MODELLING AND PREDICTION OF MATERIAL REMOVAL RATE IN ELECTRICAL DISCHARGE DIAMOND SURFACE GRINDING PROCESS OF INCONEL-718

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1 5 th International & 26 th All India Manufacturing Technology, Design and Research Conference (AIMTDR 2014) December 12 th 14 th, 2014, IIT Guwahati, Assam, India MODELLING AND PREDICTION OF MATERIAL REMOVAL RATE IN ELECTRICAL DISCHARGE DIAMOND SURFACE GRINDING PROCESS OF INCONEL-718 Harlal Singh Mali 1*, Deepak Unune 2, Sandeep Tiwari 3 1* Assistant Professor, Malaviya National Institute of Technology, Jaipur, , harlal.singh@gmail.com 2 Ph.D. Scholar, Malaviya National Institute of Technology, Jaipur, , deepunune@gmail.com 3 M.Tech Student, Malaviya National Institute of Technology, Jaipur, , stiwari261@gmail.com Abstract Hybrid machining processes (HMP s) are gaining popularityto machine difficult to cut materials with competitive pricing with the conventional ones. After establishing the machining capability of the designed & developed electrical discharge diamond surface grinding (EDDSG) setup aim of this paper is to develop model foreddsg of Inconel-718 for predicting its material removal rate(); one of the parameter of cost effective machining. The effectiveness of the process is also verified through Scanning Electron Microscope (SEM) micrographs before and after the EDDSG. Response Surface Methodology (RSM), employing face centered central composite rotatable design (CCRD) scheme, has been used to plan and analyze the experiments. The mathematical relation between EDDSG input parameters i.e. wheel speed, pulse current, pulse on-time and duty factor with is established. Artificial neural network (ANN) technique is used for modeling purpose along with RSM. Predicted values of by RSM and ANN are in good agreement with the experimental results and will be of direct use by the machining engineer at the shop floor. Keywords:EDDSG, Inconel-718, Response Surface Methodology, Scanning Electron Microscope. 1. Introduction Nickel-based alloys are the most widely used super alloys, accounting for about 50 weight% of materials used in an aerospace engines, mainly in the gas turbine compartment as they maintains high resistance to corrosion, mechanical and thermal fatigue, mechanical and thermal shock, creep and erosion, at elevated temperatures, Ezugwuetal. (2003). Contrary to these superb mechanical properties, machining of these difficult-to-cut materials is one of challenging area in non-conventional machining processes. One of the approaches to overcoming problems of machining of nickel-based alloys to use hybrid machining processes, Feyziet al. (2013). Hybrid machining processes (HMP s) becoming more popular in previous few years as it enhances the advantages and minimizes the potential disadvantages found in the individual techniques. HMP s are based on the simultaneous and controlled interaction of process mechanisms having a significant effect on the process performance. Electro discharge machining (EDM) is one of most popular machining process used by die and mold making industry. Though, discharge pulse energy significantly affects the surface integrity of workpiece material. Micro-cracks and recast layers degradesfatigue strength of material. To overcome surface integrity issues found in EDM, Koshyet al. (1996, 1997) processed Electro discharge diamond grinding (EDDG) which is a hybrid process which combines diamond grinding and EDM for machining of electrically conductive materials. Sanjeev Kumar (2007) stated that the grinding forces are minimized by thermally softening the workpiece material on microscopic scale with aid of the electric spark. Yadavet al (2013) classified, the EDDG setups in three different grinding configurations, which is shown in Fig. 1. These configurations are: Electrical Discharge Diamond Cut-off Grinding (EDDCG), Electrical Discharge Diamond Face Grinding (EDDFG) and Electrical Discharge Diamond Surface Grinding (EDDSG). The EDDCG configuration is used to cut workpiece into pieces or making grooves into workpiece using abrasion and sparking actions at peripheral surface of the grinding wheel. The EDDFG 822-1

2 MODELLING AND PREDICTION OF MATERIAL REMOVAL RATE IN ELECTRICAL DISCHARGE DIAMOND SURFACE GRINDING PROCESS OF INCONEL-718 configuration is used for machining end surface of workpiece using abrasion and sparking at face surface of grinding wheel. The EDDSG configuration is used for machining the flat Figure 1 Different configurations of EDDG process, Yadavet al.(2013) Table 1 Past research studies in EDDG Author ( s) Materials used Type of wheel Machining Parameters Kozak(2002) Polycrystalline diamond Copper bonded Pulse current, pulse on-time (PCD), aluminum diamond wheel and wheel speed silicon carbide (Al/SiC) Koshy et al. (1996) High speed steel (HSS) Bronze Pulsecurrent and wheel bonded diamond wheel speed Koshy et al. (1997) Cemented carbide Bronze Pulse current and pulse bonded diamond wheel on-time Choudhuryet al. HSS Different voltage, (1999) pulse on-time and duty Response parameters Material removal rate () and surface roughness (R a), grinding forces and power consumption. and grinding forces and grinding forces Jain and Mote (1999) temperature and specific energy Yadav et al. (2008) HSS Copper bonded Pulse current, wheel speed diamond wheel and pulse on-time Yadav and Yadava HSS Bronze bonded Pulse current, wheel speed and R a (2010) diamond wheel andpulse on-time Singh et al.(2010, 2011, 2012) HSS and WC-Co composite Bronze bonded diamond wheel, R a and wheel wear rate (WWR) Pulse current, pulse on-time, duty factor and wheel speed surface using abrasion and sparking at peripheral surface of grinding wheel [6]. Table 1 summarizes the past experimental work by several researchers who reported EDDCG and EDDSG processes. From the review of literature, it is observed that no plausible studies were conducted on machining new and advanced materials, such as Inconel 718 nickel-based super-alloys using EDDSG process. Thus, this experimental work is attempted here to evaluate the EDDSG characteristics of Inconel 718. Additionally, the EDDSG parameters are modelled using ANN. Predicted values of by RSM and ANN are compared with experimental results. 2. Experimentation: All the experiments were performed on ENC 35 EDM, which is attached with self-designed and fabricated in surface grinding setup. Actual photograph of view of developed EDDSG setup is shown in Fig.2. This setup consists of bronze-diamond abrasive grinding wheel, direct current motor, shaft, V-belt and bearing, alternating current reversible synchronous motor, as shown in Fig.3, schematic diagram of EDDSG). The bronze diamond wheel is attached with the ram of the machine through special attachment and the servo system of the ZNC EDM machine was used to maintain the predetermined gap between the two electrodes i.e. the diamond wheel and the flat work piece which is mounted on the fixture and which in turn is clamped on 822-2

3 5 th International & 26 th All India Manufacturing Guwahati, Assam, India Technology, Design and Research Conference (AIMTDR 2014) December 12 th 14 th, 2014, IIT the EDM machine table. The detail of metal bonded diamond wheel is given in Table 2. The width of the gap between wheel and work piece depends upon the local breakdown strength of dielectric that is selected for a particular gap voltage setting. The design consideration of some of the critical components has been selected by keeping the different important factor. The function of the shaft is to rotate the grinding wheel and is itself rotating. So its design requires some of the input parameters like material, diameter, RPM, motor power. Keeping all the input factors 19 mm shaft diameter, EN 24 as shaft material and bearing whose ISI No is selected. A permanent magnet direct current motor (PMDC) of 2 HP and 4600 rpm is used to drive the grinding wheel. The V shaped belt (13 mm x 9 mm) is used to transmit power from driver to driven pulley. The V belt has trapezoidal cross section so that it remains in touch with the side of the pulley to avoid slip. The PMDC motor speed can be controlled through a direct current drive. The relative motion between the diamond abrasive wheel and the work piece is achieved by reciprocating the machine table through an automatic feed arrangement. For this purpose alternating current reversible synchronous motor is attached with machine table lead screw through special attachment. This synchronous motor is controlled with 2 way control switches. Suppose the motor is revolving clock wise direction and due to which the table is moving in one direction. As the control stick attached to EDM table strikes with 2 way control switches, motor polarity will change and the motor starts revolving in anticlockwise direction and in turn EDM table will move in opposite direction. All the experiment is conducted in surface grinding mode. Inconel 718 is selected as the workpiece material for experimentation. It is flat and rectangular in shape. The composition, as shown in table 3, of Inconel 718 has been found after conducting the Optical Emission Scanning (OES) test. The wheel speed, pulse current, pulse on-time and duty factor are taken as control factors while is taken as performance parameters. The value of each input parameter has been decided on the basis of pilot experimentation. The actual values of control factors are given in Table 4. The experiments were performed according to the central composite rotatable design (CCRD) which requires minimum three levels of each factor [42]. In present case three level of four control factors with seven central point of standard CCRD matrix has been selected, which requires total 31 experimental runs with 2 p factorial runs, 2*p axial runs and seven center points (where p is the number of control factor). Therefore, total number of runs = *4+7 = 31. The experimental matrix (coded variables) and experimental results are shown in Table 5. All the experiments weree performed on the surface of a 60 mm x 10mm workpiece of Inconel-718 for 30 minutes. High Precision Electronic digital balance (WENSAR HPB-310 Model) with readability of 1 mg is used to calculate weight difference before machining and after machining. The grinding wheel speed was measured by digital tachometer in revolution per minute. Figure 2 Photograph of EDDSG setup Table 2 Details of metal bondedd grinding wheel Description Diamond Wheel Diameter Thickness Abrasive Used Bore Diameter Bonding Material Concentration Grit Size Deapth Of Indentation Specification 100 mm 10 mm Diamond 32 mm Bronze 75% 80/100 3 mm Figure 3: Schematic Diagram of in-house-designed and fabricated Powder Mixed Electro Discharge Diamond Surface Grinding frame-up 822-3

4 MODELLING AND PREDICTION OF MATERIAL REMOVAL RATE IN ELECTRICAL DISCHARGE DIAMOND SURFACE GRINDING PROCESS OF INCONEL-718 Table-3 Chemical composition of Inconel-718 Element Mo Co Al C 165 Si 251 Mn 403 P 185 Cr 267 Fe 406 V 411 Nb 405 W 429 B 182 Ti 337 Al 308 Zr 339 Ni 216 % Development of mathematical model: 3.1 Response surface methodology Response surface methodology (RSM) is a collection of mathematical and statistical technique useful for analyzing problems in which several independent variables influence a dependent variable or response and the goal is to optimize the response. In many experimental conditions, it is possible to represent independent factors in quantitative form as given in Eq. (1). Then these factors can be thought of as having a functional relationship or response as follows:,,, (1) Between the response and,,, of k quantitative factors, the function φ is called response surface or response function. The first order polynomial (regression) equation used to represent the response surface Y is given by [15] (2) After determining the significant coefficients (at 95% confidence level), the final model was developed using only these coefficients and the final mathematical model to estimate is given by: (3) 3.2 Checking adequacy of model The adequacy of the developed model was tested using the analysis of variance (ANOVA) technique and the results of second order response surface model fitting in the form of analysis of variance (ANOVA) are given in Table 6. The determination coefficient (R 2 ) indicates the goodness of fit for the model. In this case, the value of the determination coefficient (R 2 = ) indicates that only less than 3% of the total variations are not explained by the model. The value of adjusted the goodness of fit for the model. In this case, the value of the determination coefficient (R 2 =0.7927) indicates 20% of the total variations are not explained by the model. The value of adjusted determination coefficient (adjusted R 2 =0.7779) is also high and close to determination coefficient, which indicates a high significance of the model. Predicted R 2 is also in a good agreement with the adjusted R 2. The value of probability >F in Table 6 for model is less than 0.05, which indicates that the model is significant. In the same way, wheel speed (S), current (C) have significant effect. Lack of fit is non-significant Normal probability % Normal Probability Plot (response is ) Residual as it is desired. The normal probability plot of the residuals for tensile strength shown in Fig.4 reveals that the residuals are falling on the straight line, which means the errors are distributed normally. All the above consideration indicates a good adequacy of the regression model. Table 4: Experimental input parameter and their levels Input factor s Sym bol Coded level Description Unit Wheel speed rpm S Current A C Pulse on Time µs P Duty Factor % DF Figure 4: Normal probability plot of residuals for 4. Modeling of EDDSG process using ANN In present paper, ANN architecture with backpropagation algorithm has been selected for the modeling of EDDSG process. The ANN modeling of any processis completed in three phases: training,

5 5 th International & 26 th All India Manufacturing Technology, Design and Research Conference (AIMTDR 2014) December 12 th 14 th, 2014, IIT Guwahati, Assam, India testing and validation using a code developed in MATLAB. The ANN architecture was trained with experimental data, which is shown in Table 3.For testing of the network, a separate set of 70% numbers of experimental results were used. The experimental results used for testing is shown in Table 4. The network was trained using Levenberg-Marquardt (LM) back-propagation algorithm for fast supervised learning. LM is a network training function that updates weight and bias values according to Levenberg-Marquardt optimization. It is constituted by selecting numbers of hidden layer and optimum number of neuron in each hidden only. The number of hidden layer and number of neurons in each hidden layer were selected on the basis of performance of network after exhaustive trails. The performance of network was measured in term of mean square error (MSE). The MSE of network have been calculated as: (4) Table 5 Experimental Layout as Per CCRD showing Coded Input Parameters and observed, and predicted by RSM and ANN method RunO rder S C P DF Experimental (mm 3 /min) RSM predicted ANN predicted % Error Between Expt. and RSM Expt. and ANN Table-6: ANOVA results for model (only significant terms) Source Sum of squares df Mean square F value p-value probability > F Model Linear

6 MODELLING AND PREDICTION OF MATERIAL REMOVAL RATE IN ELECTRICAL DISCHARGE DIAMOND SURFACE GRINDING PROCESS OF INCONEL-718 S C Residual Error Lack-of-Fit Pure Error Total Standard deviation R 2 =79.27 where is the desired output of the j th neuron, is the predicted value of j th neuron, N is the total number of training pattern and M is the number of output nodes. For testing of the network, a separate set of 70% numbers of experimental results were used. The experimental results used for testing is shown in Table 4. The network was trained using Levenberg-Marquardt (LM) back-propagation algorithm for fast supervised learning. LM is a network training function that updates weight and bias values according to Levenberg- Marquardt optimization. It is constituted by selecting numbers of hidden layer and optimum number of neuron in each hidden only. The number of hidden layer and number of neurons in each hidden layer were selected on the basis of performance of network after exhaustive trails. The performance of network was measured in term of mean square error (MSE). The MSE of network have been calculated as: (4) where is the desired output of the j th neuron, is the predicted value of j th neuron, N is the total number of training pattern and M is the number of output nodes. Finally, ANN architecture, as shown in Table 7, with back propagation algorithm has been selected for modeling of the process parameters of EDDSG process, which is capable to predict with absolute average percentage error of2.66 % with maximum percentage error of 29.22% respectively. The experimental and predicted values by RSM and ANN of by summarized in Table 5. The comparison between experimental and predicted value of through graphical presentation is shown in Fig Comparison between RSM and ANN The ANN and RSM models were compared for predictive capability, and both models were trained. The predicted values by the RSM as well the ANN model are tabulated in Table 8. Figure 5 shows the comparative resemblance plot for ANN and RSM predictions for model outputs. The ANN model was fitted to the experimental data for all the outputs with good accuracy. The RSM model shows greater Press Adjusted R 2 =77.79% Predicted R 2 =73.82 deviation than the ANN. Thus, the ANN shows a significantly higher generalization capacity than the RSM. This higher predictive accuracy of the ANN can be attributed to its universal ability to approximate the nonlinearity of the system. 6. Surface topography of EDDSG machined surfaces Surface topographical images taken by Scanning Electron Microscopy of Inconel 718 before machining and at machining conditions of highest are shown in Fig. 6. The un-machined surface shown in the micrograph at Fig. 6(a) indicates the casted / pitted marks which are removed and fresh surface is generated while EDDSG of Inconel 718 at machining condition 1200 rpmwheel speed, 9 Amps current, 150 µspulse on Time and 70% duty factor. Few EDM craters are also seen, which could not be cleaned by the grinding process. Figure 5: Comparison of experimental vs predicted by RSM and ANN Object model: Input neurons Output neuron Network structure Network type: Table 7: ANN parameters S,C,P,DF Feed-forward backpropagation 822-6

7 5 th International & 26 th All India Manufacturing Technology, Design and Research Conference (AIMTDR 2014) December 12 th 14 th, 2014, IIT Guwahati, Assam, India Transfer function Purelin Training function TRAINLM Learning function LEARNGDM Learning conditions Learning scheme Supervised learning Learning rule Gradient descent rule Hidden neurons Twelve Output neurons One Sample pattern vector 70% (train), 15% (valid and test) Number of hidden layer 3 Neurons in hidden layer 5 Learning rate α : Marquardt adjustment Mu:0.01 parameter Maximum number of 500 epochs to train Goal MSE at the end of training Conclusion: In this paper, in-house fabricated EDDSG experimental setup is used to increase the material removal rate of Inconel-718 and investigated for the analysis of various factors influencing the performance characteristics, following the Response Surface Method of experimental design. Two models were developed for predicting of EDDSG of Inconel-718 using response surface methodology and artificial neural network. From this investigation, the following important conclusions are derived. 1) Wheel speed is the factor that has greater influence on, followed by pulse current. 2) The combination of high wheel speed, high pulse current, high pulse on-time and duty factor are more suitable for getting the higher value of. 3) The predictive ANN model is found to be capable of better predictions of within the range that they had been trained. The results of the ANN model indicate it is much more robust and accurate in estimating the values of when compared with the response surface model. References: (a)un-machined surface (b) EDDSG machined surface Figure 6 SEM images of Inconel before and after machining 1. Ezugwu, E.O., Bonney, J., Yamane, Y. (2003). An overview of the machinability of aeroengine alloys. Journal of Materials Processing Technology,Vol. 134, no. 2, pp Feyzi, T., Mohsen Safavi, S. (2013). Improving machinability of Inconel 718 with a new hybrid machining technique.international journal of Advanced Manufacturing Technology, Vol. 66, pp Koshy P, Jain V K, Lal G K. (1996). Mechanism of material removal in electrical discharge diamond grinding. International Journal of Machine Tools & Manufacture, Vol. 36, no. 10, pp Koshy P, Jain V K, Lal G K. (1997).Grinding of cemented carbide with electrical spark assistance.journal of Materials Processing Technology, Vol. 72, no. 1, pp Sanjeev Kumar (2007). Prediction of wear and surface roughness in electro-discharge diamond grinding.journal of Materials Processing Technology, Vol. 191, pp Yadav, R. N., Yadava, V., Singh, G. K., (2013). Multiobjective optimization of process parameters in Electro- Discharge Diamond Face Grinding based on ANN-NSGA-II hybrid technique. Front. Mech. Eng. vol. 8, no. 3, p Kozak J. (2002).Abrasive electrodischarge grinding (AEDG) of advanced materials.archives of Civil and Mechanical Engineering,Vol. 2, pp Choudhary, S.K.; Jain, V.K.; Gupta, M. (1999).Electrical discharge diamond grinding of high-speed steel.machining Science and Technology,Vol.3, no. 1, pp Jain V K, Mote R G. (2005). On the temperature and specific energy during electrodischarge diamond grinding (EDDG). International Journal of Advanced Manufacturing Technology, Vol. 26, no.1-2, pp Yadav S K S, Yadava V, Narayana V L. (2008) Experimental study and parameter design of electro-discharge 822-7

8 MODELLING AND PREDICTION OF MATERIAL REMOVAL RATE IN ELECTRICAL DISCHARGE DIAMOND SURFACE GRINDING PROCESS OF INCONEL-718 diamond grinding. International Journal of Advanced Manufacturing Technology, Vol. 36, no.1-2 pp Yadav S K S, Yadava V. (2010).Multi-objective optimization of electrical discharge diamond cutoff grinding (EDDCG) using Taguchi method.international Journal of Manufacturing Technology and Industrial Engineering, Vol.1, pp Singh G K, Yadava V, Kumar R. (2012). Robust parameter design and multi-objective optimization of electrodischarge diamond face grinding process of HSS. International Journal of Machining and Machinability of Materials, Vol.11, pp Singh G K, Yadava V, Kumar R. (2010).Diamond face grinding of WC-Co composite with spark assistance: Experimental study and parameter optimization. International Journal of Precision Engineering and Manufacturing, Vol.11, no.4, pp Singh G K, Yadava V, Kumar R. (2011). Experimental study and parameter optimization of electro-discharge diamond face grinding. International Journal of Abrasive Technology, Vol.4, pp