Proceedings of the International Conference on Advances in Production and Industrial Engineering

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1 Proceedings of the International Conference on Advances in Production and Industrial Engineering Prediction of Material Removal in Electro Chemical Machining using Multiple Regression Analysis N.Manikandan 1*, Somasundaram Kumanan 2, C.Sathiyanarayanan Department of Production Engineering, National Institute of Technology, Tiruchirappalli India Abstract Electrochemical Machining (ECM) has the capability of machining intricateforms in electrically conductive materials independent of its hardness. Making decision is increasingly fractious in the manufacturing domain due to need of products with high quality and rapid alteration in design. This paper proposes the development of multiple regression models for prediction of material removal rate (MRR) in electrochemical machining. Trials are designed as per Taguchi s principles and mathematical equations are established using multiple regression model. Taguchi s approach can be applied as a single objective optimization technique to attain the best possible combination of process parameter for material removal rate.analysis of variance (ANOVA) is applied for determining the significance of process parameters which affects the MRR. The evolved method for prediction of material removal rate is flexible, competent, and accurate than existing models and it scopes better monitoring system. Finally the evolved models are validated. The predicted results which are based on the developed models observed to be in good agreement with the experimental outcomes. Keywords: Electrochemical machining (ECM), material removal rate (MRR), Prediction, Taguchi s approach, ANOVA,Multiple regression analysis Introduction Electrochemical Machining (ECM) is an unconventional machining processes acts on the principle of electrochemical dissolution proposed by Faraday (2005). The ECM has ample applications in manufacturing industries (1999) and is characterized by machining of hard to machine materials with no tool wear. The rate of machining is independent of hardness of the work material. The typical ECM Setup is shown Figure 1. The cutting tool is directed along the desired path nearer to the work piece by non-contact mode. Flow rate of electrolyte is customized by flow control valve. Machining is achieved by sinking of tool into workpiecefor forming its replica in the workpiece. The important input process parameters are tool feed rate, electrolyte flow rate and electrolyte concentration. These parameters influence the Material Removal (MRR). Silva Neto et al. (2006) studied about theintervening variables in ECM. The MRR, roughness of the machined surface and over-cut has been analyzed. Feed rate, electrolyte, electrolyte flow rate and voltage are the parameters considered during experiments. Various electrolyte solutions were used for experiments namely Sodium nitrate (NaNO3) and Sodium chloride (NaCl). Outcomes of the experiments proved thatfeed rate was the significantparameter which affects the MRR.Klocke et al. (2013)performed an experimental research on the ECM of advanced titanium and nickel based alloys. During the study an analysis was made about the electrochemical machinability of those materials which are used for aero engine components. Nickel based alloys are most used materials in the industries due to its inherent properties and advantages than titanium-based alloys. Heat resistant and retaining the properties at high temperature, high corrosion resistance and high melting temperature are some important advantages of nickel-based alloys. [Wu(2007), Saoubi et.al (2008) Guo et.al (2009)]. Incoloy 800HT is comprehensively used in heat treating component such as baskets, fixtures and trays due to its better corrosion resistance properties. Furthermore this material has the applications in petroleum processing industries as the material for heat exchanger (Yilbaset al.1999). Nickel based super alloys executes a highly important role in aerospace applications, engines for rockets, nuclear reactors, thermal power plants, submarines and high temperature applications.since these alloys are difficult to machine using the traditional methods, the nontraditional methods have been proposed to machine these alloys.

2 Proceedings of the International Conference on Advances in Production and Industrial Engineering Figure 1 - Schematic diagram of electrochemical machining The complication of machining of these kinds of hard to machine materials by conventional process leads to the development of ECM process. ECM is one of the nontraditional processes that has been used to machine nickel alloys. Ali et al. (2009) discussed about the shaped tube electrochemical machining.taguchi s design approach and ANOVA were used to identify the important process parameters that affect the quality of hole.coteaja et al. (2008) et al. derived a mathematical model to reveal the importance of process parameters on performance measures namely MRR, electrode tool wear and accuracy of machined hole. UlasCaydas and AhmetHascalık (2008) made an experimental study to predict the roughness of machined surface in abrasive water-jet machining (AWJ) process using artificial neural network (ANN) and regression model. The results proved that the developed regression model performed better while comparing the ANN model. Ashok Kumar Sahoo and SwastikPradhan (2013) presented a study on machining of aluminium metal matrix composites by Taguchi s approach. Mathematical models were evolved for roughness of the machined surface and flank wear. It is observed from the study that the developed mathematical models are found to be statistically significant for predicting the process parameters. Ahilan et al. (2013) developed a mathematical for CNC turning process by applying advanced decision making methods and concluded that the developed mathematical models are reliable for prediction of various output parameters. Rama Rao et al. (2012) employed the Taguchi methods and ANOVA inecm process parameter optimizationfor MRRand concluded that MRR is increased by increasing feed rate and electrolyte concentration. SaeedZareChavoshi (2009) experimentally investigated about the effect of different input parameters on performance measures namely MRR and over cut.from the investigation it is revealed that MRR is increased while increasing the feed rate. Chakradhar and VenuGopal (2011)presented the multi-objective optimization of the ECM process by the Grey Relational Analysis (GRA) and it has been determined that feed rate is the important process parameter which affects the robustness of ECM.Milan kumar Das et al. (2014) presented an experimental study on electrochemical machining parameters namely electrolyte concentration, inter electrode gap, feed rate and voltage. It is concluded from the results that the concentration of electrolyte influences the MRR and roughness of the machined surface. Tang and Yang (2013) detailed an experimental study on electrochemical machining of stainless steel by using various electrolytes and concluded that the MRR is increased with increase in feed rate of the tool. In this present investigation, an attempt was made to establish the significance of machining parameters for the MRR and alsothe regression models wereevolved to predict the MRR. ANOVA has been applied to identify influence of the ECM process parameter which affects the MRR. Taguchi s single objective optimization method is used to optimize the material removal rate in electrochemical machining. The comparison is made between the predicted values which were determined by the developed regression model and the experimental results also discussed. 2. Experimentation Incoloy 800HT was the work material under this investigation. Typical chemical of workpiece is shown in Table 1. INCOLOY 800HT is used extensively in aerospace such as jet engine and airframe components, power plants, industrial furnace, pressure vessels, chemical and petrochemical processing industries. Taguchi s design approach is used to plan the experiments. Total of three machining parameters namely feed rate (A), flow rate electrolyte (B), concentration of electrolyte (C) were selected. Based on selected process parameters levels, an L 27 orthogonal array was employedto conduct experiments and the values taken for the analysis purpose.the trials were performed using ELECHEMTECHNIK Electrochemical Machine, based on L 27 orthogonal array. The ECM setup comprises of machining chamber,control panel,electrolyte circulation system and designed to work at a constant voltage of 24 V. The workpiece is fixed in the vice provided inside of the machining chamber and tool is connected to the main screw which is driven by a stepper motor. For avoiding short circuits, a current sensing circuit is connected between the tool and the stepper motor controller circuit. If the current get increased than the limit, a signal is sent to the stepper motor circuit which immediately stops the downward movement of the tool. The input process parameters namely tool feed rate, flow rate of electrolyte and concentration of electrolyte were varied. The electrolyte flow rate was in the range

3 Proceedings of the International Conference on Advances in Production and Industrial Engineering of lit/min through an inter electrode gap. Brass is the tool materials with a circular cross section of 6 mm diameter with an internal hole of diameter 4 mm. The tool was insulated excludingthe base of the tool which is exposed to the workpiece. Electrolyte was pumped and circulated through the machining region via the central hole in the tool. The sodium chloride (NaCl) solution has been chosen as electrolyte. The machining was performed to make a hole and MRR was measured using weight loss method. Table 1 Composition of the material Constituents Percentage Ni Fe 39.5 min Cr Cu 0.75 Ti Al C max Mn 1.5 max S max Si 1 max The machining results after ECM drilling are evaluated based on material removal rate. The ECM process parameters and their levels are shown in Table 2. Table 2 Parameters and their levels for electrochemical machining Factor Process parameter Levels A Feed rate (mm/min) B Electrolyte flow (lit/min) C Electrolyte concentration (%) Based on the selected L 27 Orthogonal Array, a number of machining operations were performed in electrochemical machine. The layout for ECM process parameters and outcomes of experiments were tabulated in Table 3. Table 3 Experimental observations S. No Feed Flow Elec. Conc. M.R.R (g/min) Development of Regression model for MRR Regression analysis is a tool used for analyzing statistically the relationship among the variables. It is one of the most generally used techniques during prediction of performance measures. Regression equations have linear model, quadratic model, interaction model and full quadratic model and are as follows (for three input parameters): Linear equation: y=β 0 + β 1 X 1 + β 2 X 2 + β 3 X 3 (1) Quadratic equation: y=β 0 + β 1 X 1 + β 2 X 2 + β 3 X 3 + β 4 (X 1 ) 2 + β 5 (X 2 ) 2 + β6(x 3 ) 2 (2) Interaction equation: y=β 0 + β 1 X 1 + β 2 X 2 + β 3 X 3 + β 4 X 1 X 2 + β 5 X 1 X 3 + β6x 2 X 3 (3) Second order equation (Full model): y=β 0 + β 1 X 1 + β 2 X 2 + β 3 X 3 + β 4 (X 1 ) 2 + β 5 (X 2 ) 2 + β6(x 3 ) 2 + β 7 X 1 X 2 + β 8 X 1 X 3 + β 9 X 2 X 3 (4) Where y is criterion variable (MRR) X1, X2 and X3 are predictor variables. β 1, β 2, β 3 are regression coefficients. R 2 values for material removal rate is tabulated in Table 4 and the desired full quadratic model is shown in equation. (5)

4 Proceedings of the International Conference on Advances in Production and Industrial Engineering A B C A*A A*B A*C B*B B*C e-005 C*C (5) Table 4 R 2 values for MRR Regression model R 2 value Linear 92.88% Linear + square 94.47% Linear + interaction 97.01% Full model (linear+square+interaction) 98.59% Where A is feed rate, B is flow rate, C is electrolyte concentration.the normal probability plot of the residuals for material removal rate is shown in Figure 2. In a normal probability plot, if all the data points fall near the line, an assumption of normality is reasonable. Figure 2Normal probability plot of the residuals for material removal rate 4. Results and Discussion Experiments have been performed as per L 27 orthogonal array to analyze the importance of input process parameters on material removal rate. An attempt has been taken to determine the best possible process parameters for attaining the effective and competent machining process. In the ECM process, higher material removal rate is the indicator of superior performance.so MRR is considered as higher the better criterion. Determination of optimal combination of process parameters: The Figure3 shows the main effects plot for material removal rate. From the graph it is clear that the MRR increases with feed rate. This is due to the reason that as the electrode advances into the workpiece. Figure 3 Main Effects Plot for MRR of INCOLOY 800HT More amounts of negative ions from the workpiece material start moving towards the electrode and the same is washed away by the electrolyte.figure 3 also shows the influence of electrolyte flow rate on MRR.Electrolyte flow rate does not affect the MRR significantly. It is also confirmed by the results of ANOVA. MRR is increased with increase in electrolyte concentration. This due to the reason that the increase in electrolyte concentration increases the electrochemical reaction and gear up the motion of workpiece ions towards the electrolyte and same is washed by the flow of electrolyte. Table 5 Taguchi Analysis - Response Table for MRR: Larger is better Level Feed Flow Elec. Conc Delta Rank Taguchi analysis response table for MRR is presented in Table 5. In summary, the best possible machining parameters for obtaining higher material removal rate are A 3 B 3 C 3. This means the optimum level for material removal rate is: feed rate 0.20 mm/min, flow rate 0.90 lit/min and Electrolyte concentration is 25%. Feed rate is most contributing parameter, followed by electrolyte concentration and flow rate. Analysis of Variance: ANOVA is statistical tool for identifying factors which are influencing the performance measures in a given data set. The Statistical software Minitab 16.0 is used to examine the influence of process parameters.

5 Proceedings of the International Conference on Advances in Production and Industrial Engineering Table 6 ANOVA for Material Removal Source DF Seq SS Adj MS P % Feed Flow Elec. Conc Error Total The contribution of parameters on material removal rate is identified using ANOVA and presented in Table 6 and it depicts percentage contribution of each factor with respect to MRR. Feed rate is more contributing parameter for MRR followed by electrolyte concentration and electrolyte flow rate as graphically shown in Figure The comparison among the experimental value and predicted values by regression models for MRR are graphically illustrated in figure 5. From the figure it is revealed that the prediction values are nearly close with the experimental values, which confirms the effectiveness of the regression model in machining of Incoloy 800HT.The deviation among the experimental and regression values isperceived to minimal, and thereforeregression has the good capability in modeling of Incoloy 800HT alloys. Figure 5 Comparison of predicted values of MRR with experimental MRR Figure 4Percentage contribution of the ECM process variables on MRR Validation of Developed model:the developed regression model for material removal rate is used for predicting the process parameters. The Table 7 shows comparison among the experimental values and the predicted values by developed regression model. It clearly depicts that the developed multiple regression model predicts the parameters with very less amount of error. Table 7 Validation test results of developed regression model for MRR Process Material removal rate (g/min) parameters A B C By By % Experiments Prediction Error Conclusion In this paper a multiple regression model has been developed for the prediction of material removal rate in electrochemical machining of Incoloy 800 HT. Observations indicates that the regression modeling results of electrochemical machining were in good agreement with experimental findings. R 2 value achieved for full model equation is 98.59%. Comparison and validation of regression model results with experiment findings were verified. Multiple regression model technique could be an economical and successful method for prediction of ECM output parameters according to input variables. From the experimental analysis, the results proved that thefeed rate is most influencing parameter which affects the Material removal rate followed by electrolyte concentration and flow rate. A statistical result shows that the feed rate, electrolyte flow rate and electrolyte concentration affects the material removal rate by 59.05%, 0.79%, 34.6% respectively.

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