EDM Drilling optimisation using stochastic techniques

Wire Electrical Discharge Machining (WEDM) is a versatile process to generate intricate and complex shapes on conductive work material with high dimensional accuracy and surface finish. Since the process is stochastic, its input parameters play critical role for achieving desired accuracy and precision of the component. Inconel 718, High-Strength-Temperature-Resistant (HSTR) material, has wide applications in the field of aerospace, automobile, mould making and medical industries. Hence, machining of Inconel 718 using WEDM is a challenging task. Also experimentation on Inconel 718 with WEDM is costly as well as time consuming process. Therefore to study the behavior of WEDM process with different process parameters for effective and efficient operation, process modeling and simulation using appropriate software is highly essential. In the present investigation, a 3-D single spark finite element thermal model for WEDM process has been developed using ANSYS software. This model has some more realistic assumptions like heat flux following Gaussian distribution and spark radius as a function of time and energy. Plasma incident region is meshed by keeping elemental size equal to one tenth of entire plasma radius, so that exact ten elements can be fitted. Identified elements were thermally loaded by applying element wise different temperatures for getting more accurate temperature distribution profile. This profile was found to be having crater shape matching with earlier Finite Element Models (FEM) available in the literature. Along with the shape, it also helps to decide the elements having temperatures greater or equal to melting point leading to estimate Material Removal Rate (MRR). Later on single spark MRR can be used to estimate multi-discharge-MRR by calculating pulse rate. Model MRR is validated with the experimental MRR which show a very good agreement, but little variation. This variation in the modeling could possibly due to assumptions like no delay in ignition, non-deposition of recast layer (100% flushing efficiency), etc. The factors like incomplete flushing of debris and inter-electrode gap arcing cause the variation in machining conditions thus reducing the actual MRR. In the present investigation, the use of dielectric is considered only for convection, but in reality, it acts as an insulator, coolant and also as debris remover. Melting and vaporization of material is the main phenomena for material removal. Dielectric fluid partially removes the molten metal because at the same time, the molten metal is under very high pressure due to plasma channel. Its adhesive property resists the material removal. It is very difficult to incorporate all real effects in the model, however the obtained results in the present study show good agreement between model MRR and experimental MRR within 10% variation.

Nitinol-shape memory alloys (SMAs) are widely preferred for applications of automobile, biomedical, aerospace, robotics, and other industrial area. Therefore, precise machining of Nitinol SMA plays a vital role in achieving better surface roughness, higher productivity and geometrical accuracy for the manufacturing of devices. Wire electric discharge machining (WEDM) has proven to be an appropriate technique for machining nitinol shape memory alloy (SMA). The present study investigated the influence of near-dry WEDM technique to reduce the environmental impact from wet WEDM. A parametric optimization was carried out with the consideration of design variables of current, pulse-on-time (Ton), and pulse-off-time (Toff) and their effect were studied on output characteristics of material removal rate (MRR), and surface roughness (SR) for near-dry WEDM of nitinol SMA. ANOVA was carried out for MRR, and SR using statistical analysis to investigate the impact of design variables on response measures. ANOVA results depicted the significance of the developed quadratic model for both MRR and SR. Current, and Ton were found to be major contributors on the response value of MRR, and SR, respectively. A teaching–learning-based optimization (TLBO) algorithm was employed to find the optimal combination of process parameters. Single-response optimization has yielded a maximum MRR of 1.114 mm3/s at Ton of 95 µs, Toff of 9 µs, current of 6 A. Least SR was obtained at Ton of 35 µs, Toff of 27 µs, current of 2 A with a predicted value of 2.81 µm. Near-dry WEDM process yielded an 8.94% reduction in MRR in comparison with wet-WEDM, while the performance of SR has been substantially improved by 41.56%. As per the obtained results from SEM micrographs, low viscosity, reduced thermal energy at IEG, and improved flushing of eroded material for air-mist mixture during NDWEDM has provided better surface morphology over the wet-WEDM process in terms of reduction in surface defects and better surface quality of nitinol SMA. Thus, for obtaining the better surface quality with reduced surface defects, near-dry WEDM process is largely suitable.

AZ91 magnesium alloys have poor machinability when conventional chip removal processes are used due to their low thermal stability and high susceptibility to softening and oxidation at elevated temperatures, which lead to excessive tool wear, poor surface quality, and deformation-induced machining challenges. This study investigated the impact of wire electrical discharge machining (WEDM) parameters on material removal rate (MRR) and surface roughness (SR) using magnesium. For this purpose, an analysis of variance (ANOVA) and Grey Relational Analysis (GRA) were performed to find the optimal settings. Findings indicate that pulse-on time (Ton) significantly affects both MRR and SR: higher Ton increases MRR but worsens SR, while shorter Ton improves SR but reduces MRR. Pulse-off time (Toff) and wire feed rate (WF) have secondary effects. Longer Toff improves surface quality but slightly reduces MRR, and lower WF improves cutting efficiency and MRR. The optimal settings identified by the Taguchi method were observed to be 123 μs Ton, 55 μs Toff, and 6 mmin−1 WF for high MRR; and 123 μs Ton, 58 μs Toff, and 4 mmin−1 WF for reduced SR. In summary, understanding how WEDM parameters affect MRR and SR allows manufacturers to achieve efficient material removal and desired surface quality.

Dry EDM (Electrical discharge machining in gas) is an innovative electrical discharge machining (EDM) method. The machining performances of dry EDM vary from that of the liquid medium due to the different physical properties of gases and liquids. The generator, a key component in an EDM machine tool, supplies electrical energy to ionize the dielectric, therefore generator mode plays important roles in EDM performance. In this work, experiments were conducted to study the machining performances of dry EDM with two generator modes: the iso-frequencial mode and the iso-pulse mode. Experimental results show that the material removal rate (MRR) and surface roughness (SR) value using the iso-frequencial mode are higher than that of the iso-pulse mode in dry EDM and reasons for the experimental phenomena were analyzed. Under the iso-pulse mode, MRR and SR present an approximately linear increase with an increase in the pulse width increase. The iso-frequencial mode could be used in the rough machining gauge and the iso-pulse mode might be used in finish machining. It is concluded that a high frequency and narrow pulse generator with a high opening voltage might be suitable for dry EDM under a certain conditions.

This study investigates the influence of various dielectric fluids on the performance of Electric Discharge Machining (EDM) in machining Waspaloy, a corrosion-resistant superalloy known for its high hardness and enhanced corrosion resistance. The purpose of this work is to fill the knowledge gap about the machining of Waspaloy using different dielectric fluids by using a Copper-5Graphite (CuGr5) composite tools. Experiments were conducted using a EDM machine with copper graphite composite electrodes and five distinct dielectric fluids, including hydrocarbon oil, kerosene, sunflower oil, used motor oil, and groundnut oil. The effects of key process parameters such as current, voltage, pulse on time, pulse off time, and gap distance on Material Removal Rate (MRR) and Surface Roughness (Ra) were evaluated. Dielectric fluid properties, including viscosity, density, thermal conductivity, and dielectric strength, were comprehensively characterized. Results revealed that MRR increased with current and pulse-on time up to a threshold, beyond which thermal damage reduced efficiency. Sunflower oil demonstrated the most favorable performance, achieving an MRR of up to 0.570mm³/min and Ra as low as 1.012μm, attributed to its high dielectric strength (40 kVA), moderate viscosity, and superior thermal conductivity. In contrast, kerosene and hydrocarbon oil, while effective in debris flushing, resulted in inconsistent spark stability and increased surface roughness at higher energy settings due to lower dielectric strength and flash points. Used motor oil and groundnut oil, though sustainable, exhibited higher surface roughness and limited MRR under elevated parameter settings owing to their higher viscosity and lower heat dissipation capabilities. A novel predictive equation for MRR and Ra, integrating dielectric fluid properties and machining parameters, was developed to enhance process optimization. Surface morphology analysis using SEM revealed characteristic features such as craters, recast layers, microcracks, and resolidified debris, highlighting the interplay between thermal, electrical, and mechanical effects during EDM. This research promotes sustainable manufacturing by assessing bio-based and waste-derived dielectric fluids as environmentally responsible alternatives to conventional EDM oil.

Wire EDM process parameter optimization for D2 steel

Engineering industry has made tremendous growth in its manufacturing fields in the last decade but still the modern machines are not utilized up to the desired level. Wire electrical discharge machining (WEDM) is one such sophisticated machining process used in industry, which is a high-speed cutting and high-precision machining to realize productivity and improved accuracy for all manufacturing parts. Optimisation of the operating parameters of WEDM will result in efficiency with regard to higher surface finish and maximum material removal rate. The present investigation of the cutting performance on die steel with different parameters such as pulse on time, pulse off time, servo voltage, wire feed, current and cutting speed were involved to determine by which using wire electric discharge machining(WEDM) process. The shape of the wire and base material dimensions of brass wire 0.25mm diameter and AISI D2 steel with 100× 100 × 10 mm were carried out. The cutting performance outputs considered in this study are material removal rate (MRR), Kerf width and surface roughness (Ra). Experiments were carried out under Taguchi's L16 orthogonal array under different conditions of parameters. An optimal combination of WEDM parameters for proper machining of AISI D2 steel to achieve better surface roughness and Materials Removal Rate (MRR) is achieved in this investigation.

The emergence of white layer in each process of Electric Discharge Machining (EDM) becomes the focus of attention of the experts. It is harder than the base metal would be advantageous to withstand friction engine components, such as the plastic molding industry. From various studies it has been reported that the increase in the value of hardness of white layer either due to migration of the electrode material and dielectric fluid to the white layer. This paper discusses the influence of electrodes Cu, CuCr and graphite as well as jatrophacurcas dielectric fluid to the surface of the material SKD 61, microhardness of the white layer, Tool Wear Rate (TWR) of Material Removal Rate (MRR), Surface roughness (Ra) and the microstructure. Initial studies conducted are the transesterification and characterization of jatrophacurcas to determine the dielectric strength and the content of metal elements. Furthermore, the EDM testing is done to dies steel of SKD 61 using the electrodes of Cu, CuCr and Graphite. The use of jatrophacurcas yield the best MRR when using Graphite electrodes followed Cu last CuCr. While the smallest TWR produced by Cu electrode, CuCr and then the highest is Graphite. The relative wear which is the ratio TWR with MRR, for the three types of electrodes is always great at a small current then decreases and then tends to be stable with the increase of electric current. Jatrophacurcas for the three types of electrodes provide higher TWR than kerosene. This means that migration of materials is beneficial because larger material, but in terms of the geometric accuracy of the product can lead to decreasing of dimensional tolerances. Vickers hardness values of white layer is achieved by CuCr electrodes, Graphite lowest. Microstructural observation results of EDM white layer using graphite electrodes produce a more uniform thickness than the white layer using Cu and CuCr electrodes. The conclusion of this study that jatrophacurcas can be used as a dielectric fluid in EDM process, because electrode wear during EDM process is relatively high compared with kerosene that is mean more profitable in the surface treatment process of dies steel of SKD 61.

When it comes to machining, manufacturers are always striving for the best possible outcomes. The improvement of the precision and productivity of material processing is the primary goal to enhance Wire Electrical Discharge Machining (EDM) parameters. Whether it's a fragile substance or one that conducts electricity, traditional approaches generally fall short. Maximizing the benefits of machining includes higher product quality, greater production efficiency, and lower production costs. Therefore, industries work hard to fine-tune the EDM settings. Within the scope of this investigation, HSS (8X200) wire EDM parameters are optimized. Finding the ideal machining settings that enhance surface roughness (SR), material removal rate (MRR), and cutting speed (CS) while maintaining economic feasibility is the goal. The Taguchi design parameter and Grey Relational Analysis (GRA) are both utilized in this research. Machining parameters such as pulse on time, pulse off time, servo feed, and servo voltage can be designed, tested, and determined with the use of the Taguchi technique. GRA analyzes experimental data and determines optimal parameter values. In order to evaluate the quality of the machining, wire-cut machines measure SR, MRR, and CS. The settings are validated using the ANOVA. In the fifth experiment, the ideal HSS (8X200) wire EDM parameters were determined. According to the findings of the study, the factors that have the largest influence on SR, MRR, and CS are pulse on time (55.43%), servo feed (17.62%), pulse off time (14.13%), and servo voltage (12.82%).

Electrical Discharge Machining (EDM) is a machining method commonly used to produce complex shapes and deep holes by eroding hard metals with an electric arc. There is a growing demand for process simulation using finite element models in order to improve the quality and efficiency of EDM, to reduce costs, to improve resource efficiency, and to facilitate its application in critical areas such as aerospace and mechanical engineering. Finite element models have greatly improved the prediction accuracy of EDM processes, simulated complex hybrid machining processes, and provided important guidance for the optimization of EDM processes. This paper systematically reviews the research progress of finite element modeling for EDM. Finite element method modeling is evaluated mainly in terms of four indicators: material removal rate, surface roughness, tool wear ratio, and recast layer thickness. Firstly, the importance and application of EDM are described, and the EDM finite element method modeling and its advantages are summarized. Then, the single-spark simulation model and the multi-spark simulation model of EDM are compared and discussed. Among the mainstream finite element models, the prediction error of the material removal rate for single-spark simulation ranges from 8.2% to 14.75%, while the prediction error of the recast layer thickness for multi-spark simulation can be as low as 1.98%. Finally, the applications of finite element modeling in EDM hybrid machining processes’ performance prediction and new material machining are summarized, and future research directions and trends in EDM finite element modeling are predicted.

Titanium alloys became multifunctional materials; initially created for the aerospace industry, they now have numerous applications in engineering and the medical field. In the present study, “Ti-6Al-7Nb” titanium alloy is machined using wire electric discharge machining (WEDM). This study investigates the effects of controllable machining variables such as Spark on-time (Ton), Spark off-time (Toff), Servo Voltage (Sv), and Peak Current (Ip) on productivity as measured by material removal rate (MRR) and surface integrity. Current research seeks to conduct a wire-electric discharge processing feasibility analysis for Ti-6Al-7Nb, a titanium alloy free from the catatoxic element vanadium. The performance of a process is evaluated based on the MRR and surface topography. After performing 81 experiments based on the Full factorial design of 3k, mathematical modeling and ANOVA analysis were conducted. The regression model is formed with a correlation coefficient (R) 0.94, which is further improved using artificial neural network (ANN) modeling. The values predicted by the ANN model are in outstanding conformity with experimental findings. The correlation coefficient (R) value in this investigation is 0.9912, which is very close to 1. It is evident that the ANN model's prediction accuracy is high. Using scanning electron microscopy, the surface conditions of the machined surface are examined. In order to evaluate the postprocess effects, the tool wear characteristics and the formation of a white layer on the surface produced are analyzed. This study examines the formation of oxide layers, craters, spherical droplets, and microcracks on the processed surface at low- and high-energy conditions. Following a thorough analysis, the discovery was made that machined surfaces contain debris, fissures, craters, and spherical particles. Additionally, to determine the elemental composition of the surface, energy-dispersive X-ray spectroscopy analysis has been carried out.

Wire Electric Discharge Machining (WEDM) of Titanium grade alloys with coated electrodes has several advantages over the traditional machining process such as increased productivity, reduction of processing cost, and improved material properties. The main objective is to create a relationship between WEDM parameters such as Pulse-on (TON), Pulse-off (TOFF), and Indicated Power (IP) with surface roughness (Ra) and Material Removal Rate (MRR). In the present work, the performance of zinc-coated brass electrodes for WEDM of Titanium Grade-7 alloy was assessed and optimized with statistical technique. ANOVA analysis is used to analysis of the MRR and Ra and validated with regression. The ANOVA analysis results indicated that TON is the highest statistically significant and followed by TOFF and IP on MRR and surface roughness. The optimum combination of higher IP(6 A) and TON time(60 μs) and lower TOFF time (12 μs) is lucrative for a higher MRR of 8.5682 mm3/min and lower surface roughness of 1.66 μm. The SEM images showed homogeneous solidification, columnar grain structure, recast layer surface, and minor surface crack density were noticed at higher cutting conditions. The predicted model and confirmation test results were close to each other with minimum error (<5%), so the model is adequate.

ABSTRACTThe current work presents a detailed exploration on real-time wire electric discharge machining (WEDM) experiments and grey relational analysis (GRA)–based multi-criteria optimization of material and machining characteristics for lowered surface roughness (Ra) and improvised material removal rate (MRR) of the newly developed magnesium/boron nitride/cathode ray tube (Mg/BN/CRT) hybrid metal matrix composites (MMCs). The composites were fabricated through powder metallurgy (PM) route by reinforcing silica-rich E-waste CRT panel glass powder crushed for different particle sizes (10, 30, and 50 µm) at various weight percentages (5%, 10%, and 15%) and with 2% boron nitride (BN). Taguchi-based orthogonal array procedure was utilized to formulate the experimental plan for WEDM considering reinforcement level and size, pulse on time (Pon), pulse off time (Poff), and wire feed (Wf) as the input process parameters. ANOVA results reveal that Pon and wt% of reinforcement has more effect on Ra and MRR than any other considered parameters. The developed mathematical model for Ra and MRR predicted values similar to that of experimental results. Multi-criteria optimization was done through GRA technique and the so recommended optimum parameter set furnishes higher MRR (22.34 mm3/min) and reduced Ra (2.87 µm).

Its unexcelled mechanical and physical properties, in addition to its biocompatibility, have made stainless steel 304 a prime candidate for a wide range of applications. Among different manufacturing techniques, electrical discharge machining (EDM) has shown high potential in processing stainless steel 304 in a controllable manner. This paper reports the results of an experimental investigation into the effect of the process parameters on the obtainable surface roughness and material removal rate of stainless steel 304, when slotted using wire EDM. A full factorial design of the experiment was followed when conducting experimental trials in which the effects of the different levels of the five process parameters; applied voltage, traverse feed, pulse-on time, pulse-off time, and current intensity were investigated. The geometry of the cut slots was characterized using the MATLAB image processing toolbox to detect the edge and precise width of the cut slot along its entire length to determine the material removal rate. In addition, the surface roughness of the side walls of the slots were characterized, and the roughness average was evaluated for the range of the process parameters being examined. The effect of the five process parameters on both responses were studied, and the results revealed that the material removal rate is significantly influenced by feed (p-value = 9.72 × 10−29), followed by current tension (p-value = 6.02 × 10−7), and voltage (p-value = 3.77 × 10−5), while the most significant parameters affecting the surface roughness are current tension (p-value = 1.89 × 10−7), followed by pulse-on time (1.602 × 10−5), and pulse-off time (0.0204). The developed regression models and associated prediction plots offer a reliable tool to predict the effect of the process parameters, and thus enable the optimizing of their effects on both responses; surface roughness and material removal rate. The results also reveal the trade-off between the effect of significant process parameters on the material removal rate and surface roughness. This points out the need for a robust multi-objective optimization technique to identify the process window for obtaining high quality surfaces while keeping the material removal rate as high as possible.

Wire Cut Electrical Discharge Machining (WEDM) is a non-conventional thermal machining process which is capable of accurately machine alloys having high hardness or part having complex shapes that are very difficult to be machined by the conventional machining processes. The WEDM finds applications in automobiles, aero–space, medical instruments, tool and die industries, etc. The input parameters considered for WEDM are pulse on time, pulse off time, flushing pressure, servo voltage, wire feed rate and wire tension. Performance of WEDM is mainly assessed by output variables such as, material removal rate (MRR), kerf width (Kw) and surface roughness (Ra) of the work piece being machined. Looking at the need of a suitable optimization model, the present work explores the feasibility of machine learning concepts to predict optimum surface roughness and kerf width simultaneously by making use of experimental data available in the literature for machining of Hastelloy C– 276 using WEDM. In most of the literatures, single objective optimization has been carried out for predicting optimum cutting parameters for WEDM. Hence, the present work presents a methodology that makes use of a machine learning algorithm namely, gradient descent method as an optimization technique to optimize both surface roughness and kerf width simultaneously (multi objective optimization) and compare the results with the existing literatures. It was observed that the input parameters such as pulse on time, pulse off time, and peak current have significant effect on both surface roughness and kerf width. The gradient descent method was successfully used for predicting the optimum values of response variables.