Wire Electro Discharge Machining Process Research Articles

Micro-feature fabrication on SS304 stainless steel using WEDM process

ABSTRACT Fabricating micro-pillars is a tough procedure as these features are highly debile. Wire Electro-Discharge Machining (WEDM) is a popular micromachining process to fabricate complex features on electrical conducting materials. SS304 stainless steel is commonly used in manufacturing of heating and cooling devices. A simple method of micro-pillars array fabrication on SS304 Stainless Steel material using WEDM was used through perpendicular crossed wire movement, keeping specified amount of step-over distance to form pillars. An analytical model for material removal rate prediction was developed using input parameters like spark duration, off time, voltage and current. Also, the influence of the process parameters was studied on depth and kerf width produced. Voltage and pulse-on time were found to be the major influencing parameters. Micro-featured surfaces fabricated for minimum kerf width and maximum feature depth were analysed using SEM and EDS along with the wettability study. The fabricated surfaces remained hydrophobic even after 100s.

Wire-EDM performance and surface integrity of Inconel 718 with unique microstructural features fabricated by laser powder bed fusion

Inconel 718 alloy is difficult to machine using conventional methods due to its physical properties. Thereby, additive manufacturing (AM) of IN718 components with near-net shapes has been extensively studied. Even though AM processes provide shape and size accuracy, there is still the need for the machining of the AM-processed components to achieve the final shape of a component. Laser powder bed fusion (LPBF) has been successfully utilized to fabricate near-net shape IN718 components; moreover, the microstructure of LPBF-IN718 was unique owing to the AM processing, resulting in differences in grain size, grain boundary characteristics, and grain orientations. Furthermore, these microstructural characteristics are expected to alter the machining performance of IN718. Therefore, this study investigated the wire electro-discharge machining (WEDM) performance of LPBF-718 samples compared to wrought IN718 while focusing on the unique microstructure characteristics of LPBF-IN718 samples (lamella, single-crystal, ploy-crystal). Three different cutting strategies (rough, semi-finish, and finish) were implemented to understand the performance of the multi-pass cutting phenomenon and its effect on the surface of IN718. For all samples, rough (single pass) cutting displayed high roughness, while finish (three passes) cutting exhibited good surface quality. Compositional analyses on the machined surface showed debris formation including Zn and Cu-containing recast material, indicating wire erosion. The surface of single-crystal LPBF-IN718 after the WEDM process was smooth owing to its large grain size and less amount of grain boundary, resulting in slow cutting speed but a good surface finish. Thus, this study, for the first time, investigated the effect of unique microstructural characteristics of LPBF-fabricated IN718 on WEDM performance and machined surface quality.

Machinability study of Al 6063-based MMCs with SiC reinforcement particles using WEDM process

ABSTRACT Metal matrix composites (MMCs) are a category of advanced materials that are challenging to process as these contain hard ceramic reinforcement particles that are distributed irregularly. Wire electro discharge machining (WEDM) is an advanced machining process that has the potential to machine hard ceramic reinforcing materials, which are electrically conductive. In this research, the WEDM process is utilized to evaluate the machinability of aluminum (Al) based MMCs. Two different fabricated Al-6063 alloy-based MMCs with silicon carbide (SiC) reinforcement particles of 220 and 600 grit size of 9 weight percentages are considered as the workpiece material. Using an experimental design with a full factorial, three process variables pulse-on time (Ton), pulse-off time (Toff), and peak current (Ip) are used to determine how they affect the on-response measures of machining time (MT), kerf width (Kf), and material removal rate (MRR). After experimentation, ANOVA analysis is applied to find out the most significant factor. ANOVA analysis reveals that Ip is the primary contributor to MT and MRR for Al/SiC (220) and Al/SiC (600) MMCs. However, Ton is the most influential factor for Kf in the case of both the MMCs. Overall evaluation criteria are also applied for the simultaneous study of response measures.

Experimental investigation and prediction modelling of slicing speed and surface roughness during wafer slicing using WEDM

To keep up with the rising demand for silicon solar cells in the photovoltaic sector, an alternative slicing method that can achieve high throughput with minimal waste is required. In recent research efforts, Wire electro-discharge machining (WEDM) has become the possible alternative method for slicing. The experimental investigation focuses on slicing monocrystalline silicon using the WEDM process with a brass wire electrode of 250 μm in diameter. The face-centered central composite design was employed for planning and conducting experiments. The investigational experiments were conducted with five different process parameters serving as inputs: peak current, wire tension, wire feed rate, pulse on and off time. The response parameter measured was the slicing speed and the surface roughness. Further, comparisons were made between different kernel functions in support vector regression (SVR) for the prediction modelling of slicing speed and surface roughness. The difficulty in prediction modelling can be attributed to the complexity of the WEDM process, which is caused by the involvement of various process parameters. The primary purpose of this work is to determine the best predictive kernel among the linear, polynomial, radial basis function (Rbf), and sigmoid kernel functions based on the experimental data. The predictive performance of different kernel functions was evaluated and compared. Grid search was used for the hyper tuning of the kernel parameters. The radial basis function produces R2 of 99.751% and 97.552%, MSE values of 0.00046 and 0.00079, RSME values of 0.0215 and 0.02814, MAE values of 0.01645 and 0.01894, and MAPE values of 1.2% and 0.9% for slicing speed and surface roughness. Support vector regression with radial basis function gives better results in comparison to other kernel functions, which concludes that support vector regression with radial basis function is well suited for the prediction of slicing speed and surface roughness.

Effect of grey relational optimization of process parameters on surface and tribological characteristics of annealed AISI P20 tool steel machined using wire EDM

AbstractTo increase the productivity and quality of the machined components, wire electro-discharge machine parameters such as cutting speed, kerf width, and surface roughness are often optimized. The Wire-electro discharge machining process generates a much higher temperature at the wire-work piece interface which can potentially change the metallurgical characteristics of the machined surface. This paper studies the changes in the tribology of surface and sub-surface of the workpiece due to the initial and optimized wire-electro discharge machine parametric setting using grey relational analysis while machining sub-cooled AISI P20 tool steel. It is concluded that the optimum parametric setting to obtain a better cutting speed, kerf width, and surface roughness using grey relational analysis, which improves the productivity and quality of the component, also produces a thicker recast layer. The wear test revealed that the surface machined with an optimized parametric setting gives higher wear resistance. Detailed scanning electron microscopy explains the characteristics of the recast layer and micro-hardness explains the surface hardenability of tool steel, which is a result of martensitic transformation through rapid cooling, formed heat affected zone, and increase in oxygen content on the surface. The wear test proved that the recast layer helps in improving the wear resistivity of tool steel. The methodology of the hardening of the surface and the sub-surface layer can strengthen the application of components.

Optimizing machining responses of homologous TiNiCu shape memory alloys using hybrid ANN-GA approach

Fabrication of shape memory alloys using wire electro discharge machining (WEDM) has gained popularity over the last few years. Most widely used machining parameters of WEDM process are pulse on time (Øon), pulse off time (Øoff), servo voltage (σ) and wire feed (ω). WEDM responses like material removal rate (MR), surface roughness (SR), kerf width (KW) and recast layer thickness (LT) have been evaluated by researchers to determine machining characteristics and are also considered for this study. These machining responses determine the quality of machining and are majorly influenced by thermal conductivity and melting temperature of the WEDM workpiece. Actuation behavior of shape memory alloys is a function of phase transformation characteristics which in turn depends on elemental composition of the selected alloys. Therefore, dissimilar machining responses of Ti50Ni40Cu10 and Ti50Ni25Cu25 have been observed even though similar machining input values were used. This study utilized artificial neural network (ANN) mapping to establish WEDM response function – which was used as fitness function to perform multi objective optimization using genetic algorithm (GA). It was found that ANN successfully predicted machining responses of selected homologous alloys and GA helped in identifying suitable input parameter values to optimize machining responses.

Multiresponse Particle Swarm Optimization of Wire-Electro-Discharge Machining Parameters of Nitinol Alloys

The conventional process of machining of nitinol alloy which possesses excess strain hardening and low thermal conductivity makes a complex process that leads to extensive wear on the tool and inadequate surface quality. Wire-electro-discharge machining (WEDM) is widely accepted for machining this alloy involving various input factors, namely, P (pulse-on-duration), Q (pulse-off-duration), C, (maximum-current), and V (voltage). Using the PSO (particle swarm optimization) method, the effect of WEDM process factors on multiresponses such as MRR (metal removal rate) and SR (surface roughness) has been investigated. ANOVA was used to create a relationship model between input factors and response characteristics, which was then optimized using response surface methods (RSM). ANOVA revealed that variables A and C are the most significant. When investigated individually, the influence of WEDM process parameters on SR and MRR is contradictory, as no response provides the best process quality. To find the optimal ideal condition for decreasing SR and maximizing MRR, the MOOPSO approach was used. P = 25.47051 μs, Q = 10.84998 μs, C = 2.026317 A, and V = 49.50757 volts were used to calculate the optimal universal solution for machining characteristics (MRRmax = 3.536791 mm3/min and SRmin = 1.822372 μm). PSO enhanced MRR and SR for every optimal combination of variables, according to the findings. Based on the findings, a wide range of optimal settings for achieving maximum MRR and minimum SR are given, depending on the product requirements.

EVALUATION OF MATERIAL REMOVAL RATE AND SURFACE ROUGHNESS IN WIRE ELECTRO-DISCHARGE MACHINING OF 10-WT.% ZrO2-REINFORCED AL ALLOY COMPOSITE

In this study, we optimize the wire electro-discharge machining (WEDM) parameters in zirconia ([Formula: see text] ceramic particulates-filled Al7075 alloy composite by using the Taguchi-combined Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. The Al7075 alloy composite was synthesized via the stir casting route by the addition of 10[Formula: see text]wt.% of ZrO2 particulates. Scanning electron microscopy (SEM) was used to analyze the microstructure of fabricated composite. Experimental work was executed as per the [Formula: see text]([Formula: see text] orthogonal array design by considering three input machining parameters like pulse current ([Formula: see text], pulse on-time ([Formula: see text] and pulse off-time ([Formula: see text], respectively. The material removal rate (MRR) and surface roughness (SR) were chosen as the output responses for machining of the developed composite. The signal-to-noise (S/N) ratio was employed to determine the optimum levels of machining parameters for the output responses. Moreover, analysis of variance (ANOVA) was used to find the significant contribution of parameters. The main effect plot explored that [Formula: see text] of 25 A, [Formula: see text] of 115[Formula: see text][Formula: see text]s and [Formula: see text] of 60[Formula: see text][Formula: see text]s were identified as the optimum levels of machining parameters which provide the maximum MRR (0.36757[Formula: see text]g/min) and minimum SR (3.826[Formula: see text][Formula: see text]m) for the proposed composite during the WEDM process. ANOVA results revealed that [Formula: see text] was the most dominant factor regarding MRR and SR followed by [Formula: see text] with contributions of 74.05% and 14.48%, respectively.

Mathematical and MATLAB based process optimization by simulated annealing algorithm for wire electrical discharge machining of EN31

The inevitability to machine hard materials and intricate shapes that are hard to be worked by traditional methods have stressed the need for non-traditional manufacturing processes specifically Wire Electro Discharge Machining (WEDM). WEDM encompasses intricate processes that include heating as well as cooling. The working parameters include pulse on time, pulse off time, table feed rate, flushing pressure, wire tension, wire velocity etc. has to be preferred suitably so that an optimized result can be attained. A single constraint has been chosen for the purpose of optimization to maximize the Metal Removal Rate (MRR). In this work a gear was machined out of EN31 material using the WEDM process and the process parameters such as pulse on time, peak current, wire feed rate and machining speed have been optimized using Simulated Annealing Algorithm and also using MATLAB based optimization. Simulated Annealing (SA) based optimization has been established to optimize the parameters in order to improve MRR. The objective function for metal removal rate is obtained based on the mathematical model.

Parametric optimization of silicon slicing using wire electro discharge machining

Abstract Silicon covers nearly about 90% of electronic sector that has made slicing a fundamental process for the manufacturing of various components such as integrated circuit, computer chips and solid-state devices. Silicon belong to the class of semi-conductor and has high brittleness due to which slicing using conventional machining processes is difficult, and many defects are to it such as chipping, scratches and large cracks. Wire Electro Discharge Machining (WEDM) is a potential process for slicing of silicon ingots. The present experimental investigation deals with the slicing of mono-crystalline silicon using WEDM process, using brass wire electrode of diameter 250 µm. The responses considered were material removal rate, surface roughness and slicing speed which were measured with the variation of the process parameters viz. open voltage(OV-V), servo voltage(SV-V), pulse on time (Ton -µs), wire tension(WT-g) and pulse off time(Toff -µs). The experimental runs were performed using Taguchi’s L27 (35) orthogonal design. The experimental results were analyzed using Taguchi based Grey Relational Analysis (TGRA) and the optimum parametric setting for the slicing was obtained at OV (111 V), Ton (0.2 µs), Toff (21 µs), WT (600 g) and SV (38 V). ANOVA analysis of the Grey Relational Grade (GRG) shows that pulse on time, wire tension and open voltage are the main contributing factor during slicing.

Experiments and optimization for the WEDM process: A trade-off analysis between surface quality and production rate

This work addressed a parameter optimization to simultaneously decrease the root mean square roughness (Rq) as well as the thickness of the white layer (TW) and improve the material removal rate (MRR) for the wire electro-discharge machining (WEDM) of a stainless steel 304 (SS304). The factors considered are the discharge current (C), the gap voltage (VO), the pulse on time (POT), and the wire drum speed (SP). The interpolative radius basic function (RBF) is applied to show the correlation between the varied factors and WEDM performances measured. The optimal selection is chosen using the multi-objective particle swarm optimization (MOPSO). Moreover, a traditional one using the response surface method (RSM) and desirability approach (DA) is adopted to compare the working efficiency of two optimization techniques. The results showed that the optimal findings of the C, POT, VO, and SP are 5.0 A, 1.0 µs, 61.0 V, and 8.0 m/min, respectively. The values of the Rq and TW are decreased by approximately 33.33% and 23.53%, respectively, while the MRR enhances 47.42% at the optimal selection, as compared to the common values used. The BRF-MOPSO can provide better performance than the RSM-DA.

Optimization of Wire EDM Process Parameters for Medical Grade Nickel Titanium Shape Memory Alloy

Abstract Nickel Titanium (NiTi) alloys are the class of smart materials classified under shape memory alloys. The traditional machining of these alloys is hard because of various inherent mechanical characteristics of these alloys. Therefore, non-traditional machining process such as wire electro discharge machining (WEDM) has been employed for machining of such alloys. The present study is focused on multi-performance characteristic simultaneous optimization of WEDM process parameters, in which three system control factors, namely, pulse on time (TON), pulse off time (TOFF) and wire feed (WF) are considered for simultaneously maximizing material removal rate (MRR), while minimizing surface roughness (SR) and tool wear rate (TWR). The simultaneous optimization is performed using Taguchi’s Quality Loss Function. Analysis of means and analysis of variance have been carried out to identify the significance level of each system control factor. The different levels of settings and the optimized setting have been analysed using scanning electron microscope images for surface morphological studies. The multi-response optimization investigations revealed that TON is the major contributing factor and optimal performance values were obtained at TON of 125μs, TOFF of 25μs and at WF of 4 m/min.

An Exploratory Investigation and Optimization of Taper Cutting Operation with Wire Electro Discharge Machining

Taper cutting, a typical application of Wire Electro Discharge Machining process is widely used to produce parts with complex geometry such as cutting and extrusion dies, which finds wide application in tool and die making industry. Taper angle becomes a vital process parameter during taper cutting and is a critical performance measure for the tool and die making industry. The present investigation is initiated with a thought to optimize the different process parameters such as pulse on time (Ton), wire tension (WT), wire feed rate (WFR), taper angle (TA) on the performance measures namely taper error (TE), material removal rate (MRR) and surface roughness (SR) during taper cutting operation utilizing WEDM. Experiments were conducted using Taguchi’s L9 (34) orthogonal array on En-31 tool steel work material using brass wire of 0.25 mm diameter, as electrode material. The parametric multi-objective optimization of the experimental results has been carried out using Taguchi based Grey Relational Analysis (TGRA). The analyzed results uncovers that the pulse on time is the utmost prompting factor for MRR and SR while the taper angle cut on the work material turn out to be a significant factor for taper error.

Multi-objective optimization of WEDM process by hybrid methodology

Wire electro-discharge machining (WEDM) is an extensively used non-conventional, thermo-electrical machining process where the generated energy from the electrical sparks that occur in the tool-workpiece interface melts and removes the metal from the workpiece. It is largely used in machining difficult-to-cut temperature-resistant alloys, which have high strength. The workpiece material is taken as AISI P20 tool steel, a special purpose tool steel that is used to make plastic dies and molds. This has high wear-resistance, toughness and hardness. Servo voltage (Sv), Peak current (Ip), Pulse-off-time (Toff) and Pulse-on-time (Ton) are taken as process parameters and Kerf Width (KW) together with Material Removal Rate (MRR) are considered as the responses. In order to attain the optimum processing condition, Fuzzy- Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), a hybrid multi-objective optimization technique is used.

Optimization of WEDM process of mould material using Kriging model to improve technological performances

Improving the technical performance of the wire electro-discharge machining (WEDM) process is an effective solution to decrease manufacturing costs. This paper addresses a multi-response optimization to simultaneously improve the cutting area rate CAR and decrease the kerf width AKW, while the average surface roughness ASR is predefined as a constraint. The processing conditions considered include the pulse-on time Ton, the current I, the voltage V and the wire speed S. A WEDM machine was adopted in conjunction with the Box–Behnken matrix to conduct experimental trials for machining of SKD61 steel. Highly nonlinear relationships between machining parameters and technological outputs were developed using the Kriging models. Finally, an archive-based micro-genetic algorithm (AMGA) was used to resolve the trade-off analysis among three responses and determine the optimal values of the processing factors. The results showed that a set of feasible solutions can be determined for the low kerf width as well as the surface roughness and the high cutting area rate. The selection of optimum parameters could help the WEDM operators to save the machining costs and time. The combination of the Kriging model and AMGA could be considered as an intelligent approach for modelling WEDM processes and predicting optimal results.

Harizko elektrohigadura bidezko ebaketa-prozesuko aldagaien azterketa altuera aldakorreko piezak ebakitzeko

Harizko elektrohigadura bidezko ebaketa-prozesua (WEDM) ezinbestekoa da gaur egun, industriak gainazal-akaberen eta dimentsio-perdoien inguruan eskatzen dituen fabrikazio-eskakizun zorrotzak betetzeko. Prozesuari buruzko ezagutzarekin batera softwarearen garapena integratzen duen ingurune batetik abiatuta, makina adimendunaren funtzionamenduaren helburua lortu ahal izango da; hots, makinak bakarrik, fidagarritasunez eta doitasunik galdu gabe funtzionatzea. Baina, horretarako, arazo larrien konponketak optimizatu behar dira; esate baterako, altuera aldakorreko piezak ebakitzeko arazoa. Lan honetan, altuera aldatzen den guneetan ebaketa-prozesuaren aldagairik esanguratsuenen portaera aztertzeko aukera ematen duen industria-sistema bat garatu da. Hau da, aurrea hartu ahal izango zaie arrazoi horrengatik gerta daitezkeen aldaketei, eta, horri esker, haria apurtzea edo prozesuaren harroketa-tasa txikitzea eragin dezaketen ezegonkortasun-arazoak saihestuko dira; hau da, produktibitateak behera egitea eragotz daiteke.

Advanced machining of TiNiCo shape memory alloys for biomedical applications

Wire electro discharge machining (WEDM) is one of the most productive non-traditional machining processes. Complex shapes can be cut through the WEDM process. In the present study, attempts have been made to study the effects of various process parameters of WEDM such as pulse on time (T on), pulse off time (T off), servo voltage (SV), wire speed (WS) and servo feed (SF) on the material removal rate (MRR) and surface roughness (SR) for machining of TiNiCo shape memory alloys, traditionally used as bone staple material. Grey-relational-analysis-based entropy measurement methods were used for formulating a hybrid combination of optimisation methods, in order to investigate the input parameters of WEDM on the comprehensive performance of a bone staple material’s SR and MRR. Experiments were carried out by using response surface design (L-33), and the input parameters were ranked based on the grey relational grade. An experimental run was conducted using the optimal combination of input parameters of WEDM, which was obtained from the analysis. T on of 125 μs, T off of 42 μs, SV of 40 V, SF of 2180 machine units and WS of 4 m/min were obtained as the best combination of input process parameters for TiNiCo alloy.

Parametric optimization of non-traditional machining processes using Taguchi method and super ranking concept

In order to achieve higher dimensional accuracy along with better surface quality, the conventional machining processes have now-a-days being replaced by non-traditional machining (NTM) processes, because of their ability to generate intricate shape geometries on various advanced engineering materials. In order to exploit their fullest machining potential, it is often recommended to operate those NTM processes at their optimal parametric settings. Several optimization tools and techniques are now available which can be effectively applied to obtain the optimal parametric conditions of those processes. In this paper, Taguchi method and super ranking concept are integrated together to present an efficient optimization technique for simultaneous optimization of three NTM processes, i.e. electro-discharge machining process, wire electro-discharge machining process and electro-chemical discharge drilling process. The derived results are validated with the help of developed regression equations, which show that the proposed approach outperforms the other popular multi-response optimization techniques. Analysis of variance is also performed to identify the most influencing control parameters for the considered NTM processes. The developed response surface plots further help the process engineers in identifying the effects of various NTM process parameters on the calculated sum of squared rank values.

Multi-Objective Optimization of Wire Electro Discharge Machining (WEDM) Process Parameters Using Grey-Fuzzy Approach

Wire electro discharge machining (WEDM) is a versatile non-traditional machining process that is extensively in use to machine the components having intricate profiles and shapes. In WEDM, it is very important to select the optimal process parameters so as to enhance the machine performance. This paper emphasizes the selection of optimal parametric combination of WEDM process while machining on EN31 steel, using grey-fuzzy logic technique. Process parameters such as servo voltage, wire tension, pulse-on-time and pulse-off-time were considered while taking into account several multi-responses such as material removal rate (MRR) and surface roughness (SR). It was found that pulse-on-time of 115 µs, pulse-off-time of 35 µs, servo voltage of 40 V and wire tension of 5 kgf results in a larger value of grey fuzzy reasoning grade (GFRG) which tends to maximize MRR and improve SR. Finally, analysis of variance (ANOVA) is applied to check the influence of each process parameters in the estimation of GFRG.

Experimental Investigation on Effects of Wire Electro Discharge Machining of Ti50Ni45Co5 Shape Memory Alloys

TiNiCo shape memory alloy is most popular shape memory alloy for biomedical applications due to their outstanding properties such as shape memory effect, pseudoelasticity and transformation temperature. Machining of such kind of alloys is very difficult through conventional machining process is very difficult because they may affect their internal properties of these alloys. However conventional machining processes give poor surface quality during the machining hence non-conventional machining processes such as (wire electro discharge machining, water jet machining and electro discharge machining etc.) are more suitable for machining of such kind of alloy. From the literature it has been found that Wire electro discharge machining (WEDM) is more suitable non-conventional machining process for such kind of alloy. Present study exhibits the effects WEDM characteristics of Ti50Ni45Co5 shape memory alloy. L-9 orthogonal array has been created by using Taguchi as a design method for machining of selected alloy and machined surface characterization has been carried out at the optimized process parameters with respect to microstructures, surface topography, microhardness, XRD analysis and residual stresses. To find the optimum setting of the input process parameters a couple of optimization techniques are used, namely principal component analysis (PCA) and Gray relational analysis (GRA) technique. 125μs pulse on time (Ton), 35μs pulse off time (Toff) and 40V servo voltage (SV) were found as an optimal setting for the higher material removal rate (MRR) with better surface roughness (SR) in the present study. Moreover, characterization of the machined surface is performed with respect to microstructures, surface topography, microhardness analysis, XRD and residual stresses. Harder surface observed near the cutting edge and TiNio3 Tio2 and CuZn were noticed on the surface of machined component through XRD analysis. However, compressive residual stress has been noticed on the machined surface during WEDM process.