MICROSTRUCTURAL ANALYSIS OF TITANIUM ALLOY GEAR USING WEDM PROCESS

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.

Wire electric discharge machining (WEDM) is widely used in dies, punches, aerospace and automotive sectors, since materials with high hardness, and temperature resistant, which are not possible to be cut with conventional machining methods, can be cut with high precision using the WEDM method. In this study, cutting speed (mm/min), material removal rate (mm3/min), wire consume (g), machining time (sec), finish measure (mm) and surface roughness (µm) changes as a result of cutting high hardness cold work tool steel using copper and brass wire in WEDM method were investigated. In addition, worn wire and workpiece residues on the machined surfaces were detected by SEM (Scanning Electron Microscope) and EDX (Energy Dispersive X-Ray) analyzes as well as topography and composition were examined. In the case of using copper wire instead of the brass wire, wire consumption and processing time decreased by 23.30 % and 66.29 %, respectively, while MRR increased by 50 % because the copper wire electrode has higher electrical and thermal conductivity than brass wire electrode. In addition, the average dimensional deviation of the parts cut with copper wire decreased from 27 μm to 8 μm compared to the use of brass electrodes, and more precise measurements were obtained.

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 paper compares some of the vigorous machinability characteristics of SMA-Nitinol during WEDM process using uncoated and zinc coated brass wire electrodes. A series of experiments were regulated based on Taguchi’s L[Formula: see text] orthogonal array with an objective of unveiling the benefits of using coated brass wire electrodes in comparison to uncoated counterparts. Five factors, namely pulse-on time ([Formula: see text]), discharge current ([Formula: see text]), wire tension (WT), wire speed (WS) and flushing pressure (FP), were considered, each at three different levels to scrutinize four responses, viz. surface roughness (Ra), kerf width (KW), machining time (MT) and micro-hardness ([Formula: see text]h). It was perceived that zinc-coated brass wire was more preferable to get favorable responses like Ra, KW and [Formula: see text]h when compared with brass wire counterparts. FESEM micrographs also revealed that micro and large cracks, wide craters, recast layer were more prominent on the WEDMed surface of brass wire compared to zinc-coated brass wire. Use of zinc-coated brass wire electrode significantly improves the machinability of the selected work material within the specified range of process variables.

As surveyed, many efforts have been made to model the performances of electrical discharge machining (EDM) using artificial neural network (ANN). However, the selections of the network parameters were mostly prepared in a random manner, resulting to unnecessary trials. Thus, orthogonal array (Taguchi) is employed in the procedure of network function and network architecture assortment to avoid excessive random trial experimentations. This proposed orthogonal based ANN modelling is employed on WEDM of Ti–48Al intermetallic alloys. Meanwhile modified multi objective genetic algorithm (multiGA) is used as the optimization technique. Material removal rate (MRR), surface roughness (Ra), cutting speed (Vc) and width of kerf (Dk) are the machining performances considered in this study. Five machining parameters observed from the previous researches are chosen as significant factors to the machining performances in this study, which are pulse on time, pulse off time, peak current, feed rate and servo voltage. Experimental studies are carried out to verify the machining performances suggested by this approach. Feed forward back propagation neural network (FFNN) is found to be the best network type on the selected dataset. Two hidden layer 5–6–6–4 FFNN showed the most precise and generalized network architecture with very good prediction accuracy. The proposed approach, OrthoANN, reduced ANN experimentation time by a large scale and produced viable results for machining optimization when integrated with multiGA.

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

The present work investigates on the material removal rate, surface roughness, and kerf width of the high-carbon and high-chromium steel during wire electrical discharge machining process. Experiments are planned and carried out on the base of Taguchi’s L9 orthogonal array in order to analyze the effects of pulse width time, wire feed rate, and pulse off time on response variables. This paper applied an efficient optimization technique; multi-objective optimization by ratio analysis (MOORA) technique for optimization of Wire EDM process parameters. Analysis of variance (ANOVA) is calculated in order to determine the effect of each input parameter on the overall efficiency of response variables. This method indicates that the machining performance is considerably changed by pulse width time and very less influenced by wire feed rate. It is also concluded that the MOORA method is a very effective technique for complex multi-objective optimization problems and can also be implemented without using any software. In future, the outcome of input process parameters, for instance, wire material, wire tension, flushing pressure, dielectric fluid type, etc., can be investigated to attain enhanced material removal rate, mirror surface quality, and cutting of nonconducting materials.

In this work, we have attempted to prepare a comparative machinability study of wire electrical discharge machining of different difficult-to-machine materials, viz., stainless steel (SS) 316, H21 hot work tool steel and M42 high-speed steel (HSS). The key features, which are compared during the analysis, are mainly material removal rate, average surface roughness, kerf width, wire consumption rate (WCR), recast layer (RL), elemental diffusion, surface morphology and micro-hardness of the machined surface. They are found to be greatly influenced by pulse energy. The pulse energy is calculated in terms of ‘specific discharge energy’. Apart from the discharge energy, the thermal conductivity of the material also plays an important role in the formation of RL and inclusion of foreign elements such as carbon, oxygen, copper and zinc in RL. H21 steel has been found to be more prone to thermal defects due to its high-thermal conductivity and high tensile residual stresses, whereas more re-solidification of foreign materials is observed in SS316 and M42 HSS due to their high adhesive properties and low-thermal conductivity. But, in low-energy cutting, more uniform surfaces are observed in H21 steel in comparison with other two types of steel.

ABSTRACTThe experimental investigation explores the effect of electrical discharge wire cutting (EDWC) variable parameters such as spark gap voltage, wire tension, pulse off time, wire feed rate, and pulse on time on the surface roughness, average cutting rate, and metallographic changes of Ni55.95Ti44.05 shape memory alloy (SMA). The spark gap voltage, pulse off time, and pulse on time have the significant effect on the surface roughness and average cutting rate, whereas wire tension and wire feed rate have the trifling effect. Ni55.95Ti44.05 SMA’s surface after EDWC is characterized by many discharge craters, microcracks, voids, and white layer of resolidified molten material. The elemental composition analysis of white layer using energy-dispersive spectroscopy divulges the deposition of the foreign element from the brass wire as well as the dielectric on the surface after EDWC. The machined surface as well as the wire electrode surface consists of various compounds of Ti, Ni, Zn, and Cu which have been identified by X-ray diffraction peak analysis.

Due to extensive mechanical load bearing capability under high temperature and pressure, Nickel based super alloys are widely incorporated in aerospace and aviation industries in various sections like chemical, fuselage, engine, combustor components, etc. Hastelloy-X is a Ni-based super alloy consisting mainly Ni, Cr, Fe, Mo and Co, which has good corrosion and heat resistance capacity. Since Hastelloy-X is a difficult-to-machine material, a non-conventional Wire Electric Discharge Machining is used. This work aims at machining characteristics study of WEDM of Hastelloy-X and prediction of major machining performances using Artificial Neural Network (ANN). At first, full factorial design of experiments was set using Minitab which includes four input machining parameters namely pulse-on time (T-on), pulse-off time (T-off), wire feed (WF) and servo voltage (SV); kept at three levels; high, medium and low. Total 81 experimental runs were performed. After machining on WEDM, machining performances MRR (material removal rate) and SR (surface roughness) were measured. There after the neural network is trained in nntool in MATLAB to predict the MRR and SR. The predicted model has mean absolute percentage error (MAPE) of 6.371% for MRR prediction and 5.92% for SR prediction while the MSE (Mean Square Error) was found to be 0.389 and 0.129 for MRR and SR respectively. The trained network has training, validation and testing regression coefficient (R) values of 0.9756, 0.9916 and 0.9662 respectively. And the overall R value was 0.97746. After prediction, the samples with extreme values of actual and predicted outputs were studied for other machining responses like recast layer, surface cracks and kerf width. Out-turn of this research can be utilized for machining hard to machine materials in a high precision WEDM for different applications.

ANFIS modelling of mean gap voltage variation to predict wire breakages during wire EDM of Inconel 718

Machining magnesium alloys using traditional methods pose several challenges which includes high flammability, accelerating wear, high material degradation, oxidation, and poor surface finish under machining forces. These challenges are overcome with non contact substrate type of wire electrical discharge machining (WEDM). This study carried out WEDM of AZ91D magnesium alloy by conducting various experiments based on Taguchi L18 orthogonal array. The key attributes which influence WEDM such as wire feed rate (WF), fluid pressure (FP), gap voltage (GV), pulse on period (PT ON ), and pulse off period (PT OFF ) were utilized to assess the quality characteristics like surface irregularity (R a ), material removal rate (MRR), and microhardness (MH). Additionally, a hybrid method combining gray relational analysis (GRA) with genetic algorithm (GA) were developed and applied to achieve multi objective optimization of quality characteristics. The effects of key attributes on the quality characteristics have been studied. The percentage improvement between the hybrid GRA-GA and GRA approach contributed 33.552% for MRR, 20.8619% for R a , and 8.34% for MH. The percentage error difference between the expected and experimental values varies within the range of ±5%, which implies strong correlation. The optimal setting were fluid pressure:- 1 bar; pulse-on-period:- 125 µs; pulse-off period:- 60 µs; gap voltage:- 10 V; wire feed rate:- 4 mm/min for minimum material removal rate and surface irregularity and for maximum microhardness. The optimized parameters can enhance the accuracy and quality of machining for medical implants, leading to improved biocompatibility and reduced risk of complications.

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.

Investigation and optimization of wire EDM process parameters for Al 5454 alloy machining