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Ti_49.4Ni_50.6 (at. %) shape memory alloy (SMA) is a unique class of smart materials because of unbeatable property which given a wide variety of their applications across a broad range of fields including an orthopedic implant. It plays a very important role in the constructions of novel orthopedic implants application (like dynamic compression plate) because of lower Young’s modulus compared to other biomedical implant materials, high mechanical strength, excellent corrosion resistance and unique property like shape memory effect. Conventional machining of Ti-Ni yields poor surface finish and low dimensional accuracy of the machined components. Hence, wire electro-discharge machining (WDEM) of Ti-Ni has been performed. The purpose of this paper is to investigate the effect of variation of five process parameters, namely, a pulse-on time, pulse-off time, spark gap set voltage (SV), wire feed and wire tension on the material removal rate, surface roughness (SR), kerf width (KW) and dimensional deviation (DD), in the WDEM of Ti_49.5Ni_50.6 SMA.

The effect of machining parameters on Ti_49.4Ni_50.6 has been fully explored using WEDM with zinc coated brass wire as an electrode. In this work, L18 orthogonal array based on Taguchi method has been used to conduct a series of experiments and statically evaluate the experimental data by the use of the method of analysis of variance. Scanning electron microscope images of the machined surface, at the highest and lowest pulse-on time, have been taken to evaluate the quality of surface in terms of their SR values.

For the highest pulse-on time, it is observed that blow holes, cracks, melted droplets and craters have been formed on the machined surface with an SR of 2.744 µm, while for the lowest pulse-on time, these are not formed with an SR of 0.862 µm. It is seen that the pulse-on time is the most significant process parameter for MRR, SR and KW, while the DD is significantly affected by spark gap SV. The optimal values of the process parameters have been obtained by the method of analysis of mean and the confirmatory experiments have been carried out to validate results of optimization. Energy dispersive spectroscopy analysis of the machined surface of Ti_49.4Ni_50.6 has shown a certain amount of deposition of material on the machined surface.

This is an original paper.

In the present study, wire electro-spark machining of Titanium alloy is performed with the machining parameter such as spark-on time, spark-off time, current and servo voltage. The purpose of this study is to model surface roughness using machine learning approach for input/controllable variable. Machined surface examined using scanning electron microscope (SEM) and XRD methods.

Full factorial approach has been used to design the experiments with varying machining parameters into three-level four factors. Obtained surface roughness was modeled using machine learning methods namely Gaussian process regression (GPR) and support vector machine (SVM) methods. These methods were compared for both training and testing data with a coefficient of correlation and root mean square error basis. Machined surface examined using scanned electron microscopy and XRD for surface quality produced and check migration of tool material to workpiece material.

Machine learning algorithms has excellent scope for prediction quality response for the wire electric discharge machining (WEDM) process, resulting in saving of time and cost as it is difficult to find each time experimentally. It has been found that the proposed model with minimum computational time, provides better solution and avoids priority weightage calculation by decision-makers.

The proposed modeling provides better predication about surface produced while machining of Ti6Al7Nb using zinc-coated brass wire electrode during WEDM operation.

In this study, powder mixed electro discharge machining (PMEDM) has been performed for the machining of AISI 304 stainless steel by using the tungsten carbide electrode when silicon carbide (SiC) powder is mixed with kerosene. The purpose of this study is to find the optimal value and ascertain the effect of significant machining parameters on surface crack density (SCD) of a machined surface of AISI 304.

A face-centered central composite design-based response surface methodology has been adopted for designing this experiment.

An increase in peak current and powder concentration decreases SCD, which is the main goal of this investigation.

From this investigation, an optimal value has been achieved to minimize the SCD and prevent fatigue and corrosion resistance of the workpiece.

The purpose of this paper is to optimise the electro‐discharge machining (EDM) parameters and investigate feasibility of using direct metal laser sintering (DMLS) parts as EDM electrodes.

In this paper the effects of discharge current, pulse‐on‐time, flushing pressure are optimized for minimum tool wear rate (TWR), maximum metal removal rate (MRR) and minimum surface roughness (Ra). Taguchi‐based L₉ orthogonal array has been used for performing experiments on EDM machining of EN 24 steel using DMLS electrodes. The grey relational analysis combined with ANOVA techniques have been employed to determine the optimal level as well as their significance.

Experimental results have shown that the performance characteristics of the EDM process (TWR, MRR and surface roughness) using DMLS electrode can be quantified and controlled effectively by grey relational approach presented in the study. Current is found to be the most affective parameter in EDM machining using DMLS electrode. Excessive DMLS tool (electrode) wear was also reported, which limits the use of DMLS tool for EDM machining and it has been found out that porosity (which was about 20 per cent) was one of the primary cause.

This paper was focused on understanding the effects of important EDM parameters on three performance characteristics (TWR, MRR and surface roughness). While this study identifies that DMLS electrode wear rate is high and porosity could be one of the main cause, presently it does not cover the investigations on reducing the porosity level and its implications.

The DMLS material had shown huge potential to be used as EDM electrode. The current investigation established a structured experimental approach to understand the effects of EDM parameters on multi response characteristics. The results derived from this study helps to focus future research on two aspects including enriching the copper content and reducing the porosity level, thereby the benefits of lead time reduction in EDM electrode making could be realized.

The previous research attempts were not focussed on optimising the EDM machining process using rapid tooling electrodes. With the best of author's knowledge none of the researchers have reported these aspects especially for DMLS electrodes. Application of grey relational analysis for performance evaluation of rapid tooling‐based EDM electrodes (DMLS electrodes) appear to be completely new.

This study aims to focus on experimenting the performance of aluminum (Al) powder mixed electric discharge machining (PMEDM) of two different materials viz plastic mould die steel (AISI P20) and nickel-based super alloy (Nimonic 75). This experimental study also focuses on using three different tool materials such as copper, brass and tungsten to analyze their influence on the process output. These materials find many uses in industrial as well as aerospace applications. The performance measures considered in this work are material removal rate (MRR), tool wear rate (TWR) and surface roughness (SR).

The experimental design used in this work is based on Taguchi’s L18 orthogonal array. Besides considering work and tool material as one of the process variables, other process variables are peak current (I_p), pulse on time (T_on) and concentration of powder (C_p). The analysis of variance (ANOVA) is performed on the experimental data to determine the significant variables that influence the output.

It is found that copper produced maximum MRR and brass tool exhibited higher TWR. However, the surface finish of the machined work piece was very much improved by using the brass tool. Though the performance of tungsten tool lies between the above two tool materials, it showed very little wear during EDM with or without the addition of Al powder.

The experimental investigation of PMEDM of nickel-based super alloy (Nimonic 75) has not been attempted before. Besides that, the study on the influence of tungsten tool on the performance of EDM is also very limited.

The purpose of this paper is to investigate the performance of powder-mixed electric discharge machining (PMEDM) using three different powders which are aluminium (Al), silicon carbide (SiC) and aluminium oxide (Al₂O₃). Besides that, the influence of different tool materials was also studied in this experimental investigation. Hence, the work material selected for this purpose was AISI P20 steel and tool materials were copper, brass and tungsten. The performance measures considered in this work were material removal rate (MRR), tool wear rate and radial over cut (ROC).

The process variables considered in this study were powder types, powder concentration, tool materials, peak current and pulse on time. The experimental design, based on Taguchi’s L₂₇ orthogonal array, was adopted to conduct experiments. Significant parameters were identified by performing the analysis of variance on the experimental data.

Based on the analysis of results, it was observed that copper tool combined with Al powder produced maximum MRR (58.35 mm³/min). Similarly, the Al₂O₃ powder combined with tungsten tool has resulted least ROC (0.04865 mm). It was also observed that wear rate of tungsten tool was very low (0.0145 mm³/min).

The experimental investigation of PMEDM involving three different powders (Al, SiC and Al₂O₃) was not attempted before. Moreover, the study of influence of different tool materials (Cu, brass and W) together with the different powders on the electric discharge machining performance was very limited.

This paper aims to investigate the influence of “BroncoCut-X” (copper core-ZnCu50 coating) electrode on the machining of Ti-3Al-2.5V in view of its extensive use in aerospace and medical applications. The machining parameters are selected as Spark-off Time (ST_off), Spark-on Time (ST_on), Wire-speed (S_w), Wire-Tension (WT) and Servo-Voltage (S_v) to explore the machining outcomes. The response characteristics are measured in terms of material removal rate (MRR), average kerf width (KW) and average-surface roughness (SA).

Taguchi’s approach is used to design the experiment. The “AC Progress V2 high precision CNC-WEDM” is used to conduct the experiments with ϕ 0.25 mm diameter wire electrode. The machining performance characteristics are examined using main effect plots and analysis of variance. The grey-relation analysis and fuzzy interference system techniques have been developed to combine (called grey-fuzzy reasoning grade) the experimental response while Rao-Algorithm is used to calculate the optimal performance.

The hybrid optimization result is obtained as ST_off = 50µs, ST_on = 105µs, S_w = 7 m/min, WT = 12N and S_v=20V. Additionally, the result is compared with the firefly algorithm and improved gray-wolf optimizer to check the efficacy of the intended approach. The confirmatory test has been further conducted to verify optimization results and recorded 8.14% overall machinability enhancement. Moreover, the scanning electron microscopy analysis further demonstrated effectiveness in the WEDMed surface with a maximum 4.32 µm recast layer.

The adopted methodology helped to attain the highest machinability level. To the best of the authors’ knowledge, this work is the first investigation within the considered parametric range and adopted optimization technique for Ti-3Al-2.5V using the wire-electro discharge machining.

The purpose of this paper is to produce electric discharge machining (EDM) electrodes by using stereolithography (SLA) rapid prototyping technique and investigate the machining performance of these electrodes. In the experimental part of the study, the performance of solid copper and copper‐coated SLA (cc/SLA) electrodes are observed and compared.

The performance outputs such as material removal rate, machining depth, workpiece surface roughness and electrode front surface wear are used as metrics of comparison. The temperature measurements taken from the face of both solid copper and cc/SLA electrodes indicated that the heat build up during machining significantly accelerated the failure of cc/SLA electrodes.

The paper finds that circulating the cooling liquid inside the internal cooling channels formed with SLA technique, elongated the life of cc/SLA electrodes by dissipating the heat from the coating.

The Fluent Computational Fluid Dynamics (CFD) Software is used to numerically analyze various aspects of cooling of cc/SLA electrodes. The key findings of the study are presented in this paper.

In the present study, wire electro-discharge machining (WEDM) of Inconel 625 (In-625) is performed with the machining parameter such as spark-on time, spark-off time, wire-speed, wire tension and servo voltage. The purpose of this study is to find the most favorable machining parameter setting with respect to WEDM performance such as material removal rate (MRR) and surface roughness (RA).

Taguchi’s L27 orthogonal array has been used to design the experiments with varying machining parameters into three-level four factors. A hybrid multi-optimization technique has been purposed with grey relation analysis and fuzzy inference system integrated with teaching learning-based optimization to achieve optimum machinability (MRR and RA in present case). The obtained result has been compared with two evolutionary optimization tools via a genetic algorithm and simulated annealing.

It has been found that proposed hybrid technique taking minimum computational time, provide better solution and avoid priority weightage calculation by decision-makers. A confirmation test has been performed at single and multi-optimal parameter settings. The decision-makers have been chosen to select any single or multi-parameter setting as per the industry’s demand.

The proposed optimization technique provides better machinability of In-625 using zinc-coated brass wire electrode during WEDM operation.

Machining of curved channels using electrical discharge machining (EDM) is a novel approach. In this study, an experimental setup was designed, developed and mounted on die-sinking EDM to manufacture curve channels in AISI P20 mold steel.

The effect of specific machining parameters such as peak current, pulse on time, duty factor and lift over material removal rate (MRR) and tool wear rate (TWR) were studied. Multi-objective optimization was performed using Taguchi technique and Jaya algorithm.

The experimental results revealed current and pulse on time to have the predominant effect over material removal and tool wear diagnostic parameters with contributions of 39.67, 32.04% and 43.05, 36.52%, respectively. The improvements in material removal and tool wear as per the various optimization techniques were 35.48 and 10.91%, respectively.

Thus, Taguchi method was used for effective optimization of the machining parameters. Further, nature-based Jaya algorithm was implemented for obtaining the optimum values of TWR and MRR.