WEDM process parameters. The parameters considered are the discharge current, voltage, pulse-on time, and pulse-off time, while the response is fractal dimension. The usefulness of fractal dimension to characterize a machined surface lies in the fact that it is independent of the resolution of the instrument or length scales. Experiments were carried out based on a rotatable central composite design. A feed-forward ANN architecture trained using the Levenberg-Marquardt (L-M) back-propagation algorithm has been used to model the complex relationship between WEDM process parameters and fractal dimension. After several trials, 4-3-3-1 neural network architecture has been found to predict the fractal dimension with reasonable accuracy, having an overall R-value of 0.97. Furthermore, the genetic algorithm (GA) has been used to predict the optimal combination of machining parameters to achieve a higher fractal dimension. The predicted optimal condition is seen to be in close agreement with experimental results. Scanning electron micrography of the machined surface reveals that the combined ANN-GA method can significantly improve the surface texture produced from WEDM by reducing the formation of re-solidified globules.

Optimization of WEDM Process Parameters for Cutting Speed using Taguchi technique

Abstract In this present experimental study, the effect and advancement of machining parameters on cutting speed in wire electrical discharge machining (WEDM) operations were studied. The hybrid metal matrix composite (MMC) was manufactured by process named as stir casting utilizing particulates of Silicon carbide and graphite each in Al6061 combination. The analyses were outlined with Taguchi L27 design matrix. WEDM parameters resemble pulse (PU) on time, current, PU off time, gapset voltage, wire tension and wire feed are considered. The impact of parameters on the cutting speed (CS) is dictated by utilizing examination of fluctuation. The optimized parameters are PU on time (Level 3), PU off time (Level 1), peak current (Level 2), gap set voltage (Level 1), wire feed (Level 3) and wire tension (Level 2) are the best combination to achieve best cutting speed. PU off time, set voltage, PU on time and discharge current have considerable effect and most influenced control parameters on CS