Multi objective optimisation of SWCNT-based electrical discharge machining process using grey relational and fuzzy logic analysis

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.

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.

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]

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

Due to the excellent characteristics of titanium alloy, was applied to aviation, marine, automotive, metallurgy, medical equipment and other fields. However, some of the characteristics make it machine difficultly, so the range of applications would be limited. In this paper, titanium alloy is cut by wire electrical discharge machining (WEDM) and study the surface finish. The model of surface roughness is established which is based on theoretical analysis. Experimental results were analyzed and optimized by the theory of signal-to-noise ratio and grey relational analysis (GRA) method. The minimal surface roughness is achieved by the optimal results .According to GRA, get the order that is the influence of electric parameters on the surface roughness.

Effect of wire materials on performance during WEDM of SS304

Nano-coating on electrodes is a new technique to enhance the machinability of difficult-to-machine alloys during Electrical Discharge Machining (EDM). The current investigation seeks to propose a comparative study of nano-particle coated and conventional electrodes during EDM process with an extended objective to machine in WEDM process. The current work seeks to propose a new experimental technique to improve the machinability of difficult-to-machine materials during EDM process by making use of nano-coated electrode considering silicon powder mixed deionised water and hence extend its application in WEDM process. The present study is focused to improve the machining performance of EDM using graphene (rGO) and carbon nano tubes (CNT) nano-coated brass electrodes. A 6–8 μm coating thickness is used for the comparative analysis. The present study considers Inconel 825 as the workpiece material. The experiments were conducted under identical operating conditions as WEDM process by using deionised water as dielectric to extend the study further for nano-coating of brass wires. Silicon powder with a concentration of 4gl was mixed into the dielectric to enhance the quality of the machining process. Grey relational analysis (GRA) and technique for order preference by similarity to ideal solution method (TOPSIS) facilitates determining the optimum input parameters. Voltage = 100 V, Peak current = 9A and Pulse on time = 100 μs was found to be the optimum condition from both GRA and TOPSIS. From the present analysis, an improvement of 16.9% (MRR), 25.59% (EWR) and 14.29% (Sa) was found in case of rGO. Whereas, CNT showed a better improvement of 30.6% (MRR), 29.50% (EWR) and 16.68% (Sa) compared to rGO.

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%).

Optimization of process parameters based on RSM and GRA method for machining of Inconel-600 by electric discharge machining

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.

9Cr18MoV stainless steel is widely employed in cutting-tool applications owing to its exceptional hardness and corrosion resistance. In this study, we systematically optimized the wire electrical discharge machining (WEDM) process parameters for 9Cr18MoV stainless steel through an L16 (44) orthogonal experimental design. The key parameters investigated include pulse width (Ton), pulse interval (Toff), peak current (IP), and wire feed speed (WS), with cutting efficiency (CE) and surface roughness (Ra) serving as the primary optimization objectives. A signal-to-noise ratio (SNR) analysis was applied to assess the effects of the individual parameters and derive single-objective optimal configurations. Subsequently, grey relational analysis (GRA) integrated with analytic hierarchy process (AHP)-based weighting was employed to establish a multi-objective optimal parameter set, which was experimentally validated. The results reveal that the optimal multi-objective performance was attained at Ton = 28 μs, Toff = 3 μs, IP = 9 A, and WS = level 3. SEM characterization confirmed that this parameter combination yields a more uniform surface morphology, with diminished oxidation and molten debris deposition, thereby significantly enhancing surface integrity. The adoption of this optimized parameter set not only ensures superior machining efficiency but also results in improved surface quality.

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).

The present work focuses to analyse the importance of turning parameters on the responses: machining time, surface roughness and material removal rate in CNC turning while machining of aluminium alloy Al-6082 T6 using tungsten carbide tool. Cutting speed, feed rate and depth of cut with three levels each have been considered in the current work as the machining parameters. The present study uses the Taguchi's DOE methodology, grey relational approach and principal component analysis (PCA) to optimise the response parameters simultaneously. Experiments have been conducted as per Taguchi's L9 orthogonal array. The experimental results were then analysed using the grey relational analysis (GRA) along with the principal component analysis (PCA). The speed and feed are observed to be statistically significant on the responses whereas the depth of cut is insignificant. Optimal levels for the parameters are determined using the grey Taguchi method and PCA. The confirmation test is carried out and the results are validated.

Cryo process is the process of treating work-pieces to cryogenic temperatures (i.e. −184 ℃) to remove residual stresses and improve wear resistance of steels. It improves the surface properties of the materials. Aerospace and tool industries use this treatment to enhance the dimensional stability and wear resistance. In this research paper, a comparison has been presented for normal and cryogenic-treated high carbon high chromium cold alloy tool (D-3) steel. The response variables namely cutting rate, metal removal rate, and surface roughness are considered for experimentations and six input process parameters like pulse width, time between two pulses, servo reference mean voltage, short pulse time, maximum feed rate, and wire mechanical tension were used for optimization on wire electric discharge machine. Grey relational analysis has been applied to calculate the grey relational coefficients and grey relational grade. ANOVA is also performed to know the significant factors for multi-response optimization.

Wire electrical discharge machining is a widely used process in manufacturing industries to machine complex profiles. The performance of any machining process is based on choosing the right combination of input parameters. Metal removal rate and surface roughness are the most important output parameters, which decide the performance of a machining process. The selection of optimal parameters in wire electrical discharge machining is difficult as it is a complex process and involves a large number of variables. The present work models the metal removal rate and the surface roughness in terms of the input variables using the response surface methodology and, consequently, the developed mathematical models are utilized for optimization. Since the influences of machining parameters on the metal removal rate and the surface roughness are opposite, the problem is formulated as a multiobjective optimization problem. Non-dominated sorting genetic algorithm is then applied to obtain the Pareto-optimal set of solutions.