Estimating the effect of process parameters on surface integrity of EDMed AISI D2 tool steel by response surface methodology coupled with grey relational analysis

In this investigation, a hybrid optimization approach is used for the estimation of minimal surface integrity of surface created in electrical discharge machining (EDM). A new combination, response surface methodology coupled with the grey relational analysis method has been proposed and used to optimize the machining parameters of EDM. The significant input parameters such as pulse current (Ip), pulse duration (Ton), duty cycle (Tau) and discharge voltage (V ) are considered, and white layer thickness, surface roughness, and surface crack density have been considered as responses for this study. Thirty experiments were conducted on American Iron and Steel Institute (AISI) D2 steel work piece materials based on central composite design. The optimum conditions of the machining parameters were obtained from the grey relational grade. Analysis of variance is used to find the percentage contribution of the input parameters and found that Tau was the most influencing parameter followed by Ton and Ip in EDM of D2 steel. The $R^2$ value for the grey relational grade model was 0.918. These results provide useful information about how to control the responses and ensure the high-quality surfaces-quality surfaces. This method is simple with easy operability. The assessment outcome provides a scientific reference to obtain the minimal condition of surface integrity, and they were found to be a pulse current of 1 A, a pulse duration of 50 μs, a duty cycle of 80 %, and discharge voltage 40 V.     

Optimization of machining characteristics in electric discharge machining of 6061Al/Al2O3p/20P composites by grey relational analysis

Present investigation applied the designs of experiments and grey relational analysis (GRA) approach to optimise parameters for electrical discharge machining process of 6061Al/Al₂O₃p/20P aluminium metal matrix composites. Planning of experiments was based on an L18 (2^1?×?3^5) orthogonal array to determine an optimal setting. The process parameters included one noise factor, aspect ratio having two levels and five control factors, viz. pulse current, pulse ON time, duty cycle, gap voltage and tool electrode lift time with three levels each. The material removal rate, tool wear rate and surface roughness were selected as the evaluation criteria, in this study. Optimal combination of process parameters is determined by the grey relational grade (GRG) obtained through GRA for multiple performance characteristics. Analysis of variance for the GRG is also implemented. It is shown that through GRA, the optimization of the multiple performance characteristics can be greatly simplified.     

Performance studies of multilayer hard surface coatings (TiN/TiCN/Al2O3/TiN) of indexable carbide inserts in hard machining: Part-II (RSM,grey relational and techno economical approach)

This paper presents the mathematical modelling and parametric optimization on flank wear and surface roughness based on response surface methodology and grey-based Taguchi method in finish hard turning of AISI 4340 steel (HRC 47 ± 1) using multilayer coated carbide (TiN/TiCN/Al₂O₃/TiN) insert under dry environment. The economical feasibility of utilizing multilayer TiN coated carbide insert has been described. Model adequacy has been checked using correlation coefficients. From main effect, it is evident that, cutting speed is the most significant factor for flank wear followed by depth of cut and feed. Again, feed is the most significant factor for surface roughness followed by cutting speed and depth of cut. The coefficient of determination (R²) is more than 75% for RSM models developed, which shows the high correlation exist between the experimental and predicted values. The experimental vs. predicted values of flank wear and surface roughness (Ra and Rz) are also found to be very close to each other implying significance of models developed. The improvement of grey relational grade from initial parameter combination (d2–f3–v4) to the optimal parameter combination (d1–f1–v3) is found to be 0.3093 using grey relational analysis coupled with Taguchi method for simultaneous optimization of responses. Flank wear (VBc) and surface roughness parameters (Ra and Rz) are decreased 1.9, 2.32 and 1.5 times respectively considering optimal parametric combinations for multi-responses. The calculated total machining cost per part is only Rs. 3.17 due to higher tool life (47 min at their optimal level) of multilayer TiN coated carbide insert. It brings to the reduction of downtime and increases the savings.     

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.     

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.     

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.     

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.     

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 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.     

The influence of the cutting tool microgeometry on the machinability of hardened AISI 4140 steel

The aim of this work is to define the cutting conditions that allow the dry drilling of carbon fiber reinforced epoxy (CFRE) composite materials taking into consideration the quality of the drilled holes (the exit delamination factor and the cylindricity error) and the optimum combination of drilling parameters. A further aim is to use grey relational analysis to improve the quality of the drilled holes. The machining parameters were measured according to 3³ full factorial parameter designs (27 experiments with independent process variables). The experiments were carried out under various cutting parameters with different spindle speeds and feed rates. Drilling tests were done using WC carbide, high-speed steel (HSS), and TiN-coated carbide drills. The experiment design was accomplished by application of the statistical analysis of variance (ANOVA). Results show that the thrust force is mainly influenced by the tool materials and the feed rate, which has a strong influence on the exit delamination factor. On the other hand, the spindle speed particularly affects the cylindricity error of the holes. Correlations were established between spindle speed/feed rate and the various machining parameters so as to optimize cutting conditions. These correlations were found by quadratic regression using response surface methodology (RSM). Finally, tests were carried out to check the concordance of experimental results.     

Experimental investigation and optimisation in drilling of GFRP composites

Glass fibre-reinforced polymer (GFRP) composite materials are one of the important materials and are economic alternative to engineering materials because of their superior properties. This paper presents an effective approach for the optimisation of drilling parameters with multiple performance characteristics based on the Tagugch’s method with grey relational analysis. Taguchi’s L₁₆, 4-level orthogonal array has been used for the experimentation. The drilling parameters such as spindle speed and feed rate are optimised with consideration of multiple performance characteristics, such as thrust force, workpiece surface roughness and delamination factor. Response table and response graph are used for the analysis. The analysis of grey relational grade indicates that feed rate is the more influential parameter than spindle speed. The results indicate that the performance of drilling process can be improved effectively through this approach.     

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.     

Taguchi-based grey relational analysis for modeling and optimizing machining parameters through dry turning of Incoloy 800H

The present research focused on the optimization of machining parameters and their effects by dry-turning an incoloy 800H on the basis of Taguchi-based grey relational analysis. Surface roughness (Ra, Rq and Rz), cutting force (Fz), and cutting power (P) were minimized, whereas Material removal rate (MRR) was maximized. An L ₂₇ orthogonal array was used in the experiments, which were conducted in a computerized and numerical-controlled turning machine. Cutting speed, feed rate, and cut depth were set as controllable machining variables, and analysis of variance was performed to determine the contribution of each variable. We then developed regression models, which ultimately conformed to investigational and predicted values. The combinational parameters for the multiperformance optimization were V = 35 m/min, f = 0.06 mm/rev and a = 1 mm, which altogether correspond to approximately 48.98 % of the improvement. The chip morphology of the incoloy 800H was also studied and reported.     

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.     

A study on the performance of some multi-response optimisation methods for WEDM processes

Wire electrical discharge machining (WEDM) processes are having several performance measures which are affected by different process parameters. In the recent past, researchers have attempted several systematic procedures for optimising the multiple responses of WEDM processes. However, most of these approaches use complex statistical/mathematical tools and are, therefore, impractical for application by the engineers who may not have a strong background in mathematics. Only a few methods, e.g., grey relational analysis and multiple response signal-to-noise ratio approaches use relatively simpler computational procedures. On the other hand, the computational procedures for the weighted signal-to-noise (WSN) ratio and VIKOR (VlseKriterijumska Optimizacija I Kompromisno Resenje in Serbian) methods are also quite simpler and, therefore, can easily be implemented by the engineers. However, an engineer needs to select the method that can give the best optimisation performance for the process. In this paper, the computational requirements for these four multi-response optimisation methods are standardised. Two sets of experimental data on WEDM processes are analysed using these standardised multi-response optimisation methods, and their relative performances are then compared. The results show that no method can lead to better optimisation than the WSN ratio method.     

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.     

Grey-RBF-FA method to optimize surface integrity for inclined end milling Inconel 718

Performance and service life of aerospace component are significantly influenced by surface integrity, especially for cyclically loaded thin-walled structure component. This work focuses on the surface roughness and residual stress of machined surface, which are considered the most important indications of surface integrity, for inclined end milling Ni-based superalloy Inconel 718. The purpose is to minimize surface roughness and maximize compressive residual stress by optimizing the cutter geometry. Based on the grey relational analysis (GRA), an integrated multi-objective optimization approach with the radial basis function (RBF) neural network and the firefly algorithm (FA) is developed. The end-tooth rake angle, end-tooth relief angle, and helix angle are selected as design factors, while the residual stress and the 3D surface roughness are taken as performance characteristics. The orthogonal array L16 (4³) is employed to generate sample data set. Then, the GRA is introduced to search the most influential factor by the grey relational grade (GRG). Subsequently, the proposed grey-RBF-FA method is applied to the multi-objective optimization problem. After calculating the GRG, the RBF network is used to relate the GRG with cutter geometric parameters. The test data show the RBF model has a low prediction error of 11.11%. Finally, the FA is utilized to search the optimal parameter-level combination. Validation experiments show that, compared with the original GRA, the proposed method further greatly improves the surface roughness and residual stresses in both directions by 1.20 μm, 249.1 MPa, and 176.5 MPa, respectively. The developed approach is proved to be feasible and can be generalized for other multi-objective optimization in manufacturing industry.     

Modeling and optimization of hard turning of X38CrMoV5-1 steel with CBN tool: Machining parameters effects on flank wear and surface roughness

The present study, aims to investigate, under turning conditions of hardened AISI H11 (X38CrMoV5-1), the effects of cutting parameters on flank wear (VB) and surface roughness (Ra) using CBN tool. The machining experiments are conducted based on the response surface methodology (RSM). Combined effects of three cutting parameters, namely cutting speed, feed rate and cutting time on the two performance outputs (i.e. VB and Ra), are explored employing the analysis of variance (ANOVA). Optimal cutting conditions for each performance level are established and the relationship between the variables and the technological parameters is determined using a quadratic regression model. The results show that the flank wear is influenced principally by the cutting time and in the second level by the cutting speed. Also, it is that indicated that the feed rate is the dominant factor affecting workpiece surface roughness.     

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.     

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 .