The effects of the process parameters in electrochemical machining on the surface quality

Use your smartphone to scan this QR code and download this article ABSTRACT Based on the number of previous studies, this study aims to investigate the effects of process parameters of an Electrochemical Machining process which are electrolyte concentration, voltage applied to the machine, feed rate of the electrode and Inter-Electrode Gap between tool and work piece. Aluminum samples of 25 mm diameter x 25 mm height and 30mm diameter x 25mm height of the tool is made up of copper with a circular cross section with 2 mm internal hole. The design of the system is based on the Taguchi method. Here, the signal-to-noise (S/N) model, the analysis of variance (ANOVA) and regression analyses are applied to determine optimal levels and to investigate the effects of these parameters on surface quality. Finally, the experiments that use the optimal levels of machining parameters are conducted to verify the effects of the process parameters to the surface quality of the products. The results pointed a set of optimal parameters of the ECMprocess. The Inter-Electrode Gap between tool andwork piece has extremely effected on these Material Removal Rate and surface roughness. The Material Removal Rate increases with diseases in Inter-ElectrodeGap, and Ra diseaseswith diseases in Inter-ElectrodeGap. The experimental results show that maximum Material Removal Rate have obtained with electrolyte concentration at 100 g/l, feed rate at 0.0375 mm/min, voltage at 15V, and Inter-Electrode Gap at 0.5mm. The minimum Ra have obtained with electrolyte concentration at 80 g/l, feed rate at 0.0468 mm/min, voltage at 10V, and Inter-Electrode Gap at 0.5mm. This results has led to need studies on these parameters in Electrochemical Machining which are improving productivities and surface roughness of the products.


INTRODUCTION
In recent years there are a large of advanced new materials and alloys which have been discovered but they are difficult to machine such as super alloys, alloys steel, tool steel, and stainless steel with conventional machining methods 1 . This demands leads to several problems, and some feasible solutions would be solved in the future. Thus, new machine methods must be taken to mitigate the problems of urgent demands that they are beneficial methods called Non -Traditional Manufacturing (NTMPs). And, Electrochemical Machining (ECM) is one of the widely used Non -Traditional Machining processes. ECM principle is based on the phenomenon of electrolysis, whose laws were established by Faraday in 1833. "Faraday believes that if two conductive poles are placed in a conductive electrolyte bath and energized by a current, metal may be depleted from the positive pole (anode) and plated onto the negative pole (cathode)" 1 . The first law states that the amount of electrochemical dissolution or deposition is proportional to amount of charge passed through the electrochemical cell, which may be descried as in (1): Where: m -Mass of material dissolved or deposition; Q -Amount of charge passed And, the second of Faraday law states that the amount of material deposited or dissolved further depends on Electrochemical Equivalence of the materials that is again the ratio of the atomic weight and valency, which may be showed as in (2): Where: m = weight of a material (g). I = Current (A). t = machining time (sec). ε = gram equivalent weight of the material. ECM equipment consists of three sub -equipment: machining setup, control unit and electrolyte circulation system. ECM process is performed without physical contact between the tool and the work -piece in contrast to the mechanical machining, and without strong heating in the machining zones in distinction to the methods like Electrical Discharge Machining -EDM. Therefore, no surface metal layer with mechanical distortion, comprehensive stresses, cracks, and thermal distortion forms in ECM. Besides, the numbers of these advantages of this process which are its applicability regardless of material hardness, no tool wear, high material removal rate and production of components of complex geometry. Despites these advantages it has been developed and applied in aerospace, aeronautics, defence, medical industries and other industries 1-3 . It is true that surface quality has become significant because of increased quality demands. Moreover, surface roughness is one of major quality attributes of ECM products beside material removal rates, accuracy and performance of machining. Hence, a lots of investigations have attempted the study of the effects of multiple machining parameters on surface roughness. The effects of a pulsating electrolyte during the electrochemical machining process on surface roughness and material removal rate have been successfully studied through experimentations, and obtained lower surface roughness and higher material removal rate on Ti6Al4V sample machined by ECM. Tool is applied to investigate the MRR, overcut diameter and overcut depth of AISI P20 work -piece. Four parameters were chosen as process variables: feed rate, voltage, electrolyte concentration and tool diameter. From these results, MRR increase with increasing the feed rate, voltage and electrolyte concentration but decreases with increasing the tool diameter. Both overcut and over depth which are increasing with increasing feed, voltage, and electrode diameter but decreases with increasing electrolyte concentration 10 . This paper deals with the effects of these parameters and optimization of the ECM process based on Taguchi techniques. From previously literatures, in this work two contradicting response parameters Material Removal Rate (MRR) and surface roughness (R a ) were considered for analysis (MRR is to be maximized and R a is to be minimized). There are consists of four input parameters which are electrolyte concentration, feed rate, voltage and Inter-Electrode Gap as process variables and Aluminium (Al) were machined by electrochemical machining process.

EXPERIMENTAL PROCEDURES
Experiments are conducted on ECM equipment as in Figure 1 and based on Taguchi's design of experiments.

Figure 1: ECM Machine
As above introduction tab, ECM setup in experiment consists of control panel, machining chamber, and electrolyte system. The work-piece is located in a safety box and to be fixed inside the chamber and a tool is attracted to the main crew which driven by a stepper motor. Applied voltage and feed rate which are controlled by control panel. And, Aluminum samples of 25 mm diameter x 25 mm height and 30mm diameter x 25mm height of the tool is made up of copper with a circular cross section with 2 mm internal hole. Figure 2 shown dimensions of a tool and work -piece. Electrolyte to be able to through the central hole of 2mm of the tool and into machining zones. Figure 3 shown experiment setup.  Based on Rebecca and Ivanov (2016) 11 NaCl solution is chosen as electrolyte, because it has no passivation effect on the surface of the job. Reference 1 electrolyte concentration is selected in the range of 80-100g/lit. Because low voltages lead to low material removal rate and high surface roughness in electrochemical machining process 9 . Thus, applied voltage in ECM process it is possible to vary range of from 5 to 30 V and feed rate from 0.2 mm/min to 2 mm/min 9 . But, they depend on experiments conditions, applied voltage the range of 10-20V and the range of feed rate from 0.0375-0.0562mm. The smaller the inter-electrode gap, the smaller the applied potential has to be reach the machining potential as the ohmic drop caused by the electrolyte resistance is reduced 11 . Thus, IEGs are selected in the range of 0.5-1.  Surface Roughness (ISO 1997, λ = 0.8, µm). The responses MRR calculated by following (3): m a : mass of Work -piece before machining (gram) m b : mass of Work -piece after machining (gram) t: machining time (min)

METHODOLOGYS Regression analysis
Regression analysis is a statistical tool for estimating the relationships among variables. Regression analysis helps one understand how the typical value of the dependent variable changes when any one of the independent variables is varied. It is also used to understand which among the independent variables are related to the dependent variable and to explore the forms of these relationships 11,13 . The general form of a multiple regression model is as follows: Where b1, b2, b3,… are estimates of the independent variables 1, 2, 3, … and ε is the error.

Taguchi Method
One of the advantages of the Taguchi method is that it uses a special design of orthogonal arrays to study the scope of a research project or the entire parameter space with a small number of experiments 11 . From results, Taguchi method allows for the analysis of many different parameters without a prohibitively high amount of experimentation. The S/N ratio for the Larger -to -better is given Taguchi as (5): Where: y -observed data. n -number of observations. The S/N ratio for the Smaller -to -better is given Taguchi as (6):

Analysis of Variance (ANOVA)
Analysis of variance (ANOVA) is a potential technique used to study the significance of the all parameters and their interactions by comparing the mean square with an estimate of the experimental error at a specific confidence level 5,9 . In present paper, ANOVA is performed using Minitab 18. The relative influence of the parameters is measured by total sum of square value (SST) by following (7): Where n is the number of experiments in the orthogonal array, n i is the mean S/N ratio for the i th experiment and n m is the total mean S/N ratio of all experiments. The percentage contribution P can be calculated as:

SI83
Where, SS d is the sum of squared deviations. Further, the Fisher's F-ratio, the ratio between the regression mean square and the mean square error, is used to identify the most significant factor on the performance characteristic.
The P-value reports the significance level (suitable and unsuitable). Percent (%) represents the significance rate of the machining parameters on the response.

Effects on MRR
From experiment results, the machinability of ECM depends on electrolyte concentration, feed rate, voltage and IEG. The influence of various machining parameters on MRR is shown in Figure 5. The Inter-Electrode Gap between tool and work -piece has ex-SI84 tremely effect on MRR and it increases with decreases in IEG. And then voltage, and then feed rate, and then feed rate. And, regression models for MRR are decried by (9): MRR=1.6562+0.00039A-0.00611B+0.00283C 0.10389D (9) In Table 3, ANOVA of MRR is presented with all the terms. After eliminating interaction of process parameter like B*C, B*D, and C*D. It can be proving that electrolyte concentration NaCl, feed rate, voltage, and Inter-Electrode Gap effects on MRR by 0.039%, 0.4%, 0.75% and 93.56%, respectively. In Table 4 showed the optimal machining performance for the Electrolyte concentration level 100g/l (level 3), Feed rate 0.0375mm/min (level 1), Voltage 15V (level 2), IEG 0.5mm (level 1). In which there IEG is important and then voltage, and then feed rate and then electrolyte concentration. The estimated model coefficients for SN ratios are shown in Table 5. Parameter results are standard deviation of error S = 0.0682, amount of variation R 2 = 99.63% and R 2 (adj.) = 98.40%. And comparing the P value is less than or equal to 0.05 it can be concluded that the effect is significant, otherwise is not significant.
The residual plots of MRR is showed in Figure 6. The residual plot in the graph for normal probability plot indicate the data are normally distributed and variables are influencing the response. Standardized residues are between 0.08 and 0.08. The residuals versus fitted value indicate the variation is constant. The histogram proved the data are not normally distributed it may be due to the fact that the number of points are very less. Residual versus order of the data indicates that there are systematic effects in the data due to data collection order.

Effects on Ra
From experiment results, the machinability of ECM depends on electrolyte concentration, feed rate, voltage and IEG. The influence of various machining parameters on the surface roughness (Ra) is shown in Figure 7. The Inter-Electrode Gap between tool and work -piece has extremely effect on R a and it increases with decreases in IEG. And then voltage, and then feed rate, and then feed rate. And, regression models for Ra are decried by (10): Ra = 4.437+0.417A+0.39511B+0.084C-0.303D (10) In Table 6, ANOVA of Ra is presented with all the terms. After eliminating interaction of process parameter like B*C, B*D, and C*D. It can be proving that electrolyte concentration NaCl, feed rate, voltage, and Inter-Electrode Gap effect the Surface Roughness by 0.15%, 16%, 0.42% and 39.31%, respectively. In Table 7 showed the optimal machining performance for the Electrolyte concentration level 80g/l (level 1), Feed rate 0.0468mm/min (level 2), Voltage 10V (level 1), IEG 0.5mm (level 1). In which there IEG is important and then feed rate, and then voltage and then electrolyte concentration. The estimated model coefficients for SN ratios are shown in Table 8. Parameter results are standard deviation of error S = 0.4297, amount of variation R 2 = 94.02% and R 2 (adj.) = 74.11%. And comparing the P value is less than or equal to 0.05 it can be concluded that the effect is significant, otherwise is not significant.
The residual plots of MRR is showed in Figure 8. The residual plot in the graph for normal probability plot indicate the data are normally distributed and variables are influencing the response. The residuals versus fitted value indicate the variation is constant. The histogram proved the data are not normally distributed it may be due to the fact that the number of points are very less. Residual versus order of the data indicates that there are systematic effects in the data due to data collection order.

CONCLUSIONS
In the present study, four factors are considered electrolyte concentration, feed rate, voltage and Inter-Electrode Gap . Aluminium as a Work -piece and 27 experiments conducted to obtain an optimum level in achieving high material removal rate and minimum surface roughness. And, to determine effect levels on two outputs. The IEG between tool and workpiece has extremely effect on MRR and it increase with diseases in Inter-Electrode Gap. And then voltage, and then Feed rate, and then electrolyte concentration. Among the four process parameters, The IEG between tool and workpiece influences highly on surface roughness and it diseases with diseases in Inter-Electrode Gap. Follwed by feed rate, and then electrolyte concentration, and then voltage Form results: 1. Maximum MRR at Electrolyte concentration level 100g/l (level 3), Feed rate 0.0375mm/min (level 1), Voltage 15V (level 2), IEG 0.5mm (level 1).