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.     

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.     

Free abrasive wire saw machining of ceramics

Currently, many kinds of ceramics are used in advanced industrial fields due to their superior mechanical properties, such as thermal, wear, corrosion resistance, and lightweight features. Wire saw machining ceramic (Al₂O₃) was investigated by ultrasonic vibration in this study. Taguchi approach is a powerful design tool for high-quality systems. Material removal rate, wafer surface roughness, steel wire wear, kerf width, and flatness during machining ceramic were selected as quality character factors to optimize the machining parameters (swinging angle, concentration, mixed grain and direction of ultrasonic vibration) to get the larger-the-better (material removal rate) and the smaller-the-better (wafer surface roughness, steel wire wear, kerf width and flatness) machining characteristics by Taguchi method. The results indicated that wire swinging produces a higher material removal rate and good wafer surface roughness. Ultrasonic vibration improved material removal rate, without affecting the flatness under different machining conditions. Experimental results show that the optimal wire saw machining parameters based on grey relational analysis can be determined effectively and material removal rate increases from 2.972 to 3.324 mm²/min, wafer surface roughness decreases from 0.37 to 0.34 μm, steel wire wear decreases from 0.78 to 0.77 μm, kerf width decreases from 0.352 to 0.350 mm, and flatness decreases from 7.51 to 7.22 μm are observed.     

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.     

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.     

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.     

Intelligent optimization and selection of machining parameters in finish turning and facing of Inconel 718

The heat-resistant super alloy material like Inconel 718 machining is an inevitable and challenging task even in modern manufacturing processes. This paper describes the genetic algorithm coupled with artificial neural network (ANN) as an intelligent optimization technique for machining parameters optimization of Inconel 718. The machining experiments were conducted based on the design of experiments full-factorial type by varying the cutting speed, feed, and depth of cut as machining parameters against the responses of flank wear and surface roughness. The combined effects of cutting speed, feed, and depth of cut on the performance measures of surface roughness and flank wear were investigated by the analysis of variance. Using these experimental data, the mathematical model and ANN model were developed for constraints and fitness function evaluation in the intelligent optimization process. The optimization results were plotted as Pareto optimal front. Optimal machining parameters were obtained from the Pareto front graph. The confirmation experiments were conducted for the optimal machining parameters, and the betterment has been proved.     

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.     

Experimental investigation on the electrochemical machining of 00Cr12Ni9Mo4Cu2 material and multi-objective parameters optimization

Special stainless steel 00Cr12Ni9Mo4Cu2 has multiple composition and inhomogeneous tissues; short circuiting will frequently occur when using conventional electrolyte processing. This article analyzes the reason why the process of machining is difficult from the material composition and structure. We used the NaNO₃ and NaClO₃ electrolyte composite to select the appropriate concentration, and then by using the orthogonal experiment and gray relational analysis method, we discussed how the voltage, feed speed, and electrolyte pressure solved the problem of the material removal rate (MRR), surface roughness (SR), and side gap. Under optimal conditions of 20 V, an electrolyte composite concentration of 178 g/l NaNO₃ and 41 g/l NaClO₃, a feed rate of 0.7 mm/min, and an electrolyte pressure of 0.8 MPa, a material removal rate of 100.8 mm3/min, a surface roughness of Ra 0.8 μm, and a side gap of 0.16 mm were produced. Given the same voltage, with an increasing cathode feed rate, the MRR was shown to increase while the surface roughness value and the side gap decreased. Under the same cathode feed rate, the MRR decreases, while the side gap and the surface roughness increase as the electrochemical machining application voltage increases. This study proves that using a certain concentration of electrolyte composite is a simple, low-cost, and feasible approach in improving efficiency and quality.     

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.     

Improving surface integrity in finish machining of Inconel 718 alloy using intelligent systems

In machining of hard materials, surface integrity is one of the major customer requirements which comprise the study of the changes induced to the workpiece. Surface roughness and residual stress are often considered as the most significant indications of surface integrity. Inducing tensile residual stress during the machining processes is a critical problem which should be avoided or minimized to obtain better service quality and component life. This problem becomes more evident in the presence of rough machined surface because fatigue life of manufactured components might be decreased significantly. Inconel 718 superalloy is one of the hard materials used extensively in the aerospace industries. It is prone to tensile residual stress in machined surface. Thus, controlling and optimizing residual stress and surface roughness in machining of Inconel 718 are so needed. Intelligent techniques based on the predictive and optimization models can be used efficiently for this purpose. In this study, the optimal machining parameters including cutting speed, depth of cut, and feed rate were accessed by intelligent systems to evaluate the state of residual stress and surface roughness in finish turning of Inconel 718. The results of experiments and analyses indicated that implemented techniques in this work provided a robust framework for improving surface integrity in machining of Inconel 718 alloy. It was shown that cutting speed has more effect on surface integrity than other investigated parameters. Also, depth of cut and feed rate were found in the moderate range to obtain satisfactory state of tensile residual stress and surface roughness.     

Soft computing models based prediction of cutting speed and surface roughness in wire electro-discharge machining of tungsten carbide cobalt composite

This paper reports on an experimental investigation of small deep hole drilling of Inconel 718 using the EDM process. The parameters such as peak current, pulse on-time, duty factor and electrode speed were chosen to study the machining characteristics. An electrolytic copper tube of 3 mm diameter was selected as a tool electrode. The experiments were planned using central composite design (CCD) procedure. The output responses measured were material removal rate (MRR) and depth averaged surface roughness (DASR). Mathematical models were derived for the above responses using response surface methodology (RSM). The results revealed that MRR is more influenced by peak current, duty factor and electrode rotation, whereas DASR is strongly influenced by peak current and pulse on-time. Finally, the parameters were optimized for maximum MRR with the desired surface roughness value using desirability function 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.     

Deformation behaviour of sintered silicon–copper steel preforms of various aspect ratios during cold upsetting

This paper presents the optimization of the face milling process of 7075 aluminum alloy by using the gray relational analysis for both cooling techniques of conventional cooling and minimum quantity lubrication (MQL), considering the performance characteristics such as surface roughness and material removal rate. Experiments were performed under different cutting conditions, such as spindle speed, feed rate, cooling technique, and cutting tool material. The cutting fluid in MQL machining was supplied to the interface of work piece and cutting tool as pulverize. An orthogonal array was used for the experimental design. Optimum machining parameters were determined by the gray relational grade obtained from the gray relational analysis.     

Optimization of Inconel 718 alloy welds in an activated GTA welding via Taguchi method, gray relational analysis, and a neural network

The purpose of this work is to optimize the weld bead geometry of Inconel 718 alloy gas tungsten arc (GTA) welds that are coated with activating flux before welding. In order to obtain the optimal welding parameters with multiple quality characteristics (QCs) such as penetration and depth-to-width ratio (DWR) of weld bead, the Taguchi method (TM), gray relational analysis (GRA), and a neural network (NN) are employed in this work. The TM is first used to construct a database for the NN. The GRA is adopted to solve the problem of multiple QCs. The gray relational grade (GRG) obtained from the GRA is used as the output of the backpropagation (BP) NN. Then, a NN with the Levenberg–Marquardt BP (LMBP) algorithm is used to provide the nonlinear relationship between welding parameters and GRG of each specimen. The optimal parameters of the activated GTA welding process are determined by simulating parameters using a well-trained BPNN model. The experimental procedure of the proposed approach not only improves the DWR of weld bead but also increases the penetration of Inconel 718 alloy welds.     

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.     

Optimizing machining parameters of silicon carbide ceramics with ED milling and mechanical grinding combined process

A novel combined process of machining silicon carbide (SiC) ceramics with electrical discharge milling and mechanical grinding is presented. The process is able to effectively machine a large surface area on SiC ceramics with a good surface quality. The effect of tool polarity on the process performance has been investigated. The effects of peak current, peak voltage, pulse on-time and pulse off-time on the material removal rate (MRR), electrode wear ratio (EWR), and surface roughness (SR) have been investigated with Taguchi experimental design. The mathematical models for the MRR, EWR, and SR have been established with the stepwise regression method. The experiment results show that the MRR, EWR, and SR can reach 46.2543 mm³/min, 20.7176%, and 0.0340 µm, respectively, with each optimal combination level of machining parameters.     

Experimental investigation and performance analysis of cemented carbide inserts of different geometries using Taguchi based grey relational analysis

In this work, effect of machining parameters cutting speed, feed rate and depth of cut, geometrical parameters cutting insert shape, relief angle and nose radius were investigated and optimized using Taguchi based grey relational analysis. 18 ISO designated uncoated cemented carbide inserts of different geometries were used to turn practically used automotive axles to study the influence of variation in carbide inserts geometry. Performance measures viz., flank wear, surface roughness and material removal rate (MRR) were optimized using grey relational grade, based on the experiments designed using Taguchi’s Design of Experiments (DoE). A weighted grey relational grade is calculated to minimize flank wear and surface roughness and to maximize MRR. Analysis of variance shows that cutting insert shape is the prominent parameter followed by feed rate and depth of cut that contributes towards output responses. An experiment conducted with identified optimum condition shows a lower flank wear and surface roughness with higher MRR. The confirmation results obtained are confirmed by calculating confidence interval, which lies within the width of the interval.     

Modeling and Optimization of Machining Process in Discontinuously Reinforced Aluminium Matrix Composites

This article presents development of an Artificial Neural Networks (ANN) based model for the prediction of surface roughness during machining of composite material using Back Propagation algorithm. Statistically designed experiments based on Taguchi method were carried out on machining of Al/SiCp composite material. The experimentation helped generate a knowledge base for the ANN system and understand the relative importance of process, tool and work material dependent parameters on the roughness of surface generated during machining. The ANN model trained using the experimental data was found to predict the surface roughness with fair accuracy. An optimization approach was also proposed to obtain optimal cutting conditions that yield the desired surface roughness while maximizing the metal removal rate.