Electrical Discharge Machining Process Parameters Research Articles

Investigating and Multi-Objective Optimizing WEDM Parameters for Al6061/Mg/MoS2 Composites Using BBD and NSGA-II.

This study aims to optimize the Wire Electrical Discharge Machining (EDM) process parameters for aluminum 6061 alloy reinforced with Mg and MoS2 using the Box-Behnken (BBD) design and the non-dominated sorting genetic (NSGA-II) algorithm. The objective is to enhance the machining efficiency and quality of the composite material. The Box-Behnken (BBD) design was utilized to design a set of experiments with varying levels of process parameters, comprising pulse-on time, servo volt, and current. The material removal rate and surface roughness were considered as machining responses for optimization. These responses were measured and used to develop a mathematical model. The NSGA-II, a multi-objective optimization algorithm, was then applied to search for the optimal combination of process parameters that simultaneously maximizes the material removal rate and minimizes the electrode wear rate and surface roughness. The algorithm generated and evolved a set of Pareto-optimal solutions, providing a trade-off between conflicting objectives. The results of the optimization process were analyzed to identify the optimal process parameters that lead to improved machining performance. The study revealed optimal Wire Electrical Discharge Machining (WEDM) parameters for Al6061/Mg/MoS2 composites using NSGA-II. The optimized parameters, including a pulse-on time (Ton) of 105 µs, servo voltage (SV) of 35 V, and peak current (PC) of 31 A, resulted in a Material Removal Rate (MRR) of 7.51 mm3/min and a surface roughness (SR) of 1.97 µm. This represents a 15% improvement in the MRR and a 20% reduction in the SR compared to non-optimized settings, demonstrating the efficiency of the BBD-NSGA-II approach.

Multi-objective optimization in EDM of functionally graded Fe-Al using grey relational analysis

Abstract Functionally Graded Materials (FGMs) are multipurpose materials with a specific goal of managing changes in structural, thermal, or functional qualities. They feature a spatial variation in microstructure and/or composition. The current work prepared three layers of sample with different chemical compositions of Fe-Al FGM (50 Al-50 Fe, 45 Al-55 Fe, and 40 Al-60 Fe) at. % produced by powder metallurgy, then studied the machining behavior of these samples. The literature on the machining behavior of FGM was reviewed, and the influence of electrical discharge machine (EDM) process parameters such as voltage (V), current (I), pulse-on time, and pulse-off time on performance characteristics (material removal rate (MRR), tool wear rate (TWR), and surface roughness (Ra)) was investigated. The optimal machining settings for Fe-Al FGM samples will be ascertained by use of Grey Relational Analysis (GRA), which is based on the Taguchi approach, after an experimental investigation of the L18 orthogonal array design of trials. The GRA findings verify that V 140 volts, Ip10 A, Ton 100 μs, and Toff 75 μs is the optimal combination of process parameters. It has been found that the voltage is more significantly affected than the rest of the input parameters to obtain greater material removal rate (MRR) and lower tool wear rate (EWR) and surface roughness (Ra) through the response table. According to the study, choosing the right process parameters can improve the multi-performance feature.

An experimental investigation on copper square electrode wear in electric discharge machining of Hastelloy B2

Hastelloy B2 is a high-performance nickel-molybdenum alloy and has been used in micro reactor, pump, pipe, and corrosion-resistant equipment. Square holes are required in the above application components. Although square holes are easily produced by spending more electrodes wear in Electrical Discharge Machining (EDM). Moreover, the square hole with small electrode wear achieved is a challenging task in thermal machining. Hence, in this work, the effects of EDM process parameters on electrode taper angle, electrode radius, electrode wear length and cutting time of Hastelloy B2 are studied. Taguchi based VišekriterijumskoKompromisnoRangiranje (VIKOR) method is used to find the optimum process parameters and compared with Taguchi based Grey Relational Analysis (GRA) method for validation. The results showed that current and pulse duration greatly affects the electrode wear out surface and edges. The square electrodes with sharp edges and surfaces are fully transformed into hemispherical shapes at electrode ends due to an unstable spark with diffusion of heat. Also, the VIKOR method provides an alternative multi-criteria optimization method that enhances the process performance over the grey relational analysis due to comparability, uniqueness, assessment and prediction.

Development of optimum performance model during spark-eroded processing of rheocasted Al6061/Cenosphere MMCs

ABSTRACT In the present study, an improved wettability casting route was used to prepare lightweight structural materials. Using the compo casting process, an Al6061 alloy was created with cenosphere particles at various weight percentages (2, 4, 6, and 8 wt%). The molten aluminium slurry incorporated hollow alumina silicate particulates. X-ray diffraction (XRD) analysis of the aluminium matrix composites (AMCs) confirmed the presence of cenospheres without any intermetallic components. Further processing of the metal matrix composites (MMCs) was conducted using electric discharge machining (EDM) with a circular copper electrode. The EDM process parameters were controlled to measure response variables such as material removal rate (MRR), tool wear rate (TWR), and surface roughness (SR). The effects of different combinations of machining parameters – including pulse current, pulse-on time, reinforcement percentage, and flushing pressure – on these performance characteristics were observed. Experimental trials were designed using the face-centred central composite design (CCD) of the response surface methodology (RSM). Optimal process variable conditions led to significant improvements in surface finish. Additionally, an artificial neural network (ANN) model was used to determine the best conditions for EDM processing. The ANN model demonstrated a strong correlation coefficient of 0.99 between the predicted and experimental values of MRR, TWR, and SR.

Experimental Investigation on the Impact of Graphite Electrodes Grain Size on Technological Parameters and Surface Texture of Hastelloy C-22 after Electrical Discharge Machining with Negative Polarity.

Electrical discharge machining (EDM) is a rapidly evolving method in modern industry that manufactures highly complex components. The physical properties of a tool electrode material are significant factors in determining the effectiveness of the process, as well as the characteristics of the machined surfaces. The current trend of implementing graphite tool electrodes in manufacturing processes is observed. Innovative material engineering solutions enable graphite production with miniaturized grain size. However, the correlation between the graphite electrode grain size and the mechanism of the process removal in the EDM is a challenge for its widespread implementation in the industry. This research introduces a new method to evaluate the impact of the graphite electrode grain size and machining parameters on the material removal effectiveness, relative tool wear rate, and surface roughness (Ra) of Hastelloy C-22 following EDM with negative polarity. The study utilized new graphite materials with a grain size of 1 µm (POCO AF-5) and 10 µm (POCO EDM-180). An assessment of the impact of the EDM process parameters on the technological parameters and the development of the surface roughness was carried out. Electrical discharge machining with fine-grained graphite electrodes increases process efficiency and reduces tool wear. Graphite grains detached from the tool electrode affect the stability of electrical discharges and the efficiency of the process. Based on the experimental results, mathematical models were developed, enabling the prediction of machining effects to advance state-of-the-art manufacturing processes. The obtained mathematical models can be implemented in modern industrial EDM machines as guidelines for selecting adequate machining parameters depending on the desired process efficiency, tool wear rate, and surface roughness for advanced materials.

Electrical discharge machining process investigation and optimization of parameters for machining of aluminum silicon dioxide (Al7075–SiO2) composite

AbstractThis research paper explores the impact of electrical discharge machining process parameters, including current, pulse‐on time (Ton), and pulse‐off time (Toff), on the material removal rate (MRR) and surface roughness (SR) during the fabrication of aluminum silicon dioxide (Al7075–SiO2) composites. The Al7075–SiO2 composites were produced through stir casting, incorporating 10% by weight of SiO2 reinforcements with an average particle size of 54 µm. To systematically investigate the process parameters' effects, Taguchi's L9 orthogonal array was implemented in the design of experiments. Subsequently, the experimental data underwent analysis of variance (ANOVA) for thorough analysis. The response table for MRR and SR demonstrated that the current significantly influenced both responses (MRR and SR). The ANOVA results provided valuable insights into the percentage contribution of each parameter to the responses. Through the analysis, the optimal control variable settings for achieving the highest MRR were identified as a current of 3 A, a pulse‐on time of 1500 µs, and a pulse‐off time of 1 µs. Conversely, to attain the lowest SR, the ideal control variable settings were determined to be a current of 1 A, a pulse‐on time of 500 µs, and a pulse‐off time of 1 µs. Results also suggest that the error was within admissible range of 5% and the percentage contribution of the current (70.19%) had the largest effect on MRR.

Influence of machining process of MoS2/B4C/Az31 Mg alloy composite and its tribological characteristics

The automotive, biomedical, and aerospace industries are among those with a rising need for lightweight materials with enhanced mechanical and tribological qualities. Composites based on magnesium alloys have attracted interest because of their excellent strength-to-weight ratio and promise to improve component performance. Magnesium (Mg) alloy-based composites find applications in sports and leisure equipment, aerospace, biomedical implants, and more. The research outlined here serves a critical need in the field of materials science and engineering, particularly regarding the development of advanced magnesium (Mg) alloy-based composites. In this study, we have created a new aluminum composite using the AZ31 alloy mixed with 5% boron carbide (B4C) and 5% molybdenum disulfide (MoS2) as reinforcement through a powder metallurgical technique. The magnesium alloy contains 3% aluminum and 1% zinc. Our research aims to understand the mechanical and tribological behaviors and the impact of Electrical Discharge Machining (EDM) process parameters on AZ31 magnesium alloy. We need to modify these properties for various applications. Many industrial researchers have studied the machinability of magnesium alloys using EDM. We conducted wear tests on AZ31 alloy reinforced with both B4C and MoS2 in altered quantities using a pin-on-disc setup. The outcome displays that the wear resistance of these composites is considerably better matched to other magnesium matrix composites (MMCs). We also measured various densities of the hybrid composite, including apparent density, green density, and sintered density, which were found to be 0.839, 1.495, and 1.504 g/cm3, respectively—better than other composites. In addition, the hybrid composite exhibited a substantial increase in micro hardness, reaching 22.012 HV, indicating improved wear resistance of the material. Comparatively, low density, minimum wear profile, and maximum hardness were recorded for the sample of AZ31 + 5%MoS2 + 5%B4C. The influence of EDM parameters was discussed.

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

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

Optimization on the electrical discharge machining (EDM) process parameters of aged AA7075/TiC metal matrix composites

The need to optimize the process parameters in Electrical Discharge Machining (EDM) for aged AA7075 Metal Matrix Composites (AAMMCs) is evident as it impacts various aspects such as mechanical properties, tool wear, surface finish, integrity, precision, accuracy, process stability, process consistency, and cost-effectiveness. In this study, aluminium alloy AA7075 was chosen as the matrix material because of the need to enhance its mechanical properties. Titanium Carbide (TiC) was chosen as the reinforcing material owing to its superior mechanical properties. Therefore, TiC holds the capability to improve the mechanical attributes of AA7075. The selection of the stir cast method for the manufacturing of AA7075/TiC (0, 4, 8, 12, and 16 wt.%) was based on its ease of fabrication, ability to achieve a uniform distribution of reinforcements, reduced susceptibility to oxidation and porosity, and improved control over the microstructure. This AA7075/12wt.%TiC MMC underwent an aging process at 520 °C for 180 min and was subsequently cooled within the furnace environment. The density of the aged and non-aged AA7075/TiC-based composites was determined through a density test using the Archimedes’ principle. Microhardness testing was conducted on the non-aged and aged AA7075-based MMCs employing a Vickers microhardness tester. Tensile strength and compressive strength of the aged and non-aged AA7075-based MMCs were determined by the usage of a universal testing machine (UTM) and a compression testing machine (CTM). The optimal combination of the manufactured AA7075/TiC MMCs was determined based on their mechanical properties. The most effective combination was identified as AA7075/12wt.%TiC MMC due to its superior values in hardness, tensile strength, compressive strength, and density compared to other combinations. The aging process aimed to enhance the mechanical properties without the need for additional reinforcements. EDAX and X-ray Diffraction Analysis (XRD) tests were employed to determine the weight percentage of the matrix and reinforcements and to identify the formation of precipitates in the AA7075/12wt.%TiC composites. The SEM equipment was utilized to verify the uniform distribution of titanium carbide in the matrix material AA7075. Optimization of EDM process parameters for aged AA7075/12wt.%TiC composite was carried out using Taguchi design-based Grey Relational Analysis (GRA). The selected input parameters for the optimization included the chromium concentration (g×l-1), current (amps) and pulse-on time (µs). The response parameters chosen for optimization were surface roughness (SR) and tool wear rate (TWR). The sequence of influencing EDM input parameters is chromium concentration, pulse on time and current. The optimized EDM process parameters were 8 g×l-1 chromium concentration, 5 amps current and 240 µs pulse on time and the corresponding response were 0.198 TWR and 1.56 SR.

Multi objective optimization of EDM process parameters for HCHCr steel

Electric discharge machining (EDM) or spark machining involves controlled erosion of electrically conducting materials by an interrupted, repetitive electric spark creating discharge between the cathode- tool and anode- work-piece, separated by a dielectric fluid medium. The objective of this work is to study the influence of machining characteristics during EDM of high carbon and high chromium steel (HCHCr) with copper electrode. EDM of HCHCr steel is conducted according to Taguchi orthogonal array (OA) design of experiment to evaluate the material removal rate (MRR), tool wear rate (TWR) and surface finish (Ra).The experiments are conducted on die-sinking EDM with varying current, voltage, and pulse-on-time to measure the above responses. Finally, the multi objective optimization technique namely principal component analysis (PCA) is used for optimizing the process parameters. The experimental results indicate the optimum parameters as: current 9 Amps, pulse-on time 15 µs and pulse-off time 10 µs.

Unravelling the analysis of electrical discharge machining process parameters, microstructural morphology, surface integrity, recast layer formation, and material properties: A comparative study of aluminum, brass, and Inconel 617 materials

Inadequate research regarding how material characteristics affect vital yield parameters in EDM, particularly for aluminium, brass, and Inconel 617. An limited study has examined how EDM process parameters like peak current (Ip), gap voltage, and pulse on time (Ton) affect recast layer thickness, tool wear rate, surface roughness, crack density, globule formation, material removal rate, and crater formation. The interaction between thermal conductivity, microcracks, and recast layers in EDM is uncertain. Material characteristics' implications on XRD patterns and globule formation during EDM machining were not extensively investigated. EDM process parameters and their impacts on response variables such tool wear rate (TWR), material removal rate (MRR), surface roughness, and globule formation for diverse materials have received minimal analysis. In addition, non-ferrous workpiece materials are integral to the electrical discharge machining process. This study delves into how the physical properties of materials influence key yield parameters, including tool wear rate, surface roughness, crack density, globules, material removal rate, and crater formation. The investigation covers Inconel 617, brass, and aluminum, revealing peak current (Ip) as the most influential factor for highly thermally conductive materials when compared to gap voltage and pulse on time (Ton). Notably, Inconel 617 exhibits longer cracks, while aluminum displays smaller ones, and deeper cracks are found on the surface of aluminum, contrasting with the broader craters observed on Inconel 617. Recast layer thickness varies, ranging from 8.3 μm for brass to 13.8 μm for aluminum. The study validates response values extensively against experimental data and highlights brass's superior surface finish. It further uncovers that plasma-generated craters are predominantly semispherical, with the plasma's diameter expanding faster than the crater's size. Sub-surface re-solidification emerges as a source of stress and micro-cracks, which diminish material's fatigue strength. Notably, aluminum exhibits a greater number of elegant microcracks on the machined surface, while brass and Inconel 617 have fewer. Recast layer thickness measurements indicate approximately 13.8 μm for aluminum, 8.3 μm for brass, and 9 μm for Inconel 617. Additionally, the thermal erosion process enhances the microhardness of the machining zone's subsurface due to the formation of oxides and carbides and the re-solidification of particles. Different material characteristics are revealed through X-ray diffraction (XRD) patterns, while globules, small and spherical, weakly adhere to the machining zone's subsurface. In particular, aluminum machined surfaces feature fewer globules, while brass surfaces exhibit a higher quantity, attributed to their varying thermal conductivity.

Electric Discharge Machining of AA7075/SiCP/B4CP Hybrid Composites machined with Al2O3 Mixed Used Engine Oil Dielectric fluid

Background: Machining hybrid composites through conventional machining technique was a challenging task as it produces excessive tool wear and exhibits poor surface roughness. In this research work, an attempt was made to electric discharge machining of AA7075/SiCP/B4CP Hybrid Composites produced through the stir casting route. Objective: The used engine oil was as the dielectric fluid with the objective of obtaining wealth from waste. The experiments were performed by varying distinct Electric Discharge Machining (EDM) process parameters with the goal of obtaining a high Material Removal Rate (MRR), low Tool Wear Rate (TWR) and least Surface Roughness (Ra). The experimental runs were optimized using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) optimization techniques. Methods: Experimental runs were designed using the L20 Taguchi orthogonal array in which Powder concentration, Current and Pulse on time were varied for three different levels, and two various dielectric fluids were used for investigation. The characteristics of the Used Engine Oil (UEO) were assessed to find its feasibility as a dielectric fluid. Results: In comparison to EDM oil, the specimen machined in UEO dielectric medium has a somewhat greater MRR. Regardless of the kind of dielectric fluid employed, adding Al2O3 particles increases the MRR because of the bridging effect. Due to its high thermal conductivity, UEO oil produced electrodes with a TWR that was greater than that of EDM oil. The TWR decreases with the addition of Al2O3 particles due to an increase in the spark gap. In comparison to EDM oil, the specimen machined under UEO displays a lower Ra value. Ra decreases with the inclusion of Al2O3 particles due to the thorough flushing of machining waste. Conclusion: The specimen machined under Al2O3 mixed UEO dielectric medium, with the process parameters tuned at 3A current and 20µs Ton, offers better machining performance and was recommended for EDM sector.

OPTIMIZATION OF MACHINING CHARACTERISTICS OF TITANIUM-BASED BIOMATERIALS: APPROACH TO OPTIMIZE SURFACE INTEGRITY FOR IMPLANTS APPLICATIONS

Nowadays, there has been continuous development of metallic biomaterials to meet special needs in the manufacturing of biomedical implants, units and systems so as to function well in the required environment. Developed biomaterials which possess exceptional properties in terms of biocompatibility and biomechanical compatibility require precision processing and machining to obtain the desired dimensional tolerances. Electrical discharge machining (EDM) is the noncontact or nontraditional process of machining that suits the precision machining of biomaterials. In this work, an effort was made to optimize the EDM parameters during machining of titanium-based biomaterials Ti-6AL-4V, so that the multi-objective responses could be obtained. The response surface method was used in designing the experiment, while the grey relational method was used to analyze the effect of multiple objectives into a single unit. The electrical parameters that were considered in this study include peak current, gap voltage, pulse turn-on and duty cycle. These parameters were set within the acceptable limits of the equipment. Three responses were studied, which are tool wear rates (TWRs), material removal rate (MRR) and surface roughness (SR). Using the signal-to-noise ratio and ANOVA optimum tool/electrode wear rate (TWR) is obtained at [Formula: see text][Formula: see text]g/min with process parameters [Formula: see text] A, [Formula: see text][Formula: see text]V, [Formula: see text][Formula: see text][Formula: see text]s, [Formula: see text]%. Optimum values of material removal rate (MRR) are obtained as 0.01035[Formula: see text]g/min with process parameters [Formula: see text] A, [Formula: see text][Formula: see text]V, [Formula: see text][Formula: see text][Formula: see text]s, [Formula: see text]%. Optimum SR is observed as 2.258[Formula: see text][Formula: see text]m with EDM process parameters [Formula: see text] A, [Formula: see text][Formula: see text]V, [Formula: see text][Formula: see text][Formula: see text]s, [Formula: see text]%. Surface characteristics are verified with SEM micrographs. Whereas, grey relation analysis predicted the multi-objective optimum response characteristics. Based on the grey relation grade, experiment number 7 ([Formula: see text][Formula: see text]A, [Formula: see text][Formula: see text]V, [Formula: see text][Formula: see text][Formula: see text]s, [Formula: see text]%) secured the first rank among the experiments/trails.

Parameters Optimization of Electrical Discharge Machining Process Using Swarm Intelligence: A Review

Electrical discharge machining (EDM) can use soft tool electrodes to process hard workpieces to achieve “soft against hard”, because it directly uses electrical energy and thermal energy to remove metal materials. Then, it can generate complex features on harder materials and meet the requirements of excellent surface quality. Since EDM involves many process parameters, including electrical parameters, non-electrical parameters, and materials properties, it is essential to optimize its process parameters to obtain good performance. In this direction, the application of the swarm intelligence (SI) technique has become popular. In this paper, the existing literature is comprehensively reviewed, and the application of the SI technique in the optimization of EDM process parameters is summarized. Sinker-EDM (SEDM), wire-EDM (WEDM), and micro-EDM (MEDM) with various hybrid techniques are among the EDM methods considered in this study because of their broad adoption in industrial sections. The fundamental nature of all review articles will assist engineers/workers in determining the process parameters and processing performance, the SI algorithm, and the optimal technique by which to obtain the desired process parameters. In addition, discussions from the perspectives of the similarity, individuality, and complementarity of various SI algorithms are proposed, and necessary outlooks are predicted, which provides references for the high performance of the EDM processes in the future.

Elucidating Powder-Mixed Electric Discharge Machining Process, Applicability, Trends and Futuristic Perspectives

Since the inception of electric discharge machining (EDM), it has facilitated the production industries, for instance, die & mold, automotive, aerospace, etc., by providing an effective solution for machining hard-to-cut materials and intricate geometries. However, achieving high machining rates and a fine surface finish is an inherent issue with the traditional EDM process. The emergence of the powder mixed electric discharge machining (PMEDM) process has not only provided the opportunity for enhancing productivity and surface finish but also opened a window for its potential application in surface modification/coating of biomaterials. The process incorporates simultaneous machining and coating of bioimplants, i.e., lacking in the already available chemical and physical coating methods while requiring costly post-treatment procedures. This study comprehends the influence of powder characteristics and EDM process parameters on the performance parameters. The impact of tool electrodes and additive powders on the machined and coated surface of commonly used biomaterials. Furthermore, the study depicts the most frequently used methods for optimizing the PMEDM process, future research directions, challenges, and research trends over the past decade.

ANN and RSM based predictive model development and EDM process parameters optimization on AISI 304 stainless steel

The current research was conducted in an Electrical Discharge Machining (EDM) on AISI 304 stainless steel. The experimental study was carried out using full factorial design methods, with three input parameters chosen for this experiment: pulse time (T), discharge current (C), and gap voltage (V), and the responses being material removal rate (MRR) and surface roughness (SR). Artificial neural network (ANN) and response surface methodology (RSM) models are the two different types of predicting models for MRR and SR that have been established. It has been found that both models have good accuracy, albeit ANN predicted model is more accurate. The maximum percentage of error in the MRR prediction was 6.16 for the ANN model and 10.45 for the RSM model, respectively Also, the maximum percentage of error in the SR prediction was 5.32 and 16.18 for the ANN and RSM models, respectively. The purpose of this paper is to increase the MRR as well as decrease the SR and equivalent optimum machining parameters which is a very important aspect of the present research. For this purpose, a multi objective function for MRR and SR has been developed based on the RSM method and finally, it is optimised. It has been found that 9 A, 200 s, and 45 V, respectively, are the optimal machining parameters for obtaining discharge current (C), pulse on time (T), and gap voltage (V). Future attempts may use more advanced artificial intelligence methods like ANN-GA, ANN-PSO, etc.

Process parameter of EDM to optimize material Removal Rate using Box Behnken’s design

In high-precision machining, Electrical Discharge Machining (EDM), product is formed with more hardness materials where the thermoelectric energy sandwiched between the electrode and the work piece serves as a basis for the EDM mechanism. Peak current, pulse on time, dielectric pressure, electrode polarity, work piece material, electrode material, flushing pressure, flushing direction, and flushing method are just a few of the many parameters that can be changed during the process. The focus of this research is to identify the EDM process's parameters in order to enhance productivity as measured by EN-31 tool wear rate and material removal rate (MRR). Peak current, pulse on time, dielectric pressure, and tool size were the variable parameters, each with three levels. Three 3-level variables were applied using RSM orthogonal arrays L29. As per the equation developed, the peak current inputs have a significant influence in determining the MRR for the range of experimental values under consideration, but for the TWR, the greatest impact is produced by the pulse on time, followed by the size of the tool. According to the optimization results, the discharge current of 34A, the pulse on time of 1000 μs, the dielectric compression of 1.2 kg/sq.cm, and the tool size of 16 mm are optimum for MRR.

Fatigue analysis of electro discharge machined Nitinol 60

Nitinol 60 (NiTi60) is a shape memory alloy (SMA) where the atomic percentage of nickel is slightly higher than titanium. It advantages the material to have higher hardness and better sensitivity to phase transformation. One of the machining techniques that is preferred to cut hard material is electrical discharge machining (EDM). This study aims to examine the fatigue characteristics of a NiTi60 alloy that has undergone electro-discharge machining and examine the impact of machining parameters on microstructural changes. In this investigation first, the influence of EDM process parameters such as pulse current, voltage, pulse on time, and pulse off time on surface integrity aspects was investigated. Second, the impact of these process variables on fatigue strength was examined. The surface integrity parameters of EDM-machined specimens, such as microcracks, surface crack density (SCD), white layer thickness (WLT), and residual stress formation have been examined by various characterization techniques. The obtained results show that pulse current and voltage are dominating factors affecting SCD. The thickness of the white layer seems to be increased with the rise in the pulse current and pulse on time, and tensile kinds of residual stresses are present in the WLT region, whose magnitude is dependent on process parameters. The fatigue tests were performed using a servo hydraulic testing machine at a frequency of 20[Formula: see text]Hz for 106 number of cycles. The fatigue crack initiation, propagation, and effects of process parameters have been examined. It has been found that an increase in pulse current and voltage leads to the generation of microvoids in the WLT region and thereby causes fatigue cracks to take birth. Later on, a correlation between WLT and SCD was observed by implementing an artificial neural network (ANN) model. The accuracy of ANN model prediction is reported to be high, where WLT and SCD have a 0.98 observed correlation coefficient.

Influence of EDM Process Parameters on the Surface Finish of Alnico Alloys.

This article deals with electrical discharge machining (EDM) of an alnico alloy, focusing on how key process parameters affect the surface finish. The experiments were conducted using a BP93L EDM machine. The Box–Behnken design was employed to study the effects of three factors, i.e., spark current, pulse-on time, and pulse-off time, each at three levels, on the surface quality. A specially designed system was employed to increase the effectiveness of the machining process by imparting an additional rotary motion to the tool and an additional rotary motion to the workpiece. The aim was to efficiently remove the eroded metal particles and create a surface with smaller craters. The workpiece surface roughness was measured with a Talysurf CCI lite non-contact profiler. During this precision machining process, the arithmetical mean height (Sa) was less than 1 µm. The surface quality was examined also using scanning electron microscopy (SEM) and optical microscopy (OM). The experimental data were analyzed by means of Statistica to determine and graphically represent the relationships between the input and output parameters.

Experimental Investigation and Optimization of Material Removal Rate and Tool Wear in the Machining of Aluminum-Boron Carbide (Al-B4C) Nanocomposite Using EDM Process

Electrical discharge machining (EDM) is a cost-effective unconventional machining method used for machining any composites materials. EDM is based on the thermoelectric energy between the electrode and workpiece. In this work, boron carbide particles of 50 nm (6 wt.%) are reinforced with aluminum 7075 (94 wt.%) prepared using stir casting method. The stir casting process is carried out at speed of 700–800 rev/min. The fabricated aluminum-boron carbide nanometal matrix composites are used as workpiece (anode); copper electrode is used as tool (cathode). This work investigates the influence of EDM process parameters such as current (I), pulse on-time (ton), and tool diameter (d) during machining of Al-B4C composite on metal removal rate (MRR) and tool wear rate (TWR). The design of experimental plan is executed by Taguchi approach, and the responses of each parameter are influenced by analysis of variances (ANOVA). Response table for average value of MRR and TWR shows that the current is the significant parameter affecting MRR and TWR. From this work, it was observed that material removal rate increased with increasing the current, and the tool wear rate decreases.