Wire Electrical Discharge Machining Process Research Articles

Taguchi optimization of Wire EDM process parameters for machining LM5 aluminium alloy.

LM5 alloy is suitable for metal castings for marine and aesthetic uses due to its admirable resistance to corrosion. In order to make intricate shapes in the LM5 alloy, this study intends to assess the impact of Wire Electric Discharge Machining process variables, like Pulse on Time (Ton), Pulse off Time (Toff), Gap Voltage (GV) and Wire Feed (WF) on responses like Material Removal Rate (MRR), Surface Roughness (SR), and Kerf Width (Kw). The LM5 aluminium alloy plate was produced through stir casting process. SEM, EDAX and XRD images confirm the LM5 Al alloy's microstructure and crystal structure. WEDM studies were conducted using design of experiments approach based on L9 orthogonal array and analysed using Taguchi's Signal to Noise Ratio (S/N) analysis. Pulse on Time has the greatest statistical effects on MRR (68.25%), SR (79.46%) and kerf (81.97%). In order to assess the surface integrity of the WEDM machined surfaces, the SEM study on the topography was conducted using the optimum surface roughness process variables: Ton 110 μs, Toff 50 μs, GV 40 V, and WF 9 m/min. SEM images show the recast layer and its thickness. The average absolute error for MRR is 1.69%, SR is 3.89% and kerf is 0.88%, based on mathematical (linear regression) models. The Taguchi's Signal to Noise ratio analysis is the most appropriate for single objective optimization of responses.

The Influence of the Gap Phenomenon on the Occurrence of Consecutive Discharges in WEDM Through High-Speed Video Camera Observation

The Wire Electrical Discharge Machining (WEDM) process is an accurate method for manufacturing high-added-value components for industry. Continuous developments in the process have resulted in specialized machines used in sectors such as aerospace and biomedical engineering. However, some fundamental aspects of the discharge process remain unresolved. This work aims to study the influence of discharge location and bubble expansion on the occurrence of subsequent discharges. A high-speed video camera observation system was constructed to capture images of each discharge. From the acquired images, an algorithm was devised to determine the discharge location based on grayscale analysis. Moreover, the voltage and current waveforms of the discharges and the framing signals of the high-speed video camera were then obtained using an oscilloscope. Synchronizing the observation images and signals allowed for calculating the delay time for each single discharge. The results indicate that most of the discharges occurred near the boundary of the bubble and during bubble expansion. This finding has been observed for a variety of machining conditions and can be explained by the effect of the debris particles concentrated at the bubble boundary. This study provides useful information for better understanding the discharge process in WEDM.

Machining performance analysis of wire-EDM for machining of titanium alloy using multi-objective optimization and machine learning approach

This study investigates the multi-objective optimization of process parameters in wire electrical discharge machining (WEDM) using titanium alloy grade 5 and zinc-coated brass microwire. The novelty lies in the comprehensive investigation of the effects of varying pulse on time (Ton), wire tension (WT), and wire feed rate (WF) on output responses, including surface roughness (SR), surface crack density (SCD), corner diameter (CD), and kerf width (KW). The Box-Behnken design, a sophisticated response surface methodology (RSM) technique, is employed to efficiently explore the complex relationships and interdependencies between these input and output parameters. The study's novel approach incorporates machine learning algorithms, including decision tree, light gradient boosting machine (LightGBM) and random forest, to predict the characteristics of the laser-cut components based on the input parameters. The contour plots generated provide valuable insights into the underlying patterns and associations between input and output features, aiding in the understanding of the WEDM process. The results reveal that the optimized values for SR, SCD, CD, and KW are obtained at specific machining conditions: Ton of 10 μs, WF of 3.79 m/min, and WT of 8 gf. Among the machine learning algorithms employed, random forest outperforms the others in predicting KW, SR, SCD and CD, exhibiting significantly lower mean squared error (MSE) and mean absolute error (MAE) values. This study contributes to the field of WEDM by providing a comprehensive analysis, incorporating advanced experimental design techniques and machine learning algorithms, which can aid in process optimization and quality control in manufacturing applications.

Enhancing of Material Removal Rate and Surface Roughness in Wire EDM Process using Grey Relational Analysis

Enhancing of Material Removal Rate and Surface Roughness in Wire EDM Process using Grey Relational Analysis

Development of optimized ensemble machine learning-based prediction models for wire electrical discharge machining processes

This paper proposes development of optimized heterogeneous ensemble models for prediction of responses based on given sets of input parameters for wire electrical discharge machining (WEDM) processes, which have found immense applications in many of the present-day manufacturing industries because of their ability to generate complicated 2D and 3D profiles on hard-to-machine engineering materials. These ensembles are developed combining predictions of the three base models, i.e. random forest, support vector machine and ridge regression. These three base models are first framed utilizing the training datasets, providing predictions for all the responses under consideration. Based on these predictions, two optimization problems are formulated for each of the responses, while minimizing root mean squared error and mean absolute error, for subsequent development of two optimized ensembles whose predictions are the weighted sum of the predictions of the base models. The prediction performance of all the five models is ascertained through nine statistical metrics, after which a cumulative quality loss-based multi-response signal-to-noise (MRSN) ratio for each model is computed, for each of the responses, where a higher MRSN ratio indicates greater accuracy in prediction. This study is conducted using two experimental datasets of WEDM process. Overall, the optimized ensemble models having higher MRSN ratios than the base models are indicated to deliver better prediction accuracy.

Growth of α-Al2O3 layer involving abnormal oxides in FeCrAl alloy tube fabricated by WEDM process and electrical insulating performance in fusion reactor blanket

The tubular specimens of FeCrAl alloy APMT (Fe-22Cr-5Al-3Mo) were fabricated by a wire electrical discharge machining (WEDM) process. The oxidation characteristics of the machined surface were investigated by means of the oxidation tests performed in air atmosphere at the temperatures of 1273 K and 1373 K up to 100 h. The abnormal oxides such as CuAl2O4 and ZnAl2O4 were grew on the recast layer which was locally remained on the machined surface even after the mechanical polishing procedure. The α-Al2O3 layer involving the abnormal oxides was formed on the machined surface in the oxidation treatment at 1273 K for 10 h. The apparent electrical conductivity of the layer involving the abnormal oxides was larger than that without the abnormal oxides. Thick Al-rich oxide layer was formed under the abnormal oxides when the oxidation treatment was performed at 1373 K. The apparent electrical conductivity of the oxide layer became lower due to the presence of the thick Al-rich oxide. This oxide layer formed at 1373 K could function as an electrical insulating layer to reduce the magnetohydrodynamic pressure drop in liquid breeder blanket systems of magnetic confinement fusion reactors.

Application of integrated entropy-COPRAS and ANN approaches for maximizing wire EDM machinability attributes of Al6082-T6/GNP/TiB2 composites

Wire Electrical Discharge Machining (WEDM) utilizes electrical sparks to cut conductive materials precisely. It employs a thin, electrically charged wire to erode the material, creating complex shapes with high accuracy, tight tolerance, and minimal material waste. The current study investigates the effect of WEDM process parameters on the various output properties of machining a hybrid Aluminium metal matrix nanocomposite (HAMMNC). A series of experiments were conducted on a composite constructed using Taguchi’s standard L18 Orthogonal Array (OA) design. The acquired responses of Material-Removal-Rate (MRR), Surface-Roughness (SR), and Kerf-Width (KfW) were optimized using integrated Entropy-COPRAS and artificial neural network (ANN) algorithms respectively. The relative significance ( Qi )values obtained for the alternatives are analyzed with the Taguchi method. The outcomes showed that the optimal setting of WEDM process variables is achieved at Servo Voltage: high volts; Pulse-on-time: 60 μs; Pulse-off-time: 16 μs; Peak Current: 6 Amp; Variable frequency: 18 Hz; Speed: 50 RPM; Sim Speed: 30 RPM respectively. The models developed from ANN were adequate and accurate from the results of error histogram and regression plots and they are best suited for the prophecy of future responses. The surface morphology studies confirmed the appearance of pockmarks, voids, and globules of debris on the Wire EDMed surfaces.

Machinability of gas metal arc based 3D printed Al-Mg 5356 alloy using wire-EDM

Wire-Arc Additive Manufactured (WAAM) is relatively new method of metal 3D printing in which the raw material is heated by the gas metal arc and the molten metal pool is deposited layer-by-layer using a computer numerically controlled axis drive system. Since WAAM needs a finishing process for attaining the final dimensions of the components, there is a need to investigate the machinability aspects of WAAM fabricated materials. This work investigates the machinability of Al-Mg 5356 alloy test samples fabricated by WAAM process using wire-electric discharge machining (Wire-EDM). The test samples were subjected to Wire-EDM and the obtained material removal rate (MRR), kerf width (KW) and surface roughness (Ra) were investigated at different Wire-EDM process settings of voltage, current, pulse-on time (Ton), pulse-off time (Toff) and wire speed (Ws). Statistical analysis revealed that current had a significant influence on MRR. Ton had a strong influence on KW and Ra, whereas Toff exhibited a considerable impact on all these responses. Notably, Ws demonstrated a significant impact on Ra. However, voltage was found to have statistically negligible impact on all the machining responses. Microstructural investigations and compositional analysis were conducted providing valuable information on the cut surfaces. The results derived from the present investigation are useful for predicting the optimum process parameter settings for machining of WAAM-based 3D printed Al-Mg alloy in various manufacturing industries.

Regulating cutting fluid parameters for optimal energy and economic performance: Methods for efficient and Low-Energy electrical machining

Scholars today have been dedicated to researching economically viable and efficient methods to enhance the energy efficiency of manufacturing, aiming for sustainable production. Wire Electrical Discharge Machining (WEDM) holds a significant position in current mechanical processing due to its unique operational principle, which results in notably higher energy consumption compared to conventional processing methods. Various factors influencing machining performance are complex, among which the condition of the cutting fluid plays a crucial role. Effectively controlling the parameters of the cutting fluid to improve its discharge state during machining is vital for enhancing energy and economic efficiency. Specifically, this study analyzes the characteristics of the WEDM process, models its resource and energy consumption, and predicts its economy. A method for optimizing control of cutting fluid parameters is proposed. By correlating different cutting fluid parameters with machining performance using the selected machine tool, and recommending combinations of cutting fluid parameters that ensure quality while improving the material removal rate, the study aims to achieve optimal energy efficiency and minimal cost. The results show that the proposed models for energy efficiency and economic prediction have an average accuracy of 96.64 %. An optimized machining scheme selected reduces machining time and energy consumption by approximately 1255.6 s (about 19.14 %) and 2240112.92 J (about 17.48 %), respectively, and saves about ¥ 6.084 (approximately 14.90 %) in machining costs. Additionally, the energy consumption of the pulse system’s proportion of total energy use increased by 1.63 %. The research demonstrates a general method for specifying combinations of cutting fluid parameters aimed at optimal energy efficiency and economy during the process optimization design stage of machining, providing a reference for improving the energy efficiency of wire cutting operations.

Experimental Investigation on Wire Electric Erosion Behaviour of Silicon Dioxide Particulate Reinforced Composite

The intend of current study was focused on the prediction of material removal rate (MRR) and surface roughness (SR) for the AA7050-SiO2 composite during wire electric erosion or discharge machining (WEDM) process using a brass (Br) wire electrode. Here, stir casting process was employed to develop the AA7050 matrix composite with inclusion of 10wt.% SiO2 particle reinforcement. The multi-objective optimization method of Technique for order preference by similarity to ideal solution (TOPSIS) approach has been applied to find out the optimal setting of input machining parameters such as peak current (Ip), pulse-on time (Ton) and pulse-off time (Toff). Furthermore, the significant effects of parameters were identified by analysis of variance (ANOVA). Taguchi L9 (33) orthogonal design has been formulated to perform the experimental work. TOP SIS results stated that the optimal setting of Ip at 30 amps, Ton of 130 μs and Toff of 55 μs provide the better MRR with lesser SR. The ANOVA results noticed that Ip has the prime noteworthy parameter over the adopted responses having a contribution of 45.67%, followed by Ton (32.34%) and Toff (12.26%), respectively. The confirmation test was carried out by the optimal parameters setting to verify the predicted results. Finally, the scanning electron microscopy (SEM) test was carried out for the machined surface of the composite specimen and it was reveals that the formation of craters and recast layer thickness in the machined surfaces.

Parametric investigation of wire-EDM parameters for micro channel as a foundation of high-quality micro needle array fabrication

Micro channels are widely used in microfluidics, and medical science for the extraction of DNA, electrophoresis, and blood protein analysis. In the current work, the wire electrical discharge machining (WEDM) process was used to fabricate micro channels on the stainless steel 316 (SS316) substrate. To check the feasibility of fabricating high-quality micro channels and thereafter micro-scale structures with the WEDM process the effect of different process parameters such as pulse-off time, pulse-on time, servo gap reference voltage and wire feed rate were investigated. The quality attributes taken were namely surface roughness of the channel base, overcut, micro channel depth, and material removal rate (MRR). The pulse-on time significantly affects surface roughness and MRR. The servo gap reference voltage is the key process parameter affecting the overcut and micro channel depth. It was observed from the parametric study that 15 µs pulse-on time, 35 µs pulse-off time, 17 v servo gap reference voltage, and 8 m/min wire feed rate are the optimum levels for getting high MRR, low surface roughness, less overcut and high micro channel depth. Additionally, the topographical, morphological, surface, and sub-surface characterization were also carried out to assess the surface integrity and extent of thermal damage caused by the WEDM process. With the optimum combination of process parameters a 6 × 6 micro needle array was successfully fabricated on the same substrate using micro channels as a basis of the fabrication process for the first time using the WEDM process.

Prediction and Optimization of Ultrasonic Vibration Assisted Wire EDM Process for AISI P20 + Ni Using COOT Optimization Algorithm Based Deep Neural Network

Prediction and Optimization of Ultrasonic Vibration Assisted Wire EDM Process for AISI P20 + Ni Using COOT Optimization Algorithm Based Deep Neural Network

Multi Responses Optimization of Wire Electrical Discharge Machining (WEDM) Parameters of Tool Steel Using Grey Relation Analysis

The aim of this work is to study the effects of several wire electrical discharge machining (WEDM) process parameters, such as the servo voltage (SV), the pulse on time (TON), and the pulse off time (TOFF) on the surface finish (SR) and the kerf width (KW) of stainless steel 304 as a workpiece material. A multi-responses optimization approach based on Grey relational analysis has been designed, and it was discovered that the main affecting factor is the pulse on time followed by the servo voltage. According to the data, the grey relation analysis (GRA) grade for the second trial, including (a servo voltage of 14V, a pulse on time of 100µs, and a pulse off time of 45µs), was the optimum combination of settings that may concurrently optimize all of the specified response qualities. By utilizing the regression analysis, the mathematical equations illustrating the link between the input parameters and the responses have been established. In particular, the findings of this article will assist manufacturing engineers in selecting an optimal set of process parameters for machining stainless steel (SS304) grade.

Machinability and pulse characteristics of Ti-16Al-14Nb (α/β) alloy in wire-electric discharge machining process: A surface integrity study

The goal of the present work is to produce the efficient cutting of Ti-16Al-14Nb (α/β) alloy through wire-electric discharge machining and contemplate the terminologies such as machining ability, surface integrity and material removing capability. For this, the experimentation has been designed by considering three process parameters, pulse-on time ( Ton), pulse-off time ( Toff) and peak current ( Ip) at three significant levels (low, high, intermediate) in L27 order designed from design of experiments. The effect of these process variables on pulse characteristics, surface roughness ( Ra), kerfwidth ( Kw) and material removal rate ( MRR) is analyzed and mathematically modeled with analysis of variance. The results state that the highest MRR (0.521, 0.51, 0.506 mm3/sec) and low surface roughness (8.91, 8.89, 8.68 µm) values are observed at the highest level (110 µs, 15 µs, 8 A). If the pulse duration is kept at low ( Ton = 40µs, Toff = 3µs), the increase in peak current from 3 A to 8 A leads to a 64.54% increase in MRR and a 52.9% increase in Ra values. ANOVA results stated that Ton has contributed 20.4%, 35.8% and 19.3%, and Ip has contributed 43.1%, 30.5% and 46.6% influence on MRR, Kw and Ra respectively. The voltage–current characteristics studies confirmed that the strong intense pulses at a higher peak current (8 A) resulted in the increased MRR (0.521 mm3/sec) and Ra (8.43 µm) leading to the formation of swages and ridges. The surface integrity analysis derived from Abbott–Firestone curve agreed that the lowest level has shown better skewness ( Rsk = −0.274) and at highest level showed a larger deviation in skewness ( Rsk = +2.672) signifies that higher asymmetry (poor) of the surface.

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%.

Finite element analysis of wire EDM process parameters on performance characteristics of Al–SiC hybrid composite with micro and nano reinforcements

Aluminum Hybrid Composites (AHC) microchannels find extensive applications in aerospace, refrigeration and air conditioning (RAC), microelectronics, automotive, biomedical, and marine industries due to their improved mechanical properties. The combination of lower density, high hardness, elevated thermal and electrical conductivity, superior elasticity, and heightened oxidizing ability at low temperatures makes machining aluminum hybrid composites challenging through orthogonal methods in an open environment. Therefore, it is imperative to investigate the impact of various machining conditions to determine optimal parameter values for microchannel fabrication in aluminum hybrid composite materials. This study aims to identify the ideal machining parameters for Al–SiC micro-SiC nano hybrid composites using wire electrical discharge machining. Four distinct process parameters (pulse on time, gap voltage, wire tension, and wire feed rate) and two response parameters (material removal rate (MRR) and surface roughness (SR)) were considered for process optimization. Additionally, finite element modeling was conducted for the aforementioned process, and the results were compared with experimental outcomes. Single-variable optimization yielded maximum MRR at 125 tons (μs), 25 SV (volt), 12 WT (kgf), and 2 WFR (m/min), and minimum SR at 125 tons (μs), 10 SV (volt), 12 WT (kgf), and 4 WFR (m/min). Single-variable optimization for simulations resulted in maximum MRR at 125 tons (μs), 40 SV (volt), 4 WT (kgf), and 6 WFR (m/min), and minimum SR at 110 tons (μs), 10 SV (volt), 12 WT (kgf), and 2 WFR (m/min). Precise modeling aids in minimizing unnecessary machining, enabling exploration of a broader range of experimental results, thereby promoting environmentally friendly and sustainable machining practices.

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

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

Generation of array of square microcavity on quartz by ultrasonic micromachining process utilizing developed micro tool

Quartz possesses distinct characteristics such as exceptional hardness, high brittleness, non-conductivity, remarkable corrosion resistance, efficient transmission within specific wavelengths, strong electrical insulation properties, and the ability to withstand high temperatures and thermal shocks. These extraordinary attributes have led to its component in a wide range of applications, including the fabrication of microelectronic chips, biochips, biological channels, microfluidics, optical lenses, beam-splitting lenses, etc. Ultrasonic micromachining (USMM) is a highly promising technique within the realm of micromachining to create micro features on quartz surfaces. In the present research, array of square micro tools has been designed and developed by the wire-electrical discharge machining (EDM) process. By utilizing the developed array of square micro tools, microcavity arrays have been generated by the USMM process. The influences of process parameters on material removal rate (MRR), width deviation (WD), angular deviation, and taper of square microcavities have been investigated in the present research work. The higher MRR is obtained as 1.427 mm3/min at higher abrasive slurry concentration and lower WD of micro cavities is obtained as 37 µm and lower taper angle is 1.109° at 5% abrasive slurry concentration. The lower value of angular deviation is obtained as 0.836° at a lower power rating during the generation of an array of square microcavity on quartz by the USMM process. By employing array of micro tools, the accuracy of micro features can be improved, and also productivity can be increased.

INVESTIGATION OF THE MACHINING PERFORMANCE OF ALUMINUM–BRONZE ALLOY IN WEDM USING TAGUCHI METHOD

Aluminum–Bronze alloys (Al-10%–Cu-balance) are difficult-to-machine cut alloys. Copper (Cu) metal itself is not suitable for machining. The machining performance of the Aluminum–Bronze alloy sample was investigated by the Wire Electric Discharge Machining (WEDM) method, which is among the non-traditional manufacturing methods used in conductive materials that are difficult to process. The aluminum–bronze sample with a diameter of 20[Formula: see text]mm was cut with 0.18[Formula: see text]mm molybdenum wire electrode. Table feedrate, pulse-on-time and pulse-space were used as experimental parameters. Surface roughness and energy consumption values were measured in WEDM process. According to Taguchi analysis and ANOVA results, it was determined that the pulse-on-time parameter was effective on the surface roughness and the table feedrate parameter was effective on the energy consumption.

Investigation of experimental and numerical analysis of thermal load of WEDM on all metals through process parameters on wire electrode temperature

The thermal load on wire electrode in a wire electrical discharge machining (WEDM) is the most important factor in predicting wire electrode breakage. Thermal failure of wire electrode may be expected when the maximum temperature of the wire crosses a critical value. The maximum temperature of wire electrode is a function of process parameters of the machine and hence the failure depends upon what settings we choose to run WEDM process. In this investigation, simulation is done using finite-difference thermal model for continuous pulse without clustering of the spark. The model is used for determining distribution of the temperature throughout the length of wire electrode and analyzed the significance of various factors on wire breakage. The outcome of the present simulation model is validated by comparing it with the works of other researchers followed by its validation through experiments. A parametric study is also done to determine the effect of input process parameters and their interaction on the temperature of the wire. For finding the interaction effect, analysis of variance analysis is carried out followed by developing an empirical model. The predictive performance of our simulation model and developed regression models is assessed by comparing them with experimental value. The regression model is found to be closer, with an average percentage deviation of −2.55% on the experimental dataset, compared to 2.64% for the simulation model. During the validation test, the minimum and maximum deviations are 0.08% and 0.20% for simulation versus regression model predictions, −3.31% and −1.65% for experimental versus predicted results, and 1.82% and 3.40% for simulation versus experimental results, respectively. It is observed through this study that temperature of wire electrode mainly depends on the intensity of sparks per unit area which is a function of the input parameters of the WEDM.