Optimization of the EDM parameters on machining Ti–6Al–4V with multiple quality characteristics

In this paper, parameter optimization of the electrical discharge machining process to Ti–6Al–4V alloy considering multiple performance characteristics using the Taguchi method and grey relational analysis is reported. Performance characteristics including the electrode wear ratio, material removal rate and surface roughness are chosen to evaluate the machining effects. The process parameters selected in this study are discharge current, open voltage, pulse duration and duty factor. Experiments based on the appropriate orthogonal array are conducted first. The normalised experimental results of the performance characteristics are then introduced to calculate the coefficient and grades according to grey relational analysis. The optimised process parameters simultaneously leading to a lower electrode wear ratio, higher material removal rate and better surface roughness are then verified through a confirmation experiment. The validation experiments show an improved electrode wear ratio of 15%, material removal rate of 12% and surface roughness of 19% when the Taguchi method and grey relational analysis are used.     

Multiple process parameter optimization of wire electrical discharge machining on Inconel 825 using Taguchi grey relational analysis

In this paper, an effective approach, Taguchi grey relational analysis, has been applied to experimental results of wire cut electrical discharge machining (WEDM) on Inconel 825 with consideration of multiple response measures. The approach combines the orthogonal array design of experiment with grey relational analysis. The main objective of this study is to obtain improved material removal rate, surface roughness, and spark gap. Grey relational theory is adopted to determine the best process parameters that optimize the response measures. The experiment has been done by using Taguchi’s orthogonal array L36 (2¹?×?3⁷). Each experiment was conducted under different conditions of input parameters. The response table and the grey relational grade for each level of the machining parameters have been established. From 36 experiments, the best combination of parameters was found. The experimental results confirm that the proposed method in this study effectively improves the machining performance of WEDM process.     

基于灰关联分析的电火花刻槽工艺参数优化

为了获得较高的加工质量和加工效率,在对加工时间、电极蚀损量、回退次数和表面粗糙度等指标进行综合分析的基础上,对电极材料、加工电压、峰值电流、脉冲宽度、脉冲间隔、正常进给速度以及快进速度等工艺参数进行了分析和优化.采用电火花刻槽机为实验工具进行正交试验,对正交试验的结果进行了直观分析和灰关联分析.对灰关联度进行F检验后,获得了显著的影响因素,最终遴选出最优加工参数.验证试验表明,灰关联分析法能简化复合工艺指标的优化选择,具有很好的应用效果.     

Application of Taguchi-grey multi responses optimization on process parameters in electro erosion

Convention Taguchi method deals with only single response optimization problems. Since the electrical discharge machining process involved with many response parameters, Taguchi method alone cannot help to obtain optimal process parameters in such process. In the present work, an endeavor has been made to derive optimal combination of electrical process parameters in electro erosion process using grey relational analysis with Taguchi method. This multi response optimization of the electrical discharge machining process has been conducted with AISI 202 stainless steel with different tool electrodes such as copper, brass and tungsten carbide. Gap voltage, discharge current and duty factor have been used as electrical excitation parameters with different process levels. Taguchi L₂₇ orthogonal table has been assigned for conducting experiments with the consideration of interactions among the input electrical process parameters. Material removal rate, electrode wear rate and surface roughness have been selected as response parameters. From the experimental results, it has been found that the electrical conductivity of the tool electrode has the most influencing nature on the machining characteristics in EDM process. The optimal combination of the input process parameters has been obtained using Taguchi-grey relational analysis.     

Optimization of cryogenic cooled EDM process parameters using grey relational analysis

This paper presents an experimental investigation on cryogenic cooling of liquid nitrogen (LN₂) copper electrode in the electrical discharge machining (EDM) process. The optimization of the EDM process parameters, such as the electrode environment (conventional electrode and cryogenically cooled electrode in EDM), discharge current, pulse on time, gap voltage on material removal rate, electrode wear, and surface roughness on machining of AlSiCp metal matrix composite using multiple performance characteristics on grey relational analysis was investigated. The L₁₈ orthogonal array was utilized to examine the process parameters, and the optimal levels of the process parameters were identified through grey relational analysis. Experimental data were analyzed through analysis of variance. Scanning electron microscopy analysis was conducted to study the characteristics of the machined surface.     

电火花微小孔加工工艺参数的优化研究

由于目前对电火花放电机理认识的有限性而使参数优化成为电火花加工中的难点之一,特别是多项工艺指标要求下的参数优化问题更是缺乏理论指导和具体应用方法。因此,在用正交试验法研究了电火花微小孔加工中放电参数和非放电参数对加工速度、电极损耗和孔径间隙等各单项工艺指标的影响规律的基础上,采用灰色相关性理论,重点解决了多项工艺指标下的参数优化这一难点问题,简化了优化过程,得出了优化的参数组合方案。     

Optimization of machining parameters using magnetic-force-assisted EDM based on gray relational analysis

This work developed a novel process of magnetic-force-assisted electrical discharge machining (EDM) and conducted an experimental investigation to optimize the machining parameters associated with multiple performance characteristics using gray relational analysis. The main machining parameters such as machining polarity (P), peak current (I _P), pulse duration (τ _P), high-voltage auxiliary current (I _H), no-load voltage (V), and servo reference voltage (S _V) were selected to explore the effects of multiple performance characteristics on the material removal rate, electrode wear rate, and surface roughness. The experiments were conducted according to an orthogonal array L18 based on Taguchi method, and the significant process parameters that affected the multiple performance characteristics of magnetic-force-assisted EDM were also determined form the analysis of variance. Moreover, the optimal combination levels of machining parameters were also determined from the response graph and then verified experimentally. The multiple performance characteristics of the magnetic-force-assisted EDM were improved, and the EDM technique with high efficiency, high precision, and high-quality surface were established to meet the demand of modern industrial applications.     

Process parameter optimization during EDM of AISI 316 LN stainless steel by using fuzzy based multi-objective PSO

The present contribution describes an application of a hybrid approach using fuzzy logic and particle swarm optimization (PSO) for optimizing the process parameters in the electric discharge machining (EDM) of AISI 316LN Stainless Steel. In this study, each experimentation was performed under different machining conditions of pulse current, pulse on-time, and pulse off-time. Machining performances such as MRR and EWR were evaluated. A Taguchi L9 orthogonal array was produced to plan the experimentation and the regression method was applied to model the relationship between the input factors and responses. A fuzzy model was employed to provide a fitness function to PSO by unifying the multiple responses. Finally, PSO was used to predict the optimal process parametric settings for the multi-performance optimization of the EDM operation. The experimental results confirm the feasibility of the strategy and are in good agreement with the predicted results over a wide range of machining conditions employed in the process.     

基于信噪比与灰关联度的电火花微小孔加工工艺参数的优化

在正交试验的基础上,采用参数设计中的信噪比方法,更加准确地反映具有强干扰特性的电火花微细加工过程中参数对加工工艺目标的影响程度,分析出各项参数对工艺指标影响的主次关系。以减小工艺性能的波动性为目的,结合灰色关联度分析方法,将多目标参数优化问题转化为单目标灰关联度的优化问题,得到电火花微小孔加工的多项工艺指标要求下的参数优化组合。综合试验结果表明,基于信噪比和灰关联度的优化方法能有效地提高微细电火花的加工精度、加工效率和加工稳定性。     

Determination of optimum parameters for multi-performance characteristics in drilling by using grey relational analysis

The theory of grey systems is a new technique for performing prediction, relational analysis and decision making in many areas. In this paper, the use of grey relational analysis for optimising the drilling process parameters for the work piece surface roughness and the burr height is introduced. Various drilling parameters, such as feed rate, cutting speed, drill and point angles of drill were considered. An orthogonal array was used for the experimental design. Optimal machining parameters were determined by the grey relational grade obtained from the grey relational analysis for multi-performance characteristics (the surface roughness and the burr height). Experimental results have shown that the surface roughness and the burr height in the drilling process can be improved effectively through the new approach .     

Application of grey-based Taguchi method for simultaneous optimization of multiple quality characteristics in laser transmission welding process of thermoplastics

In the present work, Taguchi method in combination with grey relational analysis is applied for solving multi-criteria optimization problems in laser transmission welding processes. The welding parameters, namely laser power, welding speed and defocal position are optimized with respect to weld strength and weld width. Using the Taguchi quality design concept, an L16 orthogonal array table is chosen for the experiments. Grey relational analysis is applied to convert the multiple quality characteristics to a single performance characteristic called grey relational grade. Optimal welding parameters are then determined by the Taguchi method using grey relational grade as the quality index. Furthermore, analysis of variance is carried out to identify the most significant factor for the overall output feature of the laser transmission welding process. The results of the confirmation experiment show that the optimal laser transmission welding parameters can be determined effectively so as to improve multiple quality characteristics through this approach.     

Optimization of EDM process for multiple performance characteristics using Taguchi method and Grey relational analysis

Electrical discharge machining (EDM) is one of the most extensively used non-conventional material removal processes. The Taguchi method has been utilized to determine the optimal EDM conditions in several industrial fields. The method, however, was designed to optimize only a single performance characteristic. To remove that limitation, the Grey relational analysis theory has been used to resolve the complicated interrelationships among the multiple performance characteristics. In the present study, we attempted to find the optimal machining conditions under which the micro-hole can be formed to a minimum diameter and a maximum aspect ratio. The Taguchi method was used to determine the relations between machining parameters and process characteristics. It was found that electrode wear and the entrance and exit clearances had a significant effect on the diameter of the micro-hole when the diameter of the electrode was identical. Grey relational analysis was used to determine the optimal machining parameters, among which the input voltage and the capacitance were found to be the most significant. The obtained optimal machining conditions were an input voltage of 60V, a capacitance of 680pF, a resistance of 500Ω, the feed rate of 1.5μm/s and a spindle speed of 1500rpm. Under these conditions, a micro-hole of 40μm average diameter and 10 aspect ratio could be machined.     

Single and multiobjective optimization of Inconel 718 nickel-based superalloy in the wire electrical discharge machining

Inconel 718 is widely used in high-temperature environments, high-performance aircraft, and hypersonic missile weapon systems; however, it is very difficult to machine using conventional techniques. This study employed an L9 Taguchi orthogonal array for the analysis of wire electrical discharge machining parameters when used for the machining of Inconel 718. Our aim was to determine the optimal combination of parameters to minimize surface roughness while maximizing the material removal rate. The Taguchi method is widely applied in mechanical engineering with the aim of identifying the optimal combination of processing parameters as they pertain to single quality characteristics. Unfortunately, Taguchi analysis often leads to contradictory results when seeking to rectify multiple objectives. To resolve this issue, this study implemented gray relational analysis in conjunction with Taguchi method to obtain the optimal combination of parameters to deal specifically with multiple quality objectives. For the dual objectives of surface roughness and material removal rate, the optimal combination of parameters derived using gray relational analysis resulted in a mean surface roughness of 2.75 μm. In L9 orthogonal array experiments, run 1 produced the best gray relational grade with mean surface roughness of 2.80 μm, representing an improvement of 1.8%. The material removal rate achieved after the application of gray relational analysis was 0.00190 g/s, whereas the L9 experiment achieved a material removal rate of 0.00123 g/s, representing an improvement of 54.5%.     

Optimisation of process parameters for multi-performance characteristics in EDM of Al2O3 ceramic composite

The advantages of electrical discharge machining (EDM) in machining of complex ceramic components have promoted research in the area of EDM of ceramic composites. The recent developments in ceramic composites are focused not only on the improvements of strength and toughness, but also on possibilities for difficult-to-machine shapes using EDM. One such EDM-machinable ceramic composite material (Al₂O₃–SiC_w–TiC) has been developed recently and has been selected in the present study to investigate its EDM machinability. Experiments were conducted using discharge current, pulse-on time, duty cycle and gap voltage as typical process parameters. The grey relational analysis was adopted to obtain grey relational grade for EDM process with multiple characteristics namely material removal rate and surface roughness. Analysis of variance was used to study the significance of process variables on grey relational grade which showed discharge current and duty cycle to be most significant parameters. Other than discharge current and duty cycle, pulse-on time and gap voltage have also been found to be significant. To validate the study, confirmation experiment has been carried out at optimum set of parameters and predicted results have been found to be in good agreement with experimental findings.     

Black layer affects the thermal conductivity of the surface of copper–tungsten electrode

The aim of this article is to show that the black layer composition varies with an interaction of electrical discharge machining (EDM) input parameters, which affects the electrode wear ratio (EWR), using the Taguchi methodology. An orthogonal array that provides a countermeasure to interactions was used. The analysis seeks the optimal levels of the EDM input parameters that produce an improvement in the EWR. The additive model was used with the optimum levels of the parameters to predict the EWR values. The outcome analysis shows a poor confirmation, meaning that there is an interaction of EDM input parameters affecting the EWR values. Thus, the present investigation reveals that black layer affects the electrode wear ratio due to a change in the thermal conductivity of the surface of copper–tungsten electrode.     

Optimisation of machining parameters for hard machining: grey relational theory approach and ANOVA

The present paper deals with experimental investigations carried out for machinability study of hardened steel and to obtain optimum process parameters by grey relational analysis. An orthogonal array, grey relations, grey relational coefficients and analysis of variance (ANOVA) are applied to study the performance characteristics of machining process parameters such as cutting speed, feed, depth of cut and width of cut with consideration of multiple responses, i.e. volume of material removed, surface finish, tool wear and tool life. Tool wear patterns are measured using optical microscope and analysed using scanning electron microscope and X-ray diffraction technique. Chipping and adhesion are main causes of wear. The optimum process parameters are calculated for rough machining and finish machining using grey theory and results are compared with ANOVA.     

Optimization of multi machining characteristics in WEDM of WC-5.3%Co composite using integrated approach of Taguchi,GRA and entropy method

Wire electrical discharge machining (WEDM) is a well known process for generating intricate and complex geometries in hard metal alloys and metal matrix composites with high precision. In present work, intricate machining of WC-5.3%Co composite on WEDM has been reported. Taguchi’s design of experiment has been utilised to investigate the process parameters for four machining characteristics namely material removal rate, surface roughness, angular error and radial overcut. In order to optimize the four machining characteristics simultaneously, grey relational analysis (GRA) coupled with entropy measurement method has been employed. Through GRA, grey relational grade has been computed as a performance index for predicting the optimal parameters setting for multi machining characteristics. Using Analysis of Variance (ANOVA) on grey relational grade, significant parameters affecting the multi-machining characteristics has been determined. Confirmatory results prove the potential of present approach.     

Multi-objective optimization of surface roughness and cutting forces in high-speed turning of Inconel 718 using Taguchi grey relational analysis (TGRA)

In this paper, a new effective approach, Taguchi grey relational analysis has been applied to experimental results in order to optimize the high-speed turning of Inconel 718 with consideration to multiple performance measures. The approach combines the orthogonal array design of experiments with grey relational analysis. Grey relational theory is adopted to determine the best process parameters that give lower magnitude of cutting forces as well as surface roughness. The response table and the grey relational grade graph for each level of the machining parameters have been established. The parameters: cutting speed, 475?m/min; feed rate, 0.10?mm/rev; depth of cut, 0.50?mm; and CW2 edge geometry have highest grey relational grade and therefore are the optimum parameter values producing better turning performance in terms of cutting forces and surface roughness. Depth of cut shows statistical significance on overall turning performance at 95% confidence interval.     

Optimising robust design for correlated quality characteristics

Many industries have employed the Taguchi method over the years to improve product and process performance. This method is powerful and effective in helping manufacturers to design their products and processes as well as to solve troublesome quality problems. Several studies have presented approaches to multiple quality characteristics. Few published articles have focused primarily on optimising the correlated multiple quality characteristics. This research presents an approach to optimising the correlated multiple quality characteristics using principal component analysis and grey relational analysis based on quality loss. As can be seen in the results, the advantage of this approach is that the chosen optimal design for multiple quality characteristics is robust for the correlated multiple quality characteristics. This method finds the optimal parameter conditions more easily, even when the average quality losses of the quality characteristics are all diverse.     

Surface Improvement Using a Combination of Electrical Discharge Machining with Ball Burnish Machining Based on the Taguchi Method

This study investigates the feasibility of improving surface integrity via a novel combined process of electrical discharge machining (EDM) with ball burnish machining (BBM) using the Taguchi method. To provide burnishing immediately after the EDM process, ZrO₂ balls were attached to the tool electrode in the experiments. To verify the optimal process, three observed values, i.e. material removal rate, surface roughness, and improvement ratio of surface roughness were chosen. In addition, six independent parameters were adopted for evalu-ation by the Taguchi method. From the ANOVA and S/N ratio response graph, the significant parameters and the optimal combination level of machining parameters were obtained. Experimental results indicate that the combined process effectively improves the surface roughness and eliminates the micro pores and cracks caused by EDM. Therefore, the combination of EDM and BBM is a feasible process by which to obtain a fine-finishing surface and achieve surface modification.