Investigation of process parameters optimization in die-sinking and wire cut EDM to improve process performance using taguchi technique

Electrical discharge machining (EDM) is a non-traditional machining process which is widely used in machining of difficult-to-machine materials. EDM process can produce complex and intrinsic shaped component made of difficult-to-machine materials, largely applied in aerospace, biomedical, die and mold making industries. To meet the required applications, the EDMed components need to possess high accuracy and excellent surface finish. In this work, EDM process is performed using Nitinol as work piece material and AlSiMg prepared by selective laser sintering (SLS) as tool electrode along with conventional copper and graphite electrodes. The SLS is a rapid prototyping (RP) method to produce complex metallic parts by additive manufacturing (AM) process. Experiments have been carried out varying different process parameters like open circuit voltage (V), discharge current (Ip), duty cycle (τ), pulse-on-time (Ton) and tool material. The surface roughness parameter like average roughness (Ra), maximum height of the profile (Rt) and average height of the profile (Rz) are measured using surface roughness measuring instrument (Talysurf). To reduce the number of experiments, design of experiment (DOE) approach like Taguchi’s L27 orthogonal array has been chosen. The surface properties of the EDM specimen are optimized by desirability function approach and the best parametric setting is reported for the EDM process. Type of tool happens to be the most significant parameter followed by interaction of tool type and duty cycle, duty cycle, discharge current and voltage. Better surface finish of EDMed specimen can be obtained with low value of voltage (V), discharge current (Ip), duty cycle (τ) and pulse on time (Ton) along with the use of AlSiMg RP electrode.

Influence of workpiece materials on aerosol emission from die sinking electrical discharge machining process

Optimization of Process Parameters in EDM with Magnetic Field Using Grey Relational Analysis with Taguchi Technique

Wear Properties of Cryogenic Treated Electrodes on Machining Of En-31

Electrical discharge machining (EDM) is a well-established machining option for manufacturing geometrically complex or hard material parts that are extremely difficult-to-machine by conventional machining processes. In this research work, investigations were carried out on the machining of AISI D2 tool steel under conventional EDM and cryogenically cooled EDM process (CCEDM). The input process parameter such as current (Ip), pulse on time (Ton) and gap voltage (V) were investigated and the output responses like electrode wear ratio, metal removal rate and surface roughness were explored. Experiments were conducted using L18 orthogonal array in EDM machine. Experimental results showed reductions in the electrode wear of 16% over CCEDM process. A grey relational analysis was used to solve the EDM process with multi performance characteristics to optimize EDM parameters.

This paper presents a unique and novel empirical study of electrical discharge machining (EDM) supported by artificial intelligence and statistical analysis. Electrical discharge machining (EDM) is a general non-traditional machining process for machining geometrically complicated parts or hard materials that are very difficult to machine by traditional machining operations. EDM creates the material removal process by using electric spark erosion. This paper experimentally investigates the process parameters of EDM on high-speed steel AISI M2 as a workpiece material with copper and brass as the electrodes. The effect of various process parameters on machining performance is investigated in this study using AI and statistical analysis where current, pulse on time and pulse off time are used for the experimental work and their effect on surface roughness (Ra) are studied. The results of the present work show that the optimum Ra levels in copper and brass electrodes are 2.16 µm and 3.43 µm, respectively, at current of 10 A, pulse on time of 100 µs, and pulse off time of 25 µs. The high levels of Ra in copper and brass electrodes are found to be 6.37 µm and 7.93 µm, respectively, at current of 42 A, pulse on time of 200 µs, and pulse off time of 4 µs. Deep learning neural networks and statistical analysis are used to evaluate the results. It has been found that there is a significant correlation between the process current and average surface roughness, and the pulsation time was not found significant. The use of deep learning neural networks has shown that AI could predict the average Ra values with an average error of about 0.39% for copper and of 0.26% for brass indicating the benefits of using AI in predicting the performance of manufacturing processes and the potential use of AI in future process modelling and applications. The drive to increase productivity and enhance quality is attracting manufacturers into adopting Industry 4.0 and artificial intelligence in their facilities to increase flexibility, reduce waste, and enhance efficiency. EDM is considered to be one of the most complex operations in manufacturing due to its high variability. Therefore, this paper suggests the use of deep learning neural networks to model the process and to predict the surface roughness outcome with limited input data. Statistical analysis was also used to test the statistical significance of each process parameter on the outcome.

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.

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.

Surface alloying has been popular in recent days for the surface modification of machining components. The present involves the practical application of micro electrical discharge machining (μ-EDM) process with powders mixed in the dielectric fluid for surface alloying applications. The experiments were carried out on Ti6Al4V sheets using tungsten tool with the use of tungsten disulphide (WS2) powders suspended in the de-ionised water as the dielectric medium. Sparks occur in between the tool electrode and the work piece gap on the local region, such that melting takes place. It results in the disassociating the dielectric fluid and the powders mixed within it and hence undergo the process of alloying. Design of experiments was used for carrying out the experiments using Taguchi method where the process parameters were taken in L9 array. The desired parametrical settings adopted was voltage, duty factor and concentration of the powder particles. The EDM alloyed samples undergo field emission scanning electron microscope (FESEM) self-equipped with EDS analysis. In addition to that the surface roughness, material removal rate and micro-hardness was measured. The work relates to the use of lubricating powders in the dielectric fluid such that the alloy formed can give some self-lubricating properties. Results showed formation of recast layer (RL) containing traces of WS2 substrate over the Ti6Al4V work piece surface hence improving the lubrication characteristics of the alloyed material.

At present, the growing needs and design requirements with significant weight savings as well as high strength-to-weight ratio as compared to conventional materials raised a growing interest towards composite materials. Metal matrix composites (MMCs) are extensively used in fields like aerospace, automobiles and army-defence applications due to their high specific strength, low coefficient of thermal expansion, wear resistance, high durability, excellent physical and mechanical properties. The filler reinforcements like SiC, TiC, Al2O3 improve the stiffness, specific strength, creep properties of MMCs, but the presence of such hard reinforcements makes MMCs difficult to machine through conventional machining processes. Wirecut electrical discharge machining (WEDM) is highly precise thermoelectrical process and is considered ideal for machining very hard materials, intricate geometries and tooling. This paper reviews the researches in WEDM of metal matrix composites and the relation between different process parameters like pulse on time, pulse off time, wire feed rate,voltage, wire tension, dielectric flow rate on the machining performances such as material removal rate (MRR), Kerf widthand surface roughness (SR). The future trends in WEDM researches were also recommended in this paper.

Electrical discharge machining (EDM) is a non-conventional process for shaping hard metals and forming deep and complex-shaped holes by spark erosion in all kinds of electroconductive materials. The choice of the electrical parameters on the EDM process depends impressively on workpiece-electrode material combination. In this research, an effort has been made to study the surface finish characteristics of the machined surface in EDM on Ti-5Al-2.5Sn titanium alloy. The microstructure of the machined surface is investigated for discharge energy and electrode materials. The peak current, pulse-on time, pulse-off time, servo-voltage and electrode material (copper, copper–tungsten and graphite) are considered as process variables. The experimental work was performed based on an experiment design (central composite design). The surface roughness (SR) increases with peak current and pulse-on time and decreases with servo-voltage. Besides, the effect of the process parameters on surface roughness depends on electrode material. At low discharge energy, copper–tungsten electrode produces the finest surface structure whilst graphite delivers worst surface characteristics. Copper–tungsten with low discharge energy (low peak current and pulse-on time) can be used to obtain better surface finish.

A study on electrical discharge machining of Mg/B4C nanocomposite synthesized by stir ultrasonication technique for analyzing the effect of tool wear rate

Parametric optimization of Electric Discharge Machining (EDM) assumes more challenge while machining newer materials for the best yield particularly used in the field of medical, marines and aerospace. In most cases, one methodology/approach does not provide satisfactory results as EDM is a very difficult process. Combination of two or more techniques has been quite successful to solve multi-objective optimization problems. This paper employs a combination of grey relational analysis (GRA) and principal component analysis (PCA) as a multi-objective optimization technique to optimize the process input parameters during EDM of Inconel 825. Experiments were designed by Taguchi’s L36 orthogonal array (OA) for seven input parameters and three performance measures. The optimal combination of EDM parameters is determined using GRA. Then, PCA is adopted to find out the weighting values corresponding to various performance measures. Consideration of two different dielectric fluids, work piece material i.e. Inconel 825 and three different electrode materials is the novelty of this work. Finally, the results are validated by running confirmatory tests. A significant enhancement in performance characteristics has been obtained and reported.

Problem statement: Electrical Discharge Machining (EDM) is the most widely used non conventional machining process for removing material from workpiece by means of a series of repeated electric discharges. Electrical Discharge milling (ED-milling) is an emerging technology where a cylindrical tool electrode follows a programmed path in order to obtain the desired shape of a part. During machining of metals by EDM process, a large amount of heat is generated for which the surface characteristics of the metals are affected. This phenomenon is unavoidable but this can be controlled by suitable selection of the process parameters. Though in most of the cases only electrical parameters are chosen but non electrical parameters also play significant role in the area of surface integrity of the machined surface. The present study emphasized to establish a comprehensive analysis of surface integrity, including the micro cracks, recast layer thickness and material migration by combining both electrical and non electrical process parameters under a wide range of machining condition. Approach: In this research, experiment was designed by using design expert software (DOE). Response Surface Methodology (RSM) was used for designing the experiment. The process parameters varied in the present study were the rotational speed of the electrode (N), Voltage (V) and feed rate (f). Stainless steel AISI 304 was chosen as work material while a copper electrode was used for EDM milling operation. The EDM milling operations were performed on EDM machine mikrotools integrated multi process machine tools DT 110. In this research, Scanning Electron Microscope (SEM) was used to investigate the effect of machining parameters on recast layer thickness, micro cracks on the machined surface as well as the percentage of material migration on the workpiece surface. Results: Electrode rotation reduces recast layer thickness of about 16.58% than that of stationary electrode with same voltage and feed rate. Moreover, it was observed that migration of C and Cu on the workpiece surface reduces 8.8 and 60% respectively when using rotary electrode instead of stationary electrode with same parameters setting. Conclusion: These results indicated that voltage and rotary speed of electrode significantly affect various criteria of surface integrity. Electrode rotation helped to reduce micro cracks, recast layer thickness as well as migration of material became less when rotary electrode is used. Besides, the combination of reasonably low voltage and high rpm reduced micro cracks, recast layer thickness and minimized the surface contamination on workpiece surface during EDM milling of stainless steel.

Engineering ceramics have been widely used in modern industry for their excellent physical and mechanical properties, and they are difficult to machine owing to their high hardness and brittleness. Electrical discharge machining (EDM) is the appropriate process for machining engineering ceramics provided they are electrically conducting. However, the electrical resistivity of the popular engineering ceramics is higher, and there has been no research on the relationship between the EDM parameters and the electrical resistivity of the engineering ceramics. This paper investigates the effects of the electrical resistivity and EDM parameters such as tool polarity, pulse interval, and electrode material, on the ZnO/Al2O3 ceramic's EDM performance, in terms of the material removal rate (MRR), electrode wear ratio (EWR), and surface roughness (SR). The results show that the electrical resistivity and the EDM parameters have the great influence on the EDM performance. The ZnO/Al2O3 ceramic with the electrical resistivity up to 3410 Ω·cm can be effectively machined by EDM with the copper electrode, the negative tool polarity, and the shorter pulse interval. Under most machining conditions, the MRR increases, and the SR decreases with the decrease of electrical resistivity. Moreover, the tool polarity, and pulse interval affect the EWR, respectively, and the electrical resistivity and electrode material have a combined effect on the EWR. Furthermore, the EDM performance of ZnO/Al2O3 ceramic with the electrical resistivity higher than 687 Ω·cm is obviously different from that with the electrical resistivity lower than 687 Ω·cm, when the electrode material changes. The microstructure character analysis of the machined ZnO/Al2O3 ceramic surface shows that the ZnO/Al2O3 ceramic is removed by melting, evaporation and thermal spalling, and the material from the working fluid and the graphite electrode can transfer to the workpiece surface during electrical discharge machining ZnO/Al2O3 ceramic.