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.     

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.     

An experimental investigation on machining parameters of AISI D2 steel using WEDM

The performance of the wire electrodischarge machining (WEDM) machining process largely depends upon the selection of the appropriate machining variables. Optimization is one of the techniques used in manufacturing sectors to arrive for the best manufacturing conditions, which are essential for industries toward manufacturing of quality products at lowest cost. As there are many process variables involved in the WEDM machining process, it is difficult to choose a proper combination of these process variables in order to maximize material removal rate and to minimize tool wear and surface roughness. The objective of the this work is to investigate the effects of process variables like pulse on time, pulse off time, peak current, servo voltage, and wire feed on material removal rate (MRR), surface roughness (SR), gap voltage, gap current, and cutting rate in the WEDM machining process. The experiment has been done using Taguchi’s orthogonal array L27 (3⁵). Each experiment was conducted under different conditions of input parameters and statistically evaluated the experimental data by analysis of variance (ANOVA) using MINITAB and Design Expert tools. The present work also aims to develop mathematical models for correlating the inter-relationships of various WEDM machining parameters and performance parameters of machining on AISI D2 steel material using response surface methodology (RSM).The significant machining parameters and the optimal combination levels of machining parameters associated with performance parameters were also drawn. The observed optimal process parameter settings based on composite desirability (61.4 %) are pulse on time 112.66 μs, pulse off time 45 μs, spark gap voltage 46.95 V, wire feed 2 mm/min, peak current of 99.99 A for achieving maximum MRR, gap current, gap voltage, cutting rate, and minimum SR; finally, the results were experimentally verified.     

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.     

A new approach to surface roughness and roundness improvement in wire electrical discharge turning based on statistical analyses

In this paper, the effects and the optimization of machining parameters on surface roughness and roundness in the turning wire electrical discharge machining (TWEDM) process are investigated. In the TWEDM process, a new machining parameter, such as rotational speed, is introduced, which changes the normal machining conditions in conventional wire electrical discharge machining (WEDM). By the Taguchi method, a complete realization of the process parameters and their effects were achieved. The Taguchi method has not been used in TWEDM by other researchers. The surface roughness and roundness were measured to verify the process. In addition, the open-circuit voltage, pulse-off time, open arc voltage, and the inter-electrode gap size, which are replaced by power, time-off, voltage, and servo, respectively, and also wire tension, wire speed, and rotational speed were chosen for evaluation by the Taguchi method. An L₁₈ (2¹?×?3⁷) Taguchi standard orthogonal array was chosen for the design of experiments. The level of importance of the machining parameters on the surface roughness and roundness was determined by using analysis of variance (ANOVA). The optimum machining parameters combination was obtained by using the analysis of signal-to-noise (S/N) ratios. The variation of surface roughness and roundness with machining parameters was mathematically modeled by using the regression analysis method. Finally, experimentation was carried out to identify the effectiveness of the proposed method. The presented model is also verified by a set of verification tests.     

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 weld characteristics of friction welded AA 6061-AA 6351 joints using grey-principal component analysis (G-PCA)

Friction welding is a solid state joining process in which the quality of welded joint is influenced by the input parameter setting. The objective of the present study is to conduct experimental investigation of the bond strength and hardness of the friction welded joints involving AA 6061 and AA 6351 alloys by conducting experiments designed by Taguchi’s L⁹ orthogonal matrix array. A systematic approach becomes essential to find the optimal setting of friction welding parameters. Hence a new approach named grey-principal component analysis (G-PCA) is presented in which the principal component analysis (PCA) is used to generate weights for the grey relational coefficients obtained in the grey relational analysis (GRA). The results of the confirmation experiment conducted with the optimal setting predicted by the G-PCA have shown improvements in the performance characteristics. Hence G-PCA can be used for experimental welding optimization.     

Optimisation of the EDM Process Based on the Orthogonal Array with Fuzzy Logic and Grey Relational Analysis Method

In this paper, the use of the grey relational analysis based on an orthogonal array and fuzzy-based Taguchi method for optimising the multi-response process is reported. Both the grey relational analysis method without using the S/N ratio and fuzzy logic analysis are used in an orthogonal array table in carrying out experiments for solving the multiple responses in the electrical discharge machining (EDM) process. Experimental results have shown that both approaches can optimise the machining parameters (pulse on time, duty factor, and discharge current) with considerations of the multiple responses (electrode wear ratio, material removal rate, and surface roughness) effectively. It seems that the grey relational analysis is more straightforward than the fuzzy-based Taguchi method for optimising the EDM process with multiple process responses.     

Electrical discharge precision machining parameters optimization investigation on S-03 special stainless steel

S-03 is a novel special stainless steel, which is widely used in precision aerospace parts and electrical discharge machining technology has the merit of high-accuracy machining. This paper aims to combine gray relational analysis and orthogonal experimental to optimize electrical discharge high-accuracy machining parameters. The four process parameters of gap voltage, peak discharge current, pulse width, and pulse interval are required to optimize in the fewest experiment times. The material removal rate and surface roughness are the objective parameters. The experiment were carried out based on Taguchi L9 orthogonal array, then we carried out the gray relational analysis to optimize the multi-objective machining parameter, finally, we verified the results through a confirmation experiment. The sequence of machining parameters from primary to secondary are as follows: discharge current 7A, pulse interval 100 μs, pulse width 50 μs, and gap voltage 70 V. Using the above machining parameters, we can obtain good surface roughness Ra1.7 μm, and material removal rate 13.3 mm³/min. The machined work piece almost has no surface modification layer. The results show that combining orthogonal experiment and gray relational analysis can further optimize machining parameters, the material removal rate increased by 23.8 %, and the surface roughness almost has no change.     

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.     

Application of Taguchi,ANFIS and grey relational analysis for studying,modeling and optimization of wire EDM process while using gaseous media

In the dry wire electrical discharge machining (WEDM) process, the liquid dielectric is replaced with gaseous medium to enhance the machining environment safety. Also, this modification improves the surface quality of machined specimen but decreases the material removal rate of the process. In the present work, experimental study of dry WEDM process has been performed while machining of Al/SiC metal matrix composite. At first, a series of exploratory experiments has been conducted to identify appropriate gas and wire material based on their cutting velocity. After selection of the best gas and best wire, they were used for later stage of experiments. At the next stage, experiment were designed and conducted based on L₂₇ Taguchi's orthogonal array to study the effect of pulse on time, pulse off time, gap voltage, discharge current, wire tension and wire feed on cutting velocity (CV) and surface roughness (SR). Analysis of variances (ANOVA) has been performed to identify significant factors. In order to correlate relationship between process inputs and responses, adaptive neuro-fuzzy inference system has been utilized. At the end, a grey relational analysis has been used to maximize CV and minimize SR simultaneously. Results indicated that oxygen gas and brass wire guarantee superior cutting velocity. Also according to ANOVA, pulse on time and current were found to have significant effect on CV and SR.     

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%.     

Optimization of WEDM process of pure titanium with multiple performance characteristics using Taguchi’s DOE approach and utility concept

This paper describes the development of multi response optimization technique using utility method to predict and select the optimal setting of machining parameters in wire electro-discharge machining (WEDM) process. The experimental studies in WEDM process were conducted under varying experimental conditions of process parameters, such as pulse on time(T_on), pulse off time(T_off), peak current (IP), wire feed (WF), wire tension (WT) and servo voltage (SV) using pure titanium as work material. Experiments were planned using Taguchi’s L27 orthogonal array. Multi response optimization was performed for both cutting speed (CS) and surface roughness (SR) using utility concept to find out the optimal process parameter setting. The level of significance of the machining parameters for their effect on the CS and SR was determined by using analysis of variance (ANOVA). Finally, confirmation experiment was performed to validate the effectiveness of the proposed optimal condition.     

Multi-response optimisation of thermosonic copper wire-bonding process with correlated responses

Multi-response optimisation has become an increasingly important issue in complex industrial processes, particularly in situations where more than one correlated responses must be assessed simultaneously. This study discusses the optimisation of a multi-response thermosonic copper wire-bonding process, used in the semiconductor assembly industry. Since nine process parameters were adopted as control factors for this experiment, the design of the experiment was based on the Taguchi method as per L ₁₂ orthogonal array. Three responses, assumed to be correlated, were considered for each experimental trial. In order to take into account correlations among responses, a specific method for analysis was used, drawing on multi-criterion methodology based on Taguchi’s quality loss function, principal component analysis and grey relational analysis. Finally, experimental confirmation was carried out to identify effectiveness of the method. It has been shown that the generic approach in the analysis for correlated responses provides better results than traditional method of analysis, in terms of optimal parameter design that meets the specifications of all three responses, for the observed process optimisation.     

Multi response optimization of wire EDM operations using robust design of experiments

In this present study a multi response optimization method using Taguchi’s robust design approach is proposed for wire electrical discharge machining (WEDM) operations. Experimentation was planned as per Taguchi’s L16 orthogonal array. Each experiment has been performed under different cutting conditions of pulse on time, wire tension, delay time, wire feed speed, and ignition current intensity. Three responses namely material removal rate, surface roughness, and wire wear ratio have been considered for each experiment. The machining parameters are optimized with the multi response characteristics of the material removal rate, surface roughness, and wire wear ratio. Multi response S/N (MRSN) ratio was applied to measure the performance characteristics deviating from the actual value. Analysis of variance (ANOVA) is employed to identify the level of importance of the machining parameters on the multiple performance characteristics considered. Finally experimental confirmation was carried out to identify the effectiveness of this proposed method. A good improvement was obtained.     

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 .     

Influence of slab milling process parameters on surface integrity of HSLA: a multi-performance characteristics optimization

An attempt has been made in this paper to determine the optimal setting of slab milling process parameters. Four process parameters, i.e. cutting fluid, cutting speed, feed and depth-of-cut each at three levels except the cutting fluid at two levels, were considered. The multi-performance characteristics of the process were measured in terms of surface integrity defined by surface roughness, surface strain and micro-hardness of the work-piece. Eighteen experiments, as per Taguchi’s?L₁₈ orthogonal array, were performed on high-strength low-alloy steel. Grey relational analysis, being a widely used technique for multi-performance optimization, was used to determine Grey relational grade. Subsequently, Taguchi response table method and ANOVA were used for data analysis. Confirmation experiment was conducted to determine the improvement in the surface integrity using this approach. Results revealed that machining done in the presence of cutting fluid, at a cutting speed of 1,800 r.p.m. with a feed of 150?mm/min and depth-of-cut of 0.23?mm, yielded the optimum multi-performance characteristics of the slab milling process. Further, the results of ANOVA indicated that all four machining parameters significantly affected the multi-performance with maximum contribution from depth-of-cut (33.76%) followed by feed (24.02%), cutting speed (16.29%) and cutting fluid (13.21%).     

Competitive parameter optimization of multi-quality CNC turning

Surface roughness, tool wear, and material removal rate (MRR) are major intentions in the modern computer numerical controlled (CNC) machining industry. In this paper, the ${\text{L}}_9 \left( {3^4 } \right)$ orthogonal array of a Taguchi experiment is selected for four parameters (cutting depth, feed rate, speed, and tool nose runoff) with three levels (low, medium, and high) in optimizing the finish turning parameters on an ECOCA-3807 CNC lathe. The surface roughness (R_a) and tool wear ratio (mm^?2) are primarily observed as independent objectives for developing two combinations of optimum single-objective cutting parameters. Additionally, the levels of competitive orthogonal array are then proposed between the two parameter sets. Therefore, the optimum competitive multi-quality cutting parameters can then be achieved. Through the machining results of the CNC lathe, it is shown that both tool wear ratio and MRR from our optimum competitive parameters are greatly advanced with a minor decrease in the surface roughness in comparison to those of benchmark parameters. This paper not only proposes a competitive optimization approach using orthogonal array, but also contributes a satisfactory technique for multiple CNC turning objectives with profound insight.     

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.     

Multiple quality optimization in laser cutting of difficult-to-laser-cut material using grey–fuzzy methodology

This paper investigates the laser cutting performance of 1 mm Duralumin sheet with the aim to improve quality of cut by simultaneously optimising multiple performances such as cut edge surface roughness, kerf taper and kerf width. The experimental data obtained by Taguchi methodology-based L₂₇ orthogonal array experimentation have been used in the hybrid approach optimization of grey relational analysis and fuzzy logic theory. The predicted optimum results have been verified by conducting confirmation experiments. The verification results show an overall improvement of 19 % in multiple quality characteristics. The effects of significant factors on quality characteristics have also been discussed.