Comparison of optimization techniques for MRR and surface roughness in wire EDM process for gear cutting

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.

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.

The challenge of modern machining industries is mainly to achieve high quality, in terms of dimensional accuracy, surface finish and high production rate. In the present work, an attempt has been made to explore the effect of EDM process parameters on various Surface Roughness characteristics Ra, Rq and Rz. The experiments have been done on medium carbon steel EN8 which has high applications in tool, oil and gas industries. For the experimentation, Pulse on time (PON), Pulse off time (POFF), Wire tension (WT) and Wire feed rate (WF) were considered as EDM inputs. Similarly, Surface roughness parameters Ra, Rq and Rz were taken as output characteristics. Taguchi’s standard L27 (3 level * 3 parameters) Orthogonal Array was used for conducting the experiments and optimization of multi responses was done by Grey analysis. From the Grey analysis, optimal combination of machining parameters for Surface Roughness characteristics was found at Pulse-on time (PON):131 μs (level 3), Pulse-off time (POFF):58 μs (level 2), Wire tension (WT):2 Kg-f (level 1) and Wire feed rate (WF):4 m/min (level 1). Analysis of variance was used to find the influence of process parameters on responses. From ANOVA of Grey Relational Grade, it is found that wire feed has high influence (F= 59.18) and pulse off time has very low influence (F= 2.50) in effecting the Surface Roughness parameters taken simultaneously. Regression models for the responses were prepared by using MINITAB-16 software. From the residual analysis, it is clear that the models prepared for the responses were more accurate, adequate and they can be used for prediction of surface roughness characteristics. Finally, the experimental and predicted values were compared and they found very close to each other.

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.

At present, the growing needs and design requirements with significant weight savings as well as high strength-to-weight ratio as compared to conventional materials raised a growing interest towards composite materials. Metal matrix composites (MMCs) are extensively used in fields like aerospace, automobiles and army-defence applications due to their high specific strength, low coefficient of thermal expansion, wear resistance, high durability, excellent physical and mechanical properties. The filler reinforcements like SiC, TiC, Al2O3 improve the stiffness, specific strength, creep properties of MMCs, but the presence of such hard reinforcements makes MMCs difficult to machine through conventional machining processes. Wirecut electrical discharge machining (WEDM) is highly precise thermoelectrical process and is considered ideal for machining very hard materials, intricate geometries and tooling. This paper reviews the researches in WEDM of metal matrix composites and the relation between different process parameters like pulse on time, pulse off time, wire feed rate,voltage, wire tension, dielectric flow rate on the machining performances such as material removal rate (MRR), Kerf widthand surface roughness (SR). The future trends in WEDM researches were also recommended in this paper.

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.

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.

The present paper deals with the microstructural analysis of Titanium alloy gears (Ti-6Al-4V) by wire EDM process machined by both brass and combii wire electrodes. The objective of the present paper is to investigate the surface characteristics of both the wires and to analyze the effects on the material surface. Two types of wires i.e. brass wire and combii wires were selected for the analysis and the experiment was carried out using a full factorial design having 81 sets of combinations. Pulse on time, pulse off time, wire feed rate and servo voltage were taken as the input parameters and kerf width, material removal rate, surface roughness and wire wear rate were taken as the response parameters for the experiment. The output responses were optimized by using MOORA-based GA methodology. Microstructural analysis was carried out at the optimized settings obtained by two types of wires to investigate the surface defects and analysis present in the work-piece and the wire. The microstructural analysis for the brass wire was investigated depicting the formation of micro cracks, dendrites, spherical globules, melted debris, recast layer and wire rupture on the wire surface after the machining operation. Similarly the microstructural analysis for the combii wire was analyzed out depicting the formation of wire rupture, melted debris, spherical globules, cracks and wire burr formed on the wire after the machining process. The optical image confirms that brass wire has more rupture than the combii wire due to the spark efficiency of brass wire being more than the combii wire. The EDS of material and the wires have been analyzed depicting the presence of element and weight % in the sample. From the performance analysis and the present experiment attained, it was concluded that the combii wire is more desirable to produce high quality gears than the brass wires at the obtained optimized settings of the response.

Effect of input variables and cryogenic treatment in wire electric discharge machining of Ti-6Al-4V alloy for biomedical applications

Wire Electrical Discharge Machining is one of the most effective Non-Conventional Machining method available to machine intricate shapes which are very difficult to machine with conventional machining methods, on electrically conductive materials havingvarying hardness. In this research work, an attempt hasbeen made to study the effect of process parameters of WEDM on the Surface roughness. Input process parameters considered in this work are Pulse on time (TON), Pulse off time(TOFF), Wire Feed (WF), Wire Tension(WT) and theSurface Roughness as response. Al 7075 Aluminium alloywas taken as work material and Brass coated copper wire as electrode to machine and the values of roughness obtained by varying the input process parameters were analysed using Taguchi method (L 27 orthogonal array). The measured values have been used to develop the mathematical modeland the accuracy of the model wastested using Analysis of Variance (ANOVA) technique. Based on the predicted values the interaction graphs, were drawn betweentheSurface roughnessandthe processparameterstaken for this study. It was construedthatthepulse-on time has the highest impact on the Surface roughness followed by the Wire feed, Wire tension and the Pulse-off time. The surface roughnessgot increasedwith increase in wire feed. Finally optimum parameters have been arrived to achieve better surface roughness. WEDMed surface was also examined by using scanning electron microscope (SEM) and evinced that the physical behaviour of these process parameters on Surface roughness.Copyright © VBRI Press.

Influence of tool geometry on the quality of surface produced is well known and hence any attempt to assess the performance of end milling should include the tool geometry. In the present work, experimental studies have been conducted to see the effect of tool geometry (radial rake angle and nose radius) and cutting conditions (cutting speed and feed rate) on the machining performance during end milling of medium carbon steel. The first and second order mathematical models, in terms of machining parameters, were developed for surface roughness prediction using response surface methodology (RSM) on the basis of experimental results. The model selected for optimization has been validated with the Chi square test. The significance of these parameters on surface roughness has been established with analysis of variance. An attempt has also been made to optimize the surface roughness prediction model using genetic algorithms (GA). The GA program gives minimum values of surface roughness and their respective optimal conditions.

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.

Among various machining processes, wire electrical discharge machining (WEDM) is regarded as one of the efficient and cost-effective processes in machining of metal matrix nanocomposites (MMNC). This article discusses the investigation and optimization of machining parameters like the voltage, pulse off time, pulse on time, wire feed rate and wt% nano-SiC particles in WEDM of magnesium metal matrix nanocomposites. Experiments are conducted on magnesium reinforced with nano-silicon carbide particles of diameter 50-80 nm. Machining parameters like the voltage, pulse off time, pulse on time, wire feed rate and wt% nano-SiC are considered for the investigation using a four-factor D-optimal design based on the response surface methodology. The WEDM performances of MMNCs are evaluated by employing the parameters like metal removal rate (MRR) and surface roughness (SR). The second order quadratic models are developed between WEDM parameters and responses by regression analysis. The derived mathematical models are then optimized using the multi-objective optimization technique based on desirability analysis. The investigation indicates that the predicted values of the obtained model are in accordance with the experimental values. Surface morphology and the effects of nanoparticle reinforcement in the machined surface are observed through SEM micrographs.

Process Capability Analysis and Optimization in WEDM of Commercially Pure Titanium

In an era of continuous technological advancement, the wire electrical discharge machining (WEDM) process has evolved to address the ever-increasing demand for precision and efficiency in manufacturing. Among difficult-to-cut materials, Incoloy 800H is widely used in aerospace and nuclear applications. The current work incorporates machine learning (ML) algorithms, that is, decision tree, support vector machines, logistic regression, random forest, artificial neural network, linear regression, XGBRegressor, and K-nearest neighbors, to model and optimize the process during WEDM of Incoloy 800H. The novelty lies in the comprehensive investigation of the effects of varying process parameters, namely, pulse off time (Toff), pulse on time (Ton), servo voltage (Sv), and wire tension (Wt) on output parameters, including wire breakage and surface roughness (SR). The predicted outcomes by the XGBRegressor model exhibit strong agreement with the experimental results, demonstrating a high correlation coefficient ( R ) of 0.945. The XGBRegressor proved to be the most effective model for predicting SR, achieving the lowest mean absolute error and mean squared error among all ML algorithms evaluated. In conclusion, the findings underscore the potential of integrating ML with statistical techniques to enhance predictive capabilities and process optimization in advanced manufacturing domains.