Optimization of Wire EDM Parameters for Machining HSS (8X200) by using Grey Relational Analysis (GRA)

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.

An investigation of material removal rate and surface roughness of squeeze casted A413 alloy on WEDM by multi response optimization using RSM

This article reports about analysis and optimization of surface roughness parameters (i.e. average roughness Ra and maximum roughness Rt) of wire electrical discharge machined fine-pitch miniature spur gears made of brass. Effects of four wire electrical discharge machining process parameters (i.e. voltage, pulse-on time, pulse-off time and wire feed rate) on the surface roughness parameters of the miniature gears were studied by conducting 29 experiments with two replicates each and designed based on Box–Behnken approach of response surface methodology. Analysis of variance study found all four input parameters to be significant. Experimentally, the surface roughness has been found to increase with higher voltage and longer pulse-on time and decrease with longer pulse-off time and higher wire feed rate. Desirability analysis was used to optimize the wire electrical discharge machining parameters, so as to minimize the Ra and Rt simultaneously. Optimum values of Ra and Rt obtained from the confirmation experiments conducted at the optimized wire electrical discharge machining parameters are superior than the values reported in the literature. Artificial neural network model has been developed for prediction of the surface roughness of the wire electrical discharge machined miniature gears. Very close agreement was found among the surface roughness values predicted by response surface methodology and artificial neural network with the corresponding experimental values.

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 aluminium based matrix composites have excellent mechanical properties to use in automobile, aerospace, aviation, and military industries. The aluminium based matrix composite possesses desired characteristics like specific strength, excellent rigidity and wear resistance. The stir casting technique was used to fabricate the nanocomposite of AA6063 alloy reinforced with 5 weight % nano aluminium oxide particulate. To improve the microhardness of as cast and aluminium based nanocomposites, multiaxial forging was performed up to three passes for grain refinement. Wire electrical discharge machining was used to machine both as cast and “AA6063/Al2O3” (zero and three multi axial forging pass). The machining characteristics of samples were statistically examined using the Taguchi technique. The material removal rate in three multiaxial forging pass nanocomposite sample increased as peak current increases. However, increasing the pulse off time resulted in the lowest material removal rate. The surface roughness was found to be higher in the nanocomposite sample in comparison to as cast. The machining on nanocomposite multiaxial forged sample has a greater impact as compared to as cast. After machining, scanning electron microscopy was used to examine the surface morphology. The presence of different elements in the castings was validated using energy dispersive X‐ray spectroscopy.

Among various machining processes, wire electrical discharge machining (WEDM) is regarded as one of the efficient and cost-effective processes in machining of metal matrix nanocomposites (MMNC). This article discusses the investigation and optimization of machining parameters like the voltage, pulse off time, pulse on time, wire feed rate and wt% nano-SiC particles in WEDM of magnesium metal matrix nanocomposites. Experiments are conducted on magnesium reinforced with nano-silicon carbide particles of diameter 50-80 nm. Machining parameters like the voltage, pulse off time, pulse on time, wire feed rate and wt% nano-SiC are considered for the investigation using a four-factor D-optimal design based on the response surface methodology. The WEDM performances of MMNCs are evaluated by employing the parameters like metal removal rate (MRR) and surface roughness (SR). The second order quadratic models are developed between WEDM parameters and responses by regression analysis. The derived mathematical models are then optimized using the multi-objective optimization technique based on desirability analysis. The investigation indicates that the predicted values of the obtained model are in accordance with the experimental values. Surface morphology and the effects of nanoparticle reinforcement in the machined surface are observed through SEM micrographs.

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.

Experimental Investigation and Optimization of WEDM process parameters for Ti50Ni48Co2 Shape Memory Alloy

Magnesium alloys, like AZ31, possess a desirable low weight and high specific strength, which make them favorable for aerospace and auto applications, yet their difficulty to machine limits their broader implementation for the industry. Electrical discharge machining (EDM) is an effective technology for machining difficult-to-machine materials, particularly when the materials are reinforced with ceramic and graphene-based fillers. This study examines the impact of reinforcement percentage (R) and different electrical discharge machining (EDM) parameters such as current (I), pulse on time (Ton) and pulse off time (Toff) on the material removal rate (MRR) and surface roughness (SR) of AZ31/B4C/GNPs composites. The combined reinforcement range varies from 2 wt.% to 4 wt.%. The Taguchi design (L27) is utilized to conduct the experiments in this study. ANOVA of the experimental data indicated that current (I) significantly affects MRR and SR, exhibiting the greatest contribution of 44.93% and 51.39% on MRR and SR, respectively, among the variables analyzed. The surface integrity properties of EDMed surfaces are examined using SEM under both higher and lower material removal rate settings. Diverse machine learning techniques, including linear regression (LR), polynomial regression (PR), Random Forest (RF), and Gradient Boost Regression (GBR), are employed to construct an efficient predictive model for outcome estimation. The built models are trained and evaluated using 80% and 20% of the total data points, respectively. Statistical measures (MSE, RMSE, and R2) are utilized to evaluate the performance of the models. Among all the developed models, GBR exhibited superior performance in predicting MRR and SR, achieving high accuracy (exceeding 92%) and lower error rates compared to the other models evaluated in this work. This work demonstrated the synergy between techniques in optimizing EDM performance for hybrid composites using a statistical design and machine learning strategies that will facilitate greater use of hybrid composites in high-precision engineering applications and advanced manufacturing sectors.

Development of material, especially metal matrix composite (aluminumbased MMC), attracts worldwide researchers due to its enhancement in wear and abrasive resistance, lightweight and high strength, low coefficient of thermal expansion. This work investigates the electrical discharge machining (EDM) of Aluminum/Boron Carbide (Al/B4C) MMC. The optimization will help the researchers or metal processing industries use the investigated optimized process parameters for efficient and effective machining parameters. Pulse on time (PON), pulse off time (POFF), a variation of boron carbide(CP), and input current (IP) were varied to measure material removal rate (MRR), wear rate of the tool (TWR), and the surface roughness (Ra). The Taguchi method is used for the analysis of the machining parameters. Grey relational grade (GRA) is employed for the optimization of machining responses and the input parameters. The influence of various EDM parameters is investigated by analysis of variance (ANOVA). The multi-response optimization was carried out by GRA (CP: 4% of B4C, PON” 25 micro-sec, POFF: 9 micro-sec, and input current of 8 amp). The optimum responses were MRR: 17.58 mm3 /sec, TWR: 0.064 mm3 /sec, and Ra: 1.20 microns. The Aluminum-based alloys or composites are widely used in aerospace applications, automobile sectors, construction, aircraft industries, etc.

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 an advanced non-conventional machining process specifically used for obtaining complex 3D shape objects in hard materials with high accuracy. The present study investigates the effect of various process parameters viz. pulse on time, pulse off time, wire feed and servo voltage on response variables such as cutting speed, material removal rate, kerf width and surface roughness on machining of high carbon high chromium steel. Taguchi's L9 orthogonal array for four factors and three levels has been used for designing the experiment. A novel technique for order preference by similarity to ideal solution (TOPSIS) approach has been applied to select the optimal level of machining parameters. Analysis of variance (ANOVA) has been conducted for investigating the effect of process parameters on overall machining performance. The effectiveness of proposed optimal condition is validated through the confirmatory test. The result of this study highlights that the parameters like pulse on time and servo voltage are significantly influencing to the machining performance.

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 electrical discharge machining (WEDM) is a non-traditional machining process, which is used for machining of difficult to machine materials, like composites and inter metallic materials. In the present paper, an attempt is made to machine hypereutectic Al-Si alloys using WEDM as these materials are widely used in automotive, aerospace and electronic fields because of its attractive properties. In the study, the WEDM machining parameters, such as pulse on time, pulse off time, wire feed rate and variation of percentage of silicon are taken as controlling factors for experimentation. In the present manuscript, an attempt is made to study the influence of percentage of silicon in the alloy system on the performance measures, such as material removal rate (MRR) and surface roughness (SR) of machining. Further, the influence of various input process parameters on the responses has also been studied. In order to optimise the said performance characteristics, two multi-objective optimisation methodologies namely grey relational analysis (GRA) and principal component analysis (PCA) are implemented. It has been observed that principal component analysis is found to perform better than grey relational analysis. [Received 9 October 2015; Revised 26 May 2016; Accepted 21 June 2016]

In the present study, the effect of process parameters on kerf width, surface roughness and material removal rate in Wire electro-discharge machining (WEDM) of biomedical alloy 316 L has been studied. The process parameters considered were gap voltage, pulse on time, pulse off time and servo feed. The machining has been performed as per Taguchi design of experiments approach using an L9 orthogonal array. The results were analyzed using analysis of variance (ANOVA). Pulse on time was the most significant parameter influencing the surface roughness. Gap voltage shows the most significant effect on the kerf width. According to grey relational analysis, the optimized parameters setting obtained is 16 μs pulse on time, 50 μs pulse off time, 50 V gap voltage and 1950 units as servo feed as per GRA value.