PARAMETER OPTIMIZATION IN VERTICAL MACHINING CENTER CNC FOR EN45 (STEEL ALLOY) USING RESPONSE SURFACE METHODOLOGY

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1 International Journal of Mechanical Engineering and Technology (IJMET) Volume 7, Issue 2, March-April 2016, pp , Article ID: IJMET_07_02_031 Available online at Journal Impact Factor (2016): (Calculated by GISI) ISSN Print: and ISSN Online: IAEME Publication PARAMETER OPTIMIZATION IN VERTICAL MACHINING CENTER CNC FOR EN45 (STEEL ALLOY) USING RESPONSE SURFACE METHODOLOGY Ajay Kumar, Simranjeet Singh, Sahil Barry, Shivam Bhardwaj Research students, Mechanical Department, GNI Mullana, Kurukshetra University, Kurukshetra, Haryana, India Er. Ajay Sharma, Er. Harpreet Singh Astt. Professor, Mechanical Department, GNI Mullana, Kurukshetra University, Kurukshetra, Haryana, India ABSTRACT Now days optimal machining parameters are of great concern in manufacturing environments, where economy of machining operations plays a key role in competitiveness in the market. This paper aims at developing a statistical model to optimize the surface parameters such as material removal rate (MRR) and surface roughness (SR).Response Surface Methodology (RSM) experimental design was used for conducting experiment. The experimental study was carried out in a CNC vertical machining center. The work piece material was EN45 (steel alloy).en45 is widely used in the motor vehicle industry for leaf springs, truncated conical springs, helical springs and many general applications. The tool used was an end mill cutter made of solid carbide of dimensions (10mm 28mm 75L-4F).The experiment were carried out using Three input parameters viz. Feed rate(f),spindle speed(n) and Depth of cut(d c ). The effect of machining parameters on MRR and surface roughness is evaluated and the optimum cutting conditions for minimizing the surface roughness and maximizing the MRR is determined. The result of the present experiment shows that the feed rate (f) and depth of cut (d c ) are the highly dominating factors and most significant factors that control the surface roughness and material removal rate as compared to the other parameters. Key words: RSM, EN45, VMC, Spindle Speed, Depth of Cut, Feed Rate, MRR and Surface Roughness editor@iaeme.com

2 C Parameter Optimization In Vertical Machining Center CNC For En45 (Steel Alloy) Using Response Surface Methodology Cite this Article Ajay Kumar, Simranjeet Singh, Sahil Barry, Shivam Bhardwaj, Er. Ajay Sharma and Er. Harpreet Singh. Parameter Optimization In Vertical Machining Center CNC For En45 (Steel Alloy) Using Response Surface Methodology. International Journal of Mechanical Engineering and Technology, 7(2), 2016, pp INTRODUCTION With the increase in the precise demand of the modern engineering products, the surface texture has more importantly to be controlled. It is revealed from the study that surface texture greatly influences the functioning of the machined parts. Whatever may be the manufacturing process used, it is not possible to produce smooth surface. In this work, the machining parameters such as depth of cut (d c ), spindle speed (N) and feed rate (f) that affect the surface roughness and the MRR in the milling operation was studied. EN45 is a manganese spring steel with high carbon content. EN45 is used widely in the motor vehicle industry for leaf springs, truncated conical springs, helical springs and spring plates and many general engineering applications. EN45 springs steel is used for manufacturing the main components of cultivator. EN 45 spring steel material is used in the manufacturing of reversible shovel. Response surface methodology is used to plan the experiments and for the process of optimization. The confirmation test was conducted using optimum combination of cutting parameters. From the literature survey, it was under stood that no research work has been reported in VMC of EN45 material to find out the MRR and Surface Roughness. So, in this study, Response Surface Methodology (RSM) is used for the development of optimization of MRR and surface roughness with three different parameters feed rate, spindle speed and depth of cut.en45 steel finds its typical applications in the manufacturing of automobile and machine tool parts. Because of its wide application EN45 steel has been selected as the work material in this work. Chemical composition of EN45 ALLOY: Mn P S Si RESPONSE SURFACE METHODOLOGY Response surface methodology (RSM) is a collection of mathematical and statistical techniques for empirical model building. By careful design of experiments, the objective is to optimize a response (output variable) which is influenced by several independent variables (input variables). An experiment is a series of tests, called runs, in which changes are made in the input variables in order to identify the reasons for changes in the output response. The application of RSM to design optimization is aimed at reducing the cost of expensive analysis methods (e.g. finite element method or CFD analysis) and their associated numerical noise. Response surface method is the most effective method to study the result obtained from factorial experiments. It is an effective tool for modeling and studying the manufacturing problems. It delivers more information with fewer numbers of investigations. It is an investigation strategy for exploring the limits of the input parameters and emerging experiential statistical model for the measured response, by approximating the relationship existing between Al Ni Cr V Mo editor@iaeme.com

3 Ajay Kumar, Simranjeet Singh, Sahil Barry, Shivam Bhardwaj, Er. Ajay Sharma and Er. Harpreet Singh the response and input process parameters. The limit of the process parameters has to be defined in response surface method and the first experimentation was done to recognize the machining parameters that affect the MRR and Surface roughness and to discover the range of the selected cutting parameters. 3. METHOD OF EXPERIMENT Work piece material The work piece material used for this experiment is EN45 steel alloy. The work piece is shape of rectangular slabs with dimensions of (51mm 48mm 14mm). Composition of material was obtained using spectro analysis and is given above. Due to its long life as compared to the mild steel and the wide applications of the EN45 in automobile industry and its surface finish application, this material is selected for the research work. Experimental Setup The experiment was conducted at Ambala College of Engineering, Ambala with the following experimental setup, the equipment used was Vertical Machining Center CNC of Siemens series named as VMC 8410.the tool used was a solid carbide tool of the diameter ɸ10mm.The work piece material used was EN45 steel alloy. The operation performed was face milling operation. Parameters used for the experiment were Feed rate, Spindle speed and Depth of cut. The responses considered in this study are surface roughness and material removal rate (MRR).the surface roughness was measured with the help of SRT-8210 surface roughness tester. Material removal rate is used to evaluate machining performance.mrr is expressed as the amount of material removed under a period of machining time. The process parameters are shown in the table 3.1 below. TABLE 3.1 Process parameters with their values at three levels Parameter Level 1 Level 2 Level 3 Output parameter Feed Rate (mm/min) Spindle Speed (rpm) Depth of Cut (mm) MRR and Surface Roughness Response Surface Methodology was used for the design of the experimental array with the three different values at different levels. Table 3.2 shows the DOE with actual values. TABLE 3.2 Actual values of process parameters Std. Run Block Factor 1 Factor 2 Factor 3 Feed Rate Spindle Speed Depth of cut Block Block Block Block Block Block Block Block Block editor@iaeme.com

4 Parameter Optimization In Vertical Machining Center CNC For En45 (Steel Alloy) Using Response Surface Methodology Std. Run Block Factor 1 Factor 2 Factor 3 Feed Rate Spindle Speed Depth of cut Block Block Block Block RESULT AND DISCUSSIONS Response surface methodology indicates the area in the design region where the process is likely to give desirable result simultaneous consideration of multiple responses involves first building an appropriate response surface model for each response and trying to find operating conditions that in some sense of optimizes all response or at least keeps them in the desired range. The milling parameters such as the cutting speed, the depth of cut and the feed rate etc. are the investigated parameter that affects the surface quality and the material removal rate of the milled parts. Table 4.1 represents the experimental results with actual process parameters.mrr is calculated by the equation (1) MRR= (W b -W a )/t m (g/min) (1) Where, W b = weight of the work piece before machining (g). W a = weight of the work piece after machining (g). t m = time consumed in machining (min.). TABLE 4.1 Experimental results with actual values of process parameters. Std. Run Block Factor 1 Factor 2 Factor 3 Response 1 Response 2 S.R(R a ) Feed Spindle Depth of rate speed cut MRR Block Block Block Block Block Block Block Block Block Block Block Block Block (a) Effect of the process parameters on the Material Removal rate (MRR) Deign expert 6.0 has been used for the calculation as shown in the above table 4.1.Based on the ANOVA the table4.2 depicts the effects of the process variables and the interactions based on the 2FI model for MRR. This model was developed for the 95 confidence level. The model F value implies that the model is significant. There is only 0.04 chance that a model F value this large could occur due to noise. Values of Prob>F less than indicate model terms are significant. By checking the values of F values, it is seen that the factor A, C and AC are the most editor@iaeme.com

5 M.R.R. Ajay Kumar, Simranjeet Singh, Sahil Barry, Shivam Bhardwaj, Er. Ajay Sharma and Er. Harpreet Singh significant effect on the MRR. Values greater than indicates the model terms are not significant. TABLE 4.2 ANOVA for Response Surface 2FI Model for MRR Source Sum of Squares DF Mean Square F value Prob>F Model Significant A Significant B Not significant C Significant AB Not significant AC significant BC Not significant Residual Cor Total Std.Dev R-squared Mean 3.58 Adj.R-squared CV Pred. R-squared PRESS Adeq. Precision The Pred. R-squared of is in reasonable agreement with the Adj. R- squared of "Adeq Precision" measures the signal to noise ratio. A ratio greater than 4 is desirable. The ratio of indicates an adequate signal. This model can be used to navigate the design space. Final equation (2) in terms of actual factors: MRR = ( Feed rate Spindle speed Depth of cut Feed rate Spindle speed Feed rate Depth of cut Spindle speed Depth of cut). (2) 9.30 One Factor Plot Warning! Factor involved in an interaction Figure1 (a) Main factor plots on MRR with varied feed rate editor@iaeme.com

6 M.R.R. M.R.R. Parameter Optimization In Vertical Machining Center CNC For En45 (Steel Alloy) Using Response Surface Methodology The main factor plot on MRR with varied feed rate from mm/min. at spindle speed =1350rpm and Depth of cut =0.30mm as shown in figure1 (a) One Factor Plot Warning! Factor involved in an interaction B: SPINDLE SPEED Figure 1(b) Main factor plot on MRR with varied Spindle Speed. The main factor plot on MRR with varied spindle speed from rpm at feed rate=275mm/min. and Depth of cut=0.30mm as shown in figure 1(b) One Factor Plot Warning! Factor involved in an interaction Figure 1(c) Main factor plot on MRR with varied Depth of Cut. The main factor plot on MRR with Varied Depth of cut from mm at feed rate= 275mm/min. and Spindle speed= 1350 rpm as shown in fig.1(c).figure 2 (a) and (b) shows the three dimensional interaction response surface and contour plot for the response MRR in terms of feed rate and depth of cut at spindle speed =1350 rpm. A contour plot plays a very important role in the study of response surface. From the editor@iaeme.com

7 M.R.R. Normal Probability M.R.R. Ajay Kumar, Simranjeet Singh, Sahil Barry, Shivam Bhardwaj, Er. Ajay Sharma and Er. Harpreet Singh examination of the contour plot and response surface, it is observed that MRR increases from 1.76g/min. to 5.27g/min. with depth of cut from mm with feed rate from 50mm/min. to 500mm/min M.R.R (a) 2(b) Figure 2(a) and (b) contour plot for MRR and response surface for MRR. The figure 3(a) and 3(b) below shows the interaction plots with feed rate and depth of cut with a spindle speed =1350 rpm and normal probability distribution plot residuals respectively. Figure 3(c), 3(d) below shows the residual Vs predicted values, residuals Vs run respectively. It is clear from the figure that all the data points are following the straight line. Thus, the data is normally distributed. It can be seen from figure (c),(d) of figure 3 that all the actual values are following the predicted value and thus declaring model assumptions are correct and within the limits Interaction Graph Normal Plot of Residuals C C Studentized Residuals 3(a) 3(b) editor@iaeme.com

8 Studentized Residuals Studentized Residuals Parameter Optimization In Vertical Machining Center CNC For En45 (Steel Alloy) Using Response Surface Methodology Residuals vs. Predicted Residuals vs. Run Predicted Run Number 3(c) 3(d) Figure 3 (a) Interaction plot, (b) Normal plot of residuals, (c) Residuals Vs predicted plots, (d) Residual Vs run plots. (b)effect of the process parameters on surface roughness (R a ) The model F value of implies that the model is significant. There is only a chance of 0.25 that model F -value this large could occur due to noise. By checking F values P values, it is clear that values of prob.>f less than indicates model terms are significant. In this model the term A, (feed rate) has the most significant effect on Surface Roughness. Values greater than indicate that the model terms are not significant. TABLE 4.3 ANOVA for Response Surface Linear model for Surface Roughness. Source Sum of Mean DF Squares Square F value Prob>F Model Significant A Significant B Nonsignificant C Nonsignificant Residual Cor Total Std.Dev R-squared Mean 0.93 Adj.R-squared CV Pred. R-squared PRESS 1.21 Adeq. Precision The Pred. R-squared of is in reasonable agreement with the Adj. R- squared of Adj. R-squared measures the signal to noise ratio. A ratio greater than 4 is desirable. The ratio indicates an adequate signal. This model can be used to navigate the design space. The final equation (3) in terms of actual factors: editor@iaeme.com

9 SURFACE ROUGHNESS SURFACE ROUGHNESS SURFACE ROUGHNESS Ajay Kumar, Simranjeet Singh, Sahil Barry, Shivam Bhardwaj, Er. Ajay Sharma and Er. Harpreet Singh SURFACE ROUGHNESS (R a ) = ( E Feed rate E- 004 Spindle speed Depth of cut) (3) One Factor Plot One Factor Plot B: SPINDLE SPEED 4 (a) 4 (b) One Factor Plot SURFACE ROUGHNESS (c) 4(d) editor@iaeme.com

10 SURFACE ROUGHNESS Normal Probability SURFACE ROUGHNESS Parameter Optimization In Vertical Machining Center CNC For En45 (Steel Alloy) Using Response Surface Methodology (e) Figure 4: Main plots on Surface roughness (a) with varied feed rate (b) with varied spindle speed (c) with varied Depth of cut (d) Contour plot for surface roughness (e) Response surface plot for surface roughness. The main plots on surface roughness with varied feed rate from mm/min. at spindle speed =1350rpm and Depth of cut =0.30mm is shown in fig.4 (a) and with varied spindle speed from rpm at feed rate=275mm/min. and depth of cut=0.30mm is shown in fig.4 (b) and with varied Depth of cut from mm at feed rate=275mm/min and spindle speed=1350 rpm is shown in fig.4(c).the three dimensional interaction response and contour plots as shown in the fig. 4(d) and 4(e) for the response R a in terms of feed rate and depth of cut. By generating response surface analysis it is easy to characterize the shape of the surface and locate the optimum with reasonable precision from the examination of the contour plots and response surface, it is observed that R a increases from 0.572µm to 1.317µm.with feed rate from mm/min and depth of cut from mm A+ Interaction Graph Normal Plot of Residuals A Studentized Residuals 5(a) 5(b) editor@iaeme.com

11 Studentized Residuals Studentized Residuals Ajay Kumar, Simranjeet Singh, Sahil Barry, Shivam Bhardwaj, Er. Ajay Sharma and Er. Harpreet Singh 3.00 Residuals vs. Predicted 3.00 Residuals vs. Run Predicted Run Number 5(c) Figure 5 (a) Interaction plots, (b) Normal plots of residuals, (c) Residuals Vs Predicted, (d) Residuals Vs Run. The figure 5(a) and 5(b) above shows the interaction plots with feed rate and depth of cut with a spindle speed=1350rpm and normal probability distribution plot residuals respectively. Figure 5(c), 5(d) below shows the residual Vs predicted values, residuals Vs run plot respectively. It is clear from the figure that all the data points are following the straight line. Thus, the data is normally distributed. It can be seen from figure (c),(d) of figure 5 that all the actual values are following the predicted value and thus declaring model assumptions are correct and within the limits. CONCLUSION The observed MRR and surface roughness of the experimental result are 2.866g/min and µm respectively. Table 5.1 shows the error percentage for experimental validation of the developed models for responses with optimal parameter setting during the milling of the EN45 on vertical machining center VMC. From the analysis from the table 5.2, it can be observed that the calculated error is small. The error between experimental and predicted values for MRR and Surface roughness lies within the 14 and 6 respectively. Obviously, the above experimental result confirms excellent reproducibility of the experiment conclusions. 5(d) TABLE 5.1 Multi-optimal parameter settings for MRR and Surface Roughness. Parameters Units Optimal parameter FEED RATE mm/min 275 SPINDLE SPEED R.P.M 1350 DEPTH OF CUT Mm 0.30 TABLE 5.2 Experimental validations of the developed models with optimal parameters. Responses Predicted Experimental Value Value Error Desirability MRR(g/min) Surface roughness(µm) editor@iaeme.com

12 Parameter Optimization In Vertical Machining Center CNC For En45 (Steel Alloy) Using Response Surface Methodology In this study, the MRR and Surface roughness in parameter optimization in vertical machining center (VMC) for EN45 (steel alloy) was modeled and analyzed through Response surface methodology (RSM).Feed rate, spindle speed and depth of cut have been employed to carry out the experimental study. Summarizing the main features, the following conclusions could be drawn: The predicted values lies very close to the experimental values reasonably close, with R5 of 3.06(g/min) for MRR and 0.945(µm) of surface roughness. The error between the experimental values and predicted values at the optimal combination of parameters for MRR and Surface roughness lies within 14 and 6 respectively. From multi- response optimization, the optimal combination of parameters setting are feed rate =275mm/min. and spindle speed = 1350 rpm and depth of cut =0.30mm for achieving the required maximum Material removal rate (MRR) and minimum Surface roughness. REFERENCES [1] Kumar Anish, Kumar Vinod, Kumar Jatinder, "Prediction of Surface Roughness in Wire Electric Discharge Machining (WEDM) Process based on Response Surface Methodology, International journal of Engineering and Technology, 2(4), pp , [2] Pawan kumar, Anish kumar and Balinder singh, Optimization of process parameters in surface grinding using Response Surface Methodology, International journal of research in mechanical engineering & technology, 3(2), pp , [3] Sumit Raj & Kaushik Kumar, optimization of MRR and overcut in die sinking Electro-discharge machining of EN45 material using Grey-Taguchi technique, International journal of Applied engineering Research,10(55),pp , [4] Gunaraj V and Murugan N Application of response surface methodology for predicting weld bead quality in submerged arc welding of pipes J Mater Process Tech; 88, pp ,1999. [5] M.R. Soleymani Yazdi, A. Khorram Modeling and Optimization of Milling Process by using RSM and ANN Methods IACSIT International journal of engineering and technology, 2(5), pp , [6] Jae-Seob Kwak, " Application of Taguchi and response surface methodologies for geometric error in surface grinding process ",International Journal of Machine Tools & Manufacture,45, pp ,2005. [7] Singh Jagtar, Singh Lakhwinder Pal, Kaushik Ankur Enhancing Wear Resistance of En45 Spring Steel Using Cryogenic Treatment Friction and wear Research, 1(2), pp.22-26, [8] Raj Mohan B V S and Suresh R K. Parameter Optimization In Vertical Machining Center CNC For En45 (Steel Alloy) Using Response Surface Methodology. International Journal of Mechanical Engineering and Technology, 6(8), 2015, pp [9] Dr. B.V.R. Ravi Kumar. A Review on Importance of Microalloying in Steel. International Journal of Mechanical Engineering and Technology, 5(2), 2014, pp [10] Milon D Selvam, G.Karuppusami & A.K.Shaik Dawood, optimization of machining parameters for face milling operation in a vertical CNC milling machine using genetic algorithm, IRACST-Engineering science and technology international journal,2(4), pp , editor@iaeme.com