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

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%).

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.

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.

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.

Effect of pulse time (Ton), pause time (Toff), peak current (Ip) on MRR and surface roughness of Cu–Al–Mn ternary shape memory alloy using wire EDM

Multiple Process Parameter Optimization of WEDM on AISI304 Using Taguchi Grey Relational Analysis

The paper presents the optimisation of process parameters during wire electrode discharge machining (WEDM) of Ti6Al4V (grade 5) alloy using the response surface methodology (RSM) and desirability approach, as unique set of properties of Ti6Al4V is being increasingly utilised in various industries including biomedical and aerospace. However, due to low thermal conductivity and being highly reactive at elevated temperatures tool wear is a significant problem during conventional machining operations. Hence, WEDM may be a viable alternative in certain applications when machining titanium alloys. In this investigation the effect of WEDM process parameters such pulse on time, pulse off time, servo voltage and wire feed are evaluated for material removal rate (MRR) and surface roughness. The optimum process parameters were obtained for the selected range of experiments using a desirability approach. Confirmation experiments were conducted to validate the empirical models of MRR and surface roughness that indicated developed models are suitable for predicting the response parameters.

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.

Wire electrical discharge machining (WEDM) is a standard micro-manufacturing technology. In WEDM, surface roughness (SR), deviation dimension (DD), and machining time (MT) are critical requirements that impact machining quality and are affected by various input parameters. The workpiece often performs multiple machining steps (roughing, semi-finishing, and finishing) to achieve high accuracy. Each machining step directly affects the accuracy and machining time, and the preceding machining step influences the subsequent machining step parameters. Many input control parameters regulate WEDM’s performance. Thus, optimizing process control parameters at each step is essential to achieve optimal results. This study investigates the influence of input parameters, including pulse on time (Ton), pulse off time (Toff), and servo voltage (SV), on SR, DD, and MT when machining AISI D2 mold steel through rough, semi-finish, and finish cutting. Taguchi and Analysis of Variance (ANOVA) are applied to analyze and optimize this WEDM process. The results display that the optimal surface roughness values for rough, semi-finish, and finish-cut stages are 2.03 µm, 1.77 µm, and 0.57 µm, corresponding to the parameter set of Ton = 6 μs, Toff = 10 μs, and SV = 30 V; Ton = 3 μs, Toff = 15 μs, and SV = 60 V; and Ton = 21 μs, Toff = 45 μs, and SV = 60 V, respectively. In addition, in the finish-cut stage, the parameters for optimal DD of 0.001 mm (0.04%) are Ton = 3 μs, Toff = 15 μs, and SV = 40 V. In contrast, those values for optimal MT of 218 s are Ton = 3 μs, Toff = 30 μs, and SV = 40 V. All optimal input values are confirmed by the manufacturing mold and die parts.

In this investigation, an ultrasonic vibration and an assisted magnetic force are integrated with the electrical discharge machining (EDM) to develop a hybrid process of EDM with ultrasonic vibration and assisted magnetic force (EDMUVAMF), and then the hybrid process is adopted to explore the effects of the main machining parameters on the material removal rate (MRR), electrode wear rate (EWR), surface roughness (SR) and morphologies of the machined surface in machining SKD 61 mold steels. Moreover, the effects on expelling the debris from the machining gap are also studied via evaluating the discharge waveforms, and analyzing the morphology of the machined surface. The bottleneck correlated with large area using EDM process would be overcome when the optimal parameters of the hybrid process of EDMUVAMF is obtained. From the experimental results shown, the hybrid process of EDMUVAMF can improve the machining performance. The MRR was increased significantly and the SR was reduced to ameliorate the machining efficiency and machined surface quality. The hybrid process revealed the potential for the applications in large area via EDM technique.

Shape memory alloy (SMA), particularly those having a nickel–titanium combination, can memorize and regain original shape after heating. The superior properties of these alloys, such as better corrosion resistance, inherent shape memory effect, better wear resistance, and adequate superelasticity, as well as biocompatibility, make them a preferable alloy to be used in automotive, aerospace, actuators, robotics, medical, and many other engineering fields. Precise machining of such materials requires inputs of intellectual machining approaches, such as wire electrical discharge machining (WEDM). Machining capabilities of the process can further be enhanced by the addition of Al2O3 nanopowder in the dielectric fluid. Selected input machining process parameters include the following: pulse-on time (Ton), pulse-off time (Toff), and Al2O3 nanopowder concentration. Surface roughness (SR), material removal rate (MRR), and recast layer thickness (RLT) were identified as the response variables. In this study, Taguchi’s three levels L9 approach was used to conduct experimental trials. The analysis of variance (ANOVA) technique was implemented to reaffirm the significance and adequacy of the regression model. Al2O3 nanopowder was found to have the highest contributing effect of 76.13% contribution, Ton was found to be the highest contributing factor for SR and RLT having 91.88% and 88.3% contribution, respectively. Single-objective optimization analysis generated the lowest MRR value of 0.3228 g/min (at Ton of 90 µs, Toff of 5 µs, and powder concentration of 2 g/L), the lowest SR value of 3.13 µm, and the lowest RLT value of 10.24 (both responses at Ton of 30 µs, Toff of 25 µs, and powder concentration of 2 g/L). A specific multi-objective Teaching–Learning-Based Optimization (TLBO) algorithm was implemented to generate optimal points which highlight the non-dominant feasible solutions. The least error between predicted and actual values suggests the effectiveness of both the regression model and the TLBO algorithms. Confirmatory trials have shown an extremely close relation which shows the suitability of both the regression model and the TLBO algorithm for the machining of the nanopowder-mixed WEDM process for Nitinol SMA. A considerable reduction in surface defects owing to the addition of Al2O3 powder was observed in surface morphology analysis.

Nowadays, wire electrical discharge machining is used for sturdy materials due to an increase in demands of excellent surface finish. The aim is to study the effect of process parameters (such as pulse-on time, pulse-off time and servo voltage) on the machining performance. Selected process parameters for experimental work on steel (AISI 304); and their effect on the output characteristics such as material removal rate (MRR), and surface roughness (SR) was studied. The Taguchi technique and grey relational analysis have used to optimize the process parameters using public domain software MINITAB-17.KeywordsWEDMOrthogonal arrayAISI 304 steelAnalysis of variance

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]

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).

A novel aluminium metal matrix composite reinforced with SiC particles were prepared by liquid metallurgy route. Recent developments in composites are not only focused on the improvement of mechanical properties, but also on machinability for difficult-to-machine shapes. Electrical discharge machining (EDM) was employed to machine MMC with copper electrode. using EDM. Experiments were conducted using pulse current, gap voltage, pulse on time and pulse off time as typical process parameters. The experiment plan adopts face centered central composite design of response surface methodology. Analysis of variance was applied to investigate the influence of process parameters and their interactions viz., pulse current, gap voltage, pulse on time and pulse off time on material removal rate (MRR), electrode wear ratio (EWR) and surface roughness (SR). The objective was to identify the significant process parameters that affect the output characteristics. Further a mathematical model has been formulated by applying response surface method in order to estimate the machining characteristics such as MRR, EWR and SR.