Wire Electrical Discharge Machining Process Research Articles

Dimensional Accuracy, Surface Roughness and Hardness Properties for Microplate Implants Manufacturing by EDM Die-Sinking Process

The high cost of manufacturing microplate implants is a primary issue. This is because the production of microplate implants uses the micro-milling and wire-EDM process. Production costs can be reduced using one machining process, and die-sinking EDM is an alternative in the manufacturing of microplate implants. This paper investigates the capability of EDM die-sinking in manufacturing microplate implants. This paper also studies the reaction of electrode materials and pulse currents to the microplate’s dimensional accuracy, surface roughness and hardness. The process of EDM die-sinking uses electrodes of graphite and copper with pulse current variance of 6, 9, and 13 A. The experiment results indicate that the process of EDM die-sinking success in manufacturing microplates on commercially pure titanium sheets. Decreasing the pulse current can improve dimensional accuracy, smoothen surface roughness and minimize the hardness decrease of the microplate. These results are better using the copper electrode compare with the graphite electrode. The best quality of the microplate is at 93.3% dimensional accuracy, 5.28 µm surface roughness, and a 12% decrease in hardness. The best quality microplates was achieved by using copper electrodes with a 6 A pulse current.

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.

A comparative study on parametric optimization of a wire-electrical discharge machining process using MCDM methods in intuitionistic fuzzy environment

A comparative study on parametric optimization of a wire-electrical discharge machining process using MCDM methods in intuitionistic fuzzy environment

Investigation on the performance of wire electrical discharge machining (WEDM) using aluminium matrix composites (AMCs) micro-channel

This study focuses on microchannel fabrication of Al-10% SiC composite using Wire-EDM process with the applications of single variable and multi variable, optimization. This work was targeted keeping in considerations the wider scope of microchannel applications in the fields such as medical, aerospace, automobile, etc. In this research work, the experiment was conducted to achieve high precision and quality surface finish in terms of minimum surface roughness (SR), high material removal rate (MRR), low tool wear rate (TWR) and optimum spark gap (SG). The used experiments were design on the basis of Taguchi design of experiment approach. Four input process parameters were considered namely, pulse on time (Ton), sparking voltage (SV), wire tension (WT), and wire feed rate (WFR). For the experimentation task, the workpeice (Aluminum and silicon carbide (10% Wt SiCmicro)) material, in a desired shape was fabricated through stir casting process. The tool electrode applied for the experiment purpose had specification as, 0.25 mm diameter brass alloy wire (copper 63 percent and zinc 37 percent). For single variable optimization ANOVA (analysis of variance) was applied while as, for multi variables optimization, grey relational analysis (GRA) was applied. The results of the study indicated that, on the basis of single variable optimization, the highest contribution of Ton resulted in case of the performance measures, MRR and SR while as, for TWR and SG, the highest contribution of sparking voltage, observed. Furthermore, the findings of the study indicated that, on the basis of multi variables optimization, the optimal combination of input process parameters is, pulse on time 125 μs, sparking voltage 10 volt, wire tension 12 kgf, and wire feed rate 4 m min−1. These results also get support from the previous research works. The findings of this study be useful for microchannel manufacturing and other related industrial applications.

Machining of ZrO2 using wire EDM: an experiment based investigation via assisted electrode

ABSTRACT Machining of electrically insulating ceramic materials has been a challenging task for ceramic industries. Assisted electrode method (AEM) based wire electrical discharge machining (WEDM) process emerged as an advanced option to machine insulating ceramics. This study deals with the machining of yttrium (Y2O3) stabilised zirconia (ZrO2) ceramic using copper (Cu) and stainless steel (SS) assisted electrode (AE) in presence of alumina (Al2O3) powder, ethylene glycol and demineralised water mixed dielectric fluid. The effect of different AE materials, pulse on time (TON), peak current (IP) on length of cut (LC), surface roughness (Ra), and average micro-hardness (MH) were investigated. Further, the comparison was made between minimum and maximum LC, and Ra (case-(i) and case-(ii)), respectively. SEM and EDS were carried out to analyse the surface characteristics and distribution of elements of the machined surface. Experimental results show that machining with SS AE at high levels of TON and IP provides high spark energy that results in higher value of LC (1300.910 µm), Ra (24.520 µm) and MH (1401.713 HV). Increasing TON and IP significantly increases the LC and Ra. Moreover, hardness near the lower junction was higher than the hardness near the upper junction and middle region of the machined surface.

Experimental Investigation into the Influence of the Process Parameters of Wire Electric Discharge Machining Using Nimonic-263 Superalloy.

Nimonic alloy is difficult to machine using traditional metal cutting techniques because of the high cutting forces required, poor surface integrity, and tool wear. Wire electrical discharge machining (WEDM) is used in a number of sectors to precisely machine complex forms of nickel-based alloy in order to attempt to overcome these challenges and provide high-quality products. The Taguchi-based design of experiments is utilized in this study to conduct the tests and analyses. The gap voltage (GV), pulse-on time (Ton), pulse-off time (Toff), and wire feed (WF), are considered as the variable process factors. GRA is used for the WEDM process optimization for the Nimonic-263 superalloy, which has multiple performance qualities including the material removal rate (MRR), surface roughness (SR), and kerf width (KW). ANOVA analysis was conducted to determine the factors' importance and influence on the output variables. Multi objective optimization techniques were employed for assessing the machining performances of WEDM using GRA. The ideal input parameter combinations were determined to be a gap voltage (GV) of 40 V, a pulse-on time (Ton) of 8 µs, a pulse-off time (Toff) of 16 µs, and a wire feed (WF) of 4 m/min. A material removal rate of 8.238 mm3/min, surface roughness of 2.83 µm, and kerf width of 0.343 mm were obtained. The validation experiments conducted also demonstrated that the predicted and experimental values could accurately forecast the responses.

Machinability of Titanium Grade 5 Alloy for Wire Electrical Discharge Machining Using a Hybrid Learning Algorithm

Titanium alloys have found widespread use in aviation, automotive, and marine applications, which makes their implementation in mass production more challenging. Conventional methods of removing these alloy materials are unsuitable because of the high wear rate of cutting and slower rate of processing. The complexities of these materials have prompted the creation of cutting-edge machining methods. Wire Electrical Discharge Machining (WEDM) is a technique that has the potential to be useful for the removal of materials that are harder and electrically conductive. In order to create intricate designs, this method is frequently employed. The input factors, including pulse duration (on/off) and peak current, were taken into account during the experimental design process. The rate of material removal, surface roughness, dimensional deviation, and GD&T errors were opted for as performance indicators. The approach proposed by Taguchi was selected for the investigation of the process factors, and an Analysis of Variance was selected to find out the relative momentousness of each factor. From the analysis it is perceived that the applied current is the predominant factor that influences the chosen output characteristics. The aspiration of this article is to evolve a decision-making model based on a hybrid learning method which can be adopted to predict the selected output measures that affect the WEDM process. According to the findings, the value of the ANFIS-GRG, which was predicted to be 0.7777, was in fact closer to that value than any other value. The proposed model has the ability to help make a variety of different production processes more efficient. The analysis showed that the model’s functionality was enhanced, which helps producers make well-informed decisions.

Investigating wettability and corrosion resistance of the titanium alloy surface engineered by the WEDM process

Ti-6Al-4V or the grade 5 titanium alloy, is widely used as a biomedical implant material because of its high specific strength and corrosion resistance. The formation of oxide layer over the Ti-6Al-4V alloy produced by electrical discharge machining (EDM) process was reported to act as a corrosion barrier and promote biocompatibility. In this study, surface engineering was conducted by machining micro-channels and micro-pillars over Ti-6Al-4V surface using wire-EDM (WEDM) process to investigate surface topography, corrosion resistance and wettability for improved biocompatibility. Four different samples were produced using varied micro features and varied surface topography generated by WEDM. Two of the samples were prepared by multi-pass surface finishing before fabricating micro-patterns on the surface, whereas the other two samples containing micro-patterns were first machined by single pass WEDM process. The corrosion testing was carried out on fabricated samples in simulated body fluid. Surface wettability of the engineered Ti-6Al-4V samples were carried out by measuring water contact angles of the surfaces. Surfaces with micro-channels and micro-pillars with multi-pass surface finishing provided better results in terms of corrosion resistance compared to the samples prepared by conventional WEDM process. The machined surface with micro-channels that underwent multi-pass finishing exhibited the highest corrosion resistance as well as the highest water contact angle among the four samples.

Investigation of machining characterization of solar material on WEDM process through response surface methodology

Abstract The photovoltaic sector needs a high-throughput slicing method that produces minimal waste to meet rising demand. Wire electric discharge machining (WEDM) has emerged as an alternative slicing method in recent research efforts. Polycrystalline silicon is sliced using the WEDM process with a zinc-coated electrode of Ø0.25 mm in diameter. Experiments were planned and conducted according to Box Behnken’s design of experiments. As inputs, seven different process parameters were used: pulse on time (PONT), pulse off time (POFFT), peak current (PC), spark gap voltage (SGV), wire feed (WF), wire tension (WT), and water pressure (WP). Response parameters measured were cutting speed (CS), surface roughness (SR), and kerf width (KW). Various process parameters have also been analyzed with ANOVA methods for predictive modeling. Based on experimental data, this study determines the appropriate optimal solutions via desirability functions. During the WEDM process, the PONT, POFFT, PC, and SGV significantly influence the discharge energy on the sliced surface. As a result of this study, CS of 0.78 mm2/min, SR of 2.87 μm, and KW of 0.70 mm were observed at the optimal settings of PONT of 119 μs, POFFT of 42 μs, PC of 38A, SGV of 36V, WF of 3 mm/min, WT of 2 kg and WP of 6 kg/cm2. Surface morphology was determined using scanning electron microscope and Energy dispersive X-ray to investigate the surface characteristics.

Advanced Optimization of Surface Characteristics and Material Removal Rate for Biocompatible Ti6Al4V Using WEDM Process with BBD and NSGA II.

Machining titanium alloy (Ti6Al4V) used in orthopedic implants via conventional metal cutting processes is challenging due to excessive cutting forces, low surface integrity, and tool wear. To overcome these difficulties and ensure high-quality products, various industries employ wire electrical discharge machining (WEDM) for precise machining of intricate shapes in titanium alloy. The objective is to make WEDM machining parameters as efficient as possible for machining the biocompatible alloy Ti6Al4Vusing Box-Behnken design (BBD) and nondominated sorting genetic algorithm II (NSGA II). A quadratic mathematical model is created to represent the productivity and the quality factor (MRR and surface roughness) in terms of varying input parameters, such as pulse active (Ton) time, pulse inactive (Toff) time, peak amplitude (A) current, and applied servo (V) voltage. The established regression models and related prediction plots provide a reliable approach for predicting how the process variables affect the two responses, namely, MRR and SR. The effects of four process variables on both the responses were examined, and the findings revealed that the pulse duration and voltage have a major influence on the rate at which material is removed (MRR), whereas the pulse duration influences quality (SR). The tradeoff between MRR and SR, when significant process factors are included, emphasizes the need for a reliable multi-objective optimization method. The intelligent metaheuristic optimization method named nondominated sorting genetic algorithm II (NSGA II) was utilized to provide pareto optimum solutions in order to achieve high material removal rate (MRR) and low surface roughness (SR).

Development and solving of optimization model for WEDM of AISI D3 using TLBO algorithm

Manufacturing companies employ advanced machining techniques to create complex and accurate products, which require selecting optimum process parameters to achieve the desired quality and cost-effectiveness. Traditional techniques such as Design of Experiments and Response Surface Methodology optimize locally and may not be sufficient for complex systems. This study aims to optimize the Wire Electrical Discharge Machining process of AISI D3 material using Response Surface Methodology and Teaching and Learning-Based Optimization (TLBO), which can optimize globally. The proposed model can improve the quality and cost-effectiveness of the machining process, making it more suitable for various manufacturing applications.

Multi-Response Optimization and Influence of Expanded Graphite on Performance of WEDM Process of Ti6Al4V

Wire electrical discharge machining (WEDM) is widely preferred for machining difficult-to-cut materials like Ti6Al4V. In the present study, current, pulse-off-duration (Toff), and pulse-on-duration (Toff) were identified as vital input factors for the WEDM process of Ti6Al4V. Material removal rate (MRR) and surface roughness (SR) were selected as output measures for the study. The experiments were carried out by employing Taguchi’s L9 design at three levels. Empirical models were generated, which give the relationship between the input and output factors of the process. To check the acceptability of the model terms, analysis of variance (ANOVA) was used. The regression mode was observed to be significant for the output measures. For MRR, Toff was recorded as the highly significant factor affecting the response values with 74.95% impact, followed by Ton with 16.39%, and current with 6.56%. In the case of SR, Ton was found to be a highly significant factor with a 50.24% impact, followed by current with 43.99%, and Toff with 1.47%. Further, multi-objective optimization by using the HTS technique was performed. The effect of expanded graphite (EG) nano-powder has been studied on the output factors of MRR and SR. The use of EG nano-powder was found to improve WEDM operations as MRR was increased by 45.35%, and simultaneously, SR was reduced by 36.16%. Lastly, the surface morphology of the machined surface was investigated by employing SEM to understand the effect of EG nano-powder. The results have shown a reduction in surface defects by using EG nano-powder compared to the conventional WEDM process.

Investigation on tool wear mechanisms and machining tribology of hardened DC53 steel through modified CBN tooling geometry in hard turning

DC 53 steel has emerged as a possible replacement of AISI D2 steel possessing competitive hardness and better toughness. In the current work, turning of DC 53 steel was conducted via Xcel modified inserts by varying workpiece hardness levels (40 and 60 HRC), cutting speed (130 and 160 m/min), feed rate (0.07 and 0.112 mm/rev), and depth of cut (0.07 and 0.17 mm). A two-level 4-factor full factorial design was employed entailing 16 runs. An analysis of variance (ANOVA) was conducted to statistically analyze the effect and contributions of input parameters on response variables namely tool life, surface roughness, volume of material removed, power consumption, and machining zone temperature. Results show that the tool life, surface roughness, volume of material removed, and machining zone temperature are primarily affected by the hardness of DC53 with PCRs of ~ 96%, ~ 25%, ~ 62%, and ~ 25%, respectively. At a 40 HRC hardness value, true crater wear was observed due to continuous chips sliding at the rake face while for the workpiece having a 60 HRC, discontinuous chip formation produced less prominent crater wear. SEM images revealed complete delamination of the coating from the tool surface with adhesion and attrition wear identified as the main wear mechanisms. The formation of a groove pattern was also noticed on the flank face. The minimum surface roughness was 0.90 µm-Ra for the workpiece having a 40 HRC hardness level, and the same value was obtained for 60 HRC as well. The threshold value of the feed rate for the excellent performance of these inserts was less than 0.20 mm/rev. Additionally, the turning process proved to be productive for this material along with a lower surface roughness value in comparison to the wire EDM process.

Optimization of wire-EDM process parameters for Ni–Ti-Hf shape memory alloy through particle swarm optimization and CNN-based SEM-image classification

Shape memory alloys (SMAs) have the unique ability to regain their shape under specified conditions, making them extremely useful for a wide range of applications. However, non-traditional machining techniques could be more effective for high-temperature SMAs as they provide better shape recovery properties than conventional methods, necessitating this study's unconventional wire electric discharge machining procedure. The surface morphology of Ni–Ti-Hf-based alloys was examined using scanning electron microscopy. A convolutional neural network model was used to categorize SEM images based on the material removal rate, utilizing the pixel intensity histogram of the processed images. The study discovered that the material remelted as the discharge energy increased, leaving lumps and globules on the surface. The size of debris lumps, pores, and globules increased with increasing Ra values. Moreover, widening the inter-electrode gap by expanding the servo-voltage facilitated efficiently removing debris from the machined site. The TOPSIS method for particle swarm optimization was employed to find the optimal solutions, yielding TON = (124.239, 116.228), TOFF = (54.532, 40.781), Servo voltage = (36.216, 43.766), and Wire-Feed = (2.157, 8). These results were validated through experimentation, with minimal error percentages of 2.01%, 2.046% (MRR), and 3.86%, 1.611% (Ra) for both sets of input parameters, respectively.

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.

Experimental Investigation on the Cutting of Additively Manufactured Ti6Al4V with Wire-EDM and the Analytical Modelling of Cutting Speed and Surface Roughness

Additive manufacturing (AM) technologies for metallic materials allow for the manufacturing of high-performance components optimised in weight, geometry, and mechanical properties. However, several post-processing operations are needed after production, including removing parts from the build platform. This operation is essential and must be performed rapidly, precisely, and with a good surface finishing. This work presents an experimental investigation of the wire electric discharge machining (W-EDM) process of Ti6Al4V specimens produced by AM technologies. The influence of cutting parameters is analysed compared to the material produced by conventional technology. Models of cutting speed and surface roughness obtained by a W-EDM are inferred from the collected data. Remarkably, the results show that the manufacturing process used to produce the components plays a crucial role in defining the final surface roughness and the most significant parameters affecting the machining performance.

Optimization of the Wire Electric Discharge Machining Process of Nitinol-60 Shape Memory Alloy Using Taguchi-Pareto Design of Experiments, Grey-Wolf Analysis, and Desirability Function Analysis

The nitinol-60 shape memory alloy has been rated as the most widely utilized material in real-life industrial applications, including biomedical appliances, coupling and sealing elements, and activators, among others. However, less is known about its optimization characteristics while taking advantage to choose the best parameter in a surface integrity analysis using the wire EDM process. In this research, the authors proposed a robust Taguchi-Pareto (TP)-grey wolf optimization (GWO)-desirability function analysis (DFA) scheme that hybridizes the TP method, GWO approach, and DFA method. The point of coupling of the TP method to the GWO is the introduction of the discriminated signal-to-noise ratios contained in the selected 80-20 Pareto rule of the TP method into the objective function of the GWO, which was converted from multiple responses to a single response accommodated by the GWO. The comparative results of five outputs of the wire EDM process before and after optimization reveals the following understanding. For the CR, a gain of 398% was observed whereas for the outputs named Rz, Rt, SCD, and RLT, losses of 0.0996, 0.0875, 0.0821, and 0.0332 were recorded. This discrimination of signal-to-noise ratio based on the 80-20 rule makes the research different from previous studies, restricting the data fed into the GWO scheme to the most essential to accomplishing the TP-GWO-DFA scheme proposed. The use of the TP-GWO-DFA method is efficient given the limited volume of data required to optimize the wire EDM process parameters of nitinol.

Medical Device Hybrid Manufacturing: Translating the Coordinate System From Metal Additive Manufacturing to Subtractive Post-Processing

Abstract Additive manufacturing (AM) has transformed not only how parts can be realized but also their design. Metal additive manufacturing (MAM) has increased AM’s utility toward the manufacture of functional products. This has been seen in several industries including medical device, aerospace, and the automotive industries. The main limitation of MAM continues to be the part dimensional tolerances that can be achieved, and the respective surface finish produced. Hybrid manufacturing processes have been used to address these limitations; however, there remain challenges of how to translate the component’s coordinate system from AM to subtractive post-processes. This paper explores this topic through a medical device case study. A translatable coordinate system was produced by first designing features to serve as a datum reference frame (DRF). These features were introduced by MAM and then finalized with wire-electrical discharge machining (EDM). The produced DRF features successfully prepared the component for translation from the MAM to subtractive post-process. The completed medical device component met the expected requirements with a less than 1% difference on key part nominal dimensions. In addition, the hybrid process exhibited a potential for sustainable manufacturing with a buy-to-fly ratio of 6:1. The study demonstrated that a coordinate system can be translated effectively in hybrid manufacturing by designing part features informed by both AM and wire-EDM processes.

Data-driven model for process evaluation in wire EDM

To digitalize the wire EDM process, data-driven models are necessary for evaluating its performance. This presents a challenge due to the high volume of data and the stochastic nature of the process. In this paper, electrical parameters are measured and processed by an FPGA (field programmable gate array) system to recognize and characterize temporally and spatially resolved single discharges as either normal or abnormal. Supervised machine learning methods such as artificial neural networks (ANN) are used and models are trained with different data sets to predict the machined geometrical accuracy and cutting speed based on recorded process data.

Optimization of WEDM Parameters While Machining Biomedical Materials Using EDAS-PSO.

In the present work, an attempt has been made to study the influence of process parameters of the wire electric discharge machining (WEDM) process on the machining characteristics. The commercially pure titanium is machined by WEDM using brass wire as an electrode. The input parameters in this work were pulse on-time (Aon), pulse off-time (Aoff), servo voltage (SV) and wire tension (WT). On the other hand, dimensional accuracy (DA), average surface roughness (Ra) and maximum surface roughness (Rz) were chosen as the response parameters. The empirical relations developed for response characteristics were solved collectively using Evaluation Based on Distance from Average Solution (EDAS) and Particle Swarm Optimization (PSO). The optimized setting for minimizing the surface irregularities while machining titanium alloy on WEDM is predicted as Aon: 8 μs; Aoff: 13 μs; SV: 45 V; and WT: 8 N. Moreover, the predicted solution at the optimized parametric settings came out as DA: 95%; Ra: 3.163 μm; Rz: 22.99 μm; WL: 0.0182 g; and DR: 0.1277 mm. The validation experiments at the optimized setting showed the close agreement between predicted and experimental values. The morphological study by scanning electron microscopy (SEM) at the optimized setting revealed a significant reduction in surface defects such as micro cracks, micro cavities, globules and sub-surfaces, etc. In a nutshell, the study justified the effectiveness of EDAS-PSO in efficiently predicting the results for machining of pure titanium (Grade 2) using the WEDM process.