Multi-objective Optimization of EDM Process Parameters on Al-7075-Based Hybrid MMCs

Investigating the EDM parameter effects on aluminium based metal matrix composite for high MRR

This study presents a comprehensive investigation of electrical discharge machining (EDM) applied to Al-SiC metal matrix composites (MMCs), focusing on the comparative performance of copper and graphite tool electrodes. Material removal rate (MRR) and tool wear rate (TWR) were considered as the primary response variables, while current, pulse-on time (TON), and pulse-off time (TOFF) served as the controlling parameters. A Box–Behnken experimental design was employed to construct the design matrix, and analysis of variance (ANOVA) was used to evaluate the statistical significance of the process variables. Results indicate that both current and TON exert a strong influence on MRR and TWR, whereas increasing TOFF consistently reduces TWR for both electrodes. Overall, copper electrodes demonstrated superior machinability, offering up to 18% higher MRR and 25% lower TWR compared to graphite. These findings establish copper as a more efficient tool material for EDM of Al–SiC MMCs and provide practical guidelines for optimizing EDM parameters to enhance productivity and reduce tool wear in composite machining applications.

The goal of the present work is to produce the efficient cutting of Ti-16Al-14Nb (α/β) alloy through wire-electric discharge machining and contemplate the terminologies such as machining ability, surface integrity and material removing capability. For this, the experimentation has been designed by considering three process parameters, pulse-on time ( T on ), pulse-off time ( T off ) and peak current ( I p ) at three significant levels (low, high, intermediate) in L27 order designed from design of experiments. The effect of these process variables on pulse characteristics, surface roughness ( Ra ), kerfwidth ( Kw ) and material removal rate ( MRR ) is analyzed and mathematically modeled with analysis of variance. The results state that the highest MRR (0.521, 0.51, 0.506 mm 3 /sec) and low surface roughness (8.91, 8.89, 8.68 µm) values are observed at the highest level (110 µs, 15 µs, 8 A). If the pulse duration is kept at low ( T on = 40µs, T off = 3µs), the increase in peak current from 3 A to 8 A leads to a 64.54% increase in MRR and a 52.9% increase in R a values. ANOVA results stated that T on has contributed 20.4%, 35.8% and 19.3%, and I p has contributed 43.1%, 30.5% and 46.6% influence on MRR , Kw and Ra respectively. The voltage–current characteristics studies confirmed that the strong intense pulses at a higher peak current (8 A) resulted in the increased MRR (0.521 mm 3 /sec) and Ra (8.43 µm) leading to the formation of swages and ridges. The surface integrity analysis derived from Abbott–Firestone curve agreed that the lowest level has shown better skewness ( R sk = −0.274) and at highest level showed a larger deviation in skewness ( R sk = +2.672) signifies that higher asymmetry (poor) of the surface.

Abrasive powder-mixed electrical discharge machining (APM-EDM), a hybrid manufacturing process involving the use of a dielectric fluid mixed with abrasive powder, combines the benefits of mechanical and thermal interactions. The aim of this article is to use a new approach of performance evaluation, gray relational analysis (GRA), to evaluate the effectiveness of optimizing multiple performance characteristics of APM-EDM of 6061Al/Al2O3p/20p aluminum matrix composites (AMCs). The considered process parameter includes the seven control factors namely pulse current (A), pulse ON time (μs), duty cycle (%), gap voltage (V), time interval of tool lift (s), abrasive powder concentration (g/L), abrasive particle size (μm), and a noise factor, aspect ratio (shape of tool electrode). The combination of L18 (21 × 37) orthogonal array design of experiment with GRA enables to determine the optimal parameters for multiple responses. GRA is used to obtain a single performance index, gray relational grade through gray relational coefficient to optimize the APM-EDM process with lower tool wear rate, surface roughness, and higher material removal rate. In addition, analysis of variance (ANOVA) for the GRC is also utilized.

Near-dry electrical discharge machining (NDEDM) has characteristics of high material removal rate (MRR) and low tool wear rate (TWR). NDEDM uses the mixture of air and water as a two-phase dielectric fluid, so it does not produce toxic fumes like conventional EDM which uses hydrocarbon-based oils as the dielectric fluid. Realising all these advantages of NDEDM, it was selected to machine stainless steel. The effect of pulse current (IP), pulse-on time (Ton) and lift setting on NDEDM of stainless steel was investigated. Taguchi's technique was applied to obtain an optimal setting of the process parameters. 'The higher the better' approach was used for MRR as higher MRR is desired. The measured results and subsequent analysis revealed that IP was the only significant parameter. MRR increases with an increase in IP. Also, TWR was found to be negligible in comparison of MRR.

The aim of this paper is to develop an intelligent model for prediction of input-output relationship of slotted-electrical discharge diamond grinding (S-EDDG) process using back propagation neural network (BPNN) architecture. The experiments were conducted on aluminium-silicon carbide-graphite (Al/SiC/Gr) composite workpiece using a slotted abrasive metallic wheel. The pulse current, pulse on-time, pulse off-time, wheel speed and abrasive grit number are taken as input parameters while material removal rate (MRR) and average surface roughness (Ra) are taken as performance parameters. It has been found that the developed BPNN model is capable to predict the MRR and Ra with absolute average percentage errors as 9.46% and 7.94%, respectively. It has also been found that higher MRR and better surface finish can be achieved at pulse current value as 14 A, pulse off-time as 100 μs and wheel speed as 1,300 RPM.

Experimental investigation and optimization of cobalt bonded tungsten carbide composite by hybrid AHP-TOPSIS approach

Multi-Objective optimization and experimental analysis of Electro-Discharge Machining parameters via Gray-Taguchi, TOPSIS-Taguchi and PSI-Taguchi methods

This paper presents the results of an experimental investigation on machining of micro-hole during electrochemical micromachining (ECMM) of stir-cast hybrid aluminum/(alumina + silicon carbide + carbon particulates) metal matrix composite. An ECMM set-up was developed and utilized for carrying out the experimental investigation. The supply current (Ip), supply voltage (v), pulse-on time (Ton), pulse-off time (Toff), electrolyte concentration (EC), and electrolyte flow rate (FR) were selected as process parameters. The effect of process parameters on material removal rate (MRR), electrode wear rate (EWR), surface roughness height (SR), taper cut (TC), over cut (OC), and micro-spark-affected zone (MSAZ) were analyzed through various graphs. The surface characteristics of the micro-drilled holes were analyzed using energy dispersive X-ray spectroscopy and scanning electron microscope techniques. Experimental results revealed that the MRR, EWR, TC, OC, and MSAZ increase with an increase in supply current, voltage, and pulse-on time. The optimal parametric combination for high MRR with low TC, OC, and MSAZ was found at 1.5 A supply current, 13 V supply voltage, 10 µs pulse-on time, 10 µs pulse-off time, 15 g/L electrolyte concentration, and 0.2 L/min electrolyte flow rate.

The present study is aimed at a multi-response optimization problem by applying Principal Component Analysis (PCA) combined with Taguchi method. The investigation has been carried out through a case study in Electric Discharge Machining (EDM) of D2 steel by using copper, brass and Direct Metal Laser Sintered (DMLS) electrode produced by direct metal laser sintering using Directmetal20. The research work has been carried out to evaluate the best parametric combination which could fulfill multiple responses like lower Tool Wear Rate (TWR), higher Material Removal Rate (MRR) and lower Surface Roughness (Ra). Unlike the use of single-objective optimization problem in traditional Taguchi method, a hybrid Taguchi method has been developed in combination with PCA to solve multi-objective problem. Taguchi method assumes that the quality characteristics should be uncorrelated or independent which is not always fulfilled in actual condition. PCA is applied to remove response correlation and to calculate independent (uncorrelated) quality indices known as principal components. These principal components combined with weighted principal component analysis (WPCA) are used to calculate overall quality index denoted as Multi-response Performance Index (MPI). This investigation combines WPCA and Taguchi method for forecasting optimal setting. The predicted result by this method was validated through confirmatory test proving the efficacy of the process. Out of five input process parameters considered, tool electrode has been found to be the most significant factors through the Analysis of Variance (ANOVA).

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).

Electrical discharge machining (EDM) is an extremely prominent machining process among newly developed non-traditional machining techniques for difficult-to-cut materials. Though a lot of research has been done to improve the process performance, optimal selection of process parameters for the best performance measures still remains a challenge. In this work, the performance of electrical discharge machining process when cutting hot work tool steel 2714 with copper electrodes has been investigated. Orthogonal array of L27 (3*4) based on the Taguchi experimental design is utilised to plan the experiments. Raw data is assessed by the analysis of variance (ANOVA) to find optimal conditions for response parameters. The main machining parameters such as pulse-on time, pulse-off time, discharge current and average machining voltage are chosen to determine the EDM response parameters such as material removal rate, surface roughness and gap size. Response tables and graphs are used to find the optimal parameter level in the EDM process.

In the present work, polanga oil has been used as the bio-dielectric for the machining of P20 steel by the electro-discharge machining (EDM) process with a tool made up of pure copper. As the pure polanga oil has a high viscosity (72cSt at 40 ℃), it has been chemically processed to make it suitable to be used as a dielectric fluid. The process parameters, viz. sparking current, pulse ON time (Ton) and pulse OFF time (Toff) have been used to investigate the EDM process. The material removal rate (MRR) and surface roughness (Ra) have been considered to evaluate the EDM process. The experiments were conducted using bio-dielectric polanga oil as well as the conventional dielectric fluid, i.e., kerosene. It has been observed that the MRR is improved by 0.08–0.77 times when machined with polanga oil as the dielectric. However, no significant improvement is observed in the surface finish when polanga oil is used as a dielectric.KeywordsEDMPolanga oilBio-dielectricMRRSurface roughnessP20 steel

Material wear of the tool electrode and metal workpiece in electrochemical discharge machining

The paper investigates the effect of different machining parameters such as Current (A), Pulse ON time (Ton) and Pulse OFF time (Toff) of the Die-sink EDM process on NIMONIC 80A which influence the three major machining characteristics: Material Removal Rate (MRR), Tool Wear Rate (TWR) and Radial overcut (ROC). The experiment is executed and analysed as per Box Behnken Design (BBD) of Response Surface Methodology (RSM). After conducting the experiments, Analysis of variance (ANOVA) and optimization using desirability function were performed. Regression equation was developed to show a relationship of the influences of machining parameters on characteristics. This research results show that Current and Pulse ON time are the main important parameters for MRR and the Current is a major parameter for TWR and ROC. The optimize parameters where highest value of MRR and lowest value of TWR and ROC is Current 3 A, Pulse ON time 900 µs and Pulse OFF time 51 µs with the desirability value 0.988.