(WCB).2 full factorial design with four centre points is selected to perform the reliable experiments. Here the response parameters selected are surfa

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1 Optimization of cutting parameters in milling of Aluminium 775 alloy using response surface methodology B.Arjun*, S.Jaganbabu*,R.Jayaprakasah*, B.Kaviyarasu*, K.Gopalakrishnan Department of mechanical engineering, KSR Institute for Engineering and Technology, Tiruchengode- 6527,Tamilnadu. * Corresponding Author, kgkrishnan9@gmail.com Abstract the project is to investigate the effect of cutting parameters on the surface roughness and metal removal rate in aluminium 775 alloy end milling. Aluminium is the lightest material which has the density of 2.8 g/cm. The results and material of this experiment can be applicable in the manufacture of aircraft and aerospace industry. This experiment can be done using the Computer Numerical Controlled (CNC) milling machine with mm diameters carbide tool with end mill cutter. The parameter like speed, feed rate and depth of cut will be changed for each experiment. For this Minitab6 Statistical software is used and the results will be determined using Response Surface Methodology (RSM). The developed RSM model was tested using Analysis of Variance (ANOVA). Keywords: Aluminium alloy 775, CNC milling machine, end milling, carbide tool, surface roughness, Response surface methodology. I. INTRODUCTION Aluminium is the lightest material which has the density of 2.8 g/cm. The use of materials with low specific weight is an effective way of reducing the weight of structures. Aluminium alloys are among the most commonly used lightweight metallic materials as they offer a number of different interesting mechanical and thermal properties.aluminium alloy 775 is an Aluminium, with zinc as the primary alloying element. It is strong, with a strength comparable to many steels, and has good fatigue strength and average machinability, but has less resistance to corrosion than many other Al alloys. Its relatively high cost limits its use to applications where cheaper alloys are not suitable. The results of the research could be applied in the manufacture of automotive components and mold industry []. The application of the fuzzy logic integrated with taguchi method for minimizing the surface roughness and maximizing the material removal rate simultaneously, in CNC end milling of AL775 T6 aerospace Aluminium alloy. The input parameters taken into consideration are speed, feed, depth of cut and nose radius. AL775 T6 is one of the highest strength Aluminium alloy in 7 series family [2]. The work piece material was Aluminium 775- T6 material was chosen in this study is usually employed in the aerospace industry to manufacture components that demand: lighter, harder, stronger, tougher, stiffer, more corrosion- and erosion-resistant properties []. The trained ANN is able to predict the Ra values with reasonable accuracy. Taguchi S/N ratio analysis and ANN are useful to find the optimum combination of parameters for getting a good surface finish [4]. S/N noise ratio and Analysis of Variance (ANOVA) approve that parameter more significant affect the surface roughness is feed rate follow by cutting speed and depth of cut. Almost the correlation between dependent variable with independent variable very close and strong, which is approval by using multiple regression analysis. The value experiment with calculated almost closed. It means the Taguchi method have produced more accurate prediction value [5]. Tool life of ball nose end mill depending on up-copying and down-copying. The aim was to determine and compare the wear of ball nose end mill for different types of copy milling operations for various tool materials. Moreover, surface roughness in up-copying and down-copying was also measured and compared [6]. The experiment to find the surface roughness through feed cutting forces. They have used finite element modelling (FEM). It is considered a famous method belonging to the numerical simulation methods [7]. The parameter optimization of end milling operation for Inconel 78 super alloy with multi-response criteria based on the taguchi orthogonal array with the grey relational analysis. Nine experimental runs based on an L9 orthogonal array of Taguchi method were performed [8]. The effect of machining parameters spindle speed, feed and depth of cut were investigated during Face Milling of Wrought Cast Steed grade B 26

2 (WCB).2 full factorial design with four centre points is selected to perform the reliable experiments. Here the response parameters selected are surface roughness and flatness. To achieve the desire value of flatness and surface roughness machining parameters need to be controlled [9]. The study of Taguchi optimization method for low surface roughness value in terms of cutting parameters when face milling of the cobalt-based alloy (stellite 6) material. The milling parameters evaluated are feed rate, cutting speed and depth of cut, a series of milling experiments are performed to measure the surface roughness data []. The process has been analysed using a Response Surface Method in order to obtain a model fit for the fine tuning of the process parameters. The present study analyses the effect of simultaneous variations of four cutting parameters (cutting speed, feed rate, and radial and axial depth of cut) on energy consumption. For this purpose, the Response Surface Method (RSM) is utilized []. An experimental study related to the optimization of cutting parameters in roughing turning of AISI 66 T6 aluminium. Energy consumption and surface roughness were minimized, while the material removal rate of the process was maximized [2]. The predictive models of these three energy components are developed for orthogonal cutting mode, and the influences of cutting speed, undeformed chip thickness and tool rake angle on the cutting energy consumption are revealed []. Feed rates, cutting speeds and depth of cut were investigated to determine the effect of machining parameters on the surface roughness and the cutting force. The full factorial experimental cutting parameters with three factors and four levels involve 54 experimental results [4]. The effect of machined surface inclination angle, axial depth of cut, spindle speed and feed rate for better surface integrity in inclined end milling process utilizing titanium coated carbide ball end mill [5]. From the above literature reviews, surface roughness optimization in the end milling operation shows that spindle speed, feed rate and depth of cut have been commonly chosen as the control factors. The average surface roughness (Ra) has been the most common parameter to define the surface roughness of the machined part. In the present work, Effect of cutting parameters on surface roughness was investigated using RSM. Optimum machining parameters were carried out using RSM with BoxBehnken design and compared to the experimental results. II. EXPERIMENTAL DETAILS 2. Design of experiments The design of experiments technique is an important tool, which permits us to carry out the modeling and analysis of the influence of process variables on the response variables. The response variable is an unknown function of the process variables, which are known as design factors. In the present study, 5 numbers of experiments based on a three level three factors Box-Behnken design in RSM were performed to obtain surface roughness values measured from aluminium775 alloy under wet conditions. The machining parameters such as spindle speed, depth of cut and feed rate are considered as design factors. The process variables and there levels as shown in Table. Fig. End milling operations on the Workpiece 2.2 Experimental factors, Workpiece material and cutting tools used The machine used for the milling experiment is a three axis CNC Vertical Milling machine. CNC part programs were used for to describe the tool path. Analuminium775 alloy block ofmm x mm x 6mm was used in the present study. The chemical compositions of aluminium 775 alloy as shown in Table 2. The experiment was carried out with carbide end mill cutter with mm diameter and four flutes. End milling of material in the CNC machine as shown in Fig.. The Workpiece material is mounted onto the CNC machine table to provide maximum rigidity. The Workpiece material is parallel to the machine table and perpendicular to the machine s spindle head. The experiment was carried out under wet condition. 2. Measurement of surface roughness Surface roughness is an important measure of the technological quality of a product and a factor that greatly influences manufacturing cost. In the 27

3 present study, 5 experiments (Trail ) were conducted and 5 Ra values Table.Process parameters and levels used in the experimentation Parameter Code Unit Level Level 2 Level Spindle speed S Rpm 2 4 Feed rate F mm/min 4 Depth of cut D Mm Were measured from the machined area. Each of 5 Ra values was repeated at least two times and then, the average of these values was recorded by a SURFTEST SJ 4 roughness instrument. These 5 experiments (Trail 2) were repeated and Ra values were measured. Average roughness values were calculated from the trail and trail 2. Measured average surface roughness values are shown in Table. Surface roughness measurements recorded in the perpendicular to cutting direction. 6 mm cutoff length was set for roughness measurement. Surface roughness measurement in the material by using SURFTEST SJ-4 roughness instrument as shown in Fig. 2. Fig. 2 The setup of surface roughness measurement Table 2 Chemical compositions of aluminium 775 alloy (wt. %) Cu Mn Mg Zn Cr Pb Ni Sn Ti Si Fe Al Table Experimental results for surface roughness and MRR blocks Spindle Feed rate, Depth of Surface MRR speed, S, F, cut, D, roughness, Ra, (mm/min) (rpm) (mm/min) (mm) (µm) analysis as shown in Table 4. In Table 4, R2 value is III. RESULTS AND DISCUSSION The results from the machining trails were 97.% and adjusted R2 value is 95.66%, which is input into the MINITAB 6 software for the further desirable. When adjusted R2 value close to %, the analysis. A quadratic polynomial regression model multiple regression models match very well with was created by employing the roughness values to experimental measurements. Adjusted R2 value illustrate the fitness of experimental measurements % also agrees with the multiple regression The results obtained from multiple regression models and provides a very good relationship Run order 28

4 between machining parameters such as spindle speed, feed rate and depth of cut and surface roughness. The following equation was the final regression model in terms of coded factors for surface roughness: Ra = E5*S+458*F *D-2.47E-9*S E-7*F2-.667*D2+4.2E8*S*F+6*S*D-.8*F*D Analysis of variance (ANOVA) analysis was carried out to determine the effect of machining parameters on the surface roughness. An ANOVA table commonly used to summarize the tests performed. An ANOVA table for response surface quadratic model for surface roughness as shown in Table 5. The P test was used to evaluate the statistical significance of machining parameters for surface roughness. The P value for these machining parameters is less than.5 (i.e., 95% confidence level). This demonstrates that machining parameters have a significant effect on the surface roughnessthe normal probability plot of the residuals for the surface roughness as shown in Fig.. This illustrate that residuals generally fall on the straight line showingthat the errors are distributed normally. The plot of the residuals versus fitted value for Surface roughness as shown in Fig. This revealed that they have no obvious pattern and unusual structure. Term Coef SE Coef T Constant Speed Feed Depth Speed*Speed Feed*Feed Depth*Depth Speed*Feed Speed*Depth Feed*Depth S = PRESS = R-Sq = 97.% R-Sq(pred) = 9.7% R-Sq(adj) = 95.66% Table 4Estimated Regression Coefficients for average surface roughness Source Regression Linear Speed Feed Depth Square Speed*Speed Feed*Feed Depth*Depth Interaction Speed*Feed Speed*Depth Feed*Depth Error Lack-of-Fit Pure Error Table 5 Analysis of Variance for Average surface roughness DF Seq SS Adj SS AdjMS F P P

5 Total The response surface plots for influence of machining parameters (Speed, feed and depth of cut) on the surface roughness were shown in Fig. 4, 5 and 6. It is clear from Fig. 4. If speed is increased surface roughness decreases. If the feed rate is increased surface roughness increases.it is clear from fig 5. If feed is increased surface roughness increases. If the depth of cut is increased surface roughness first increased. From fig 6 the speed is increased surface roughness decreases. If the depth of cut is increased surface roughness increases. After that it will be decreases.spindle speed rpm, feed rate mm/min and depth of cut.25mm were the optimal parameters for the low surface roughness in aluminium 775alloy. surface plot for spindle speed vs depth of cut FEED 4.26 Ra Percent Fig 4: surface plot for speed vs doc Fitted Value Ra Versus Order 6 4 DEPTH OF CUT Frequency SP EED surface plot for spindle speed vs feed rate Histogram DEP T H O F C UT.4 Versus Fits Plots for Ra Normal Probability Plot SP EED FEED Observation Order Fig 5: surface plot for speed vs feed Fig : plots for Ra surface plot for feed rate vs depth of cut SPEED Ra FEED DEP T H O F C UT.2 Fig 6: surface plot for feed vs doc 24

6 analysis. A quadratic polynomial regression model was created by employing the MRR values to Metal removal rate: The material removal rate, MRR, can illustrate the fitness of experimental measurements. be defined as the volume of material removed The results obtained from multiple regression divided by the machining time. Another way analysis as shown in Table 6. In Table 6, R2 value is to define MRR is to imagine an 99.69% and adjusted R2 value is 99.55%, which is "instantaneous" material removal rate as there are at desirable. When adjusted R2 value close to %, the which the cross-section area of material being multiple regression models match very well with removed moves through the workpiece. experimental measurements. Adjusted R2 value Formula: 99.55% also agrees with the multiple regression MRR= Depth of cut (mm) * Width of cut models and provides a very good relationship (mm) * Feed rate (mm/min). between machining parameters such as spindle speed, The results from the machining trails were feed rate and depth of cut and surface roughness. input into the MINITAB 6 software for the further Table 6 Estimated Regression Coefficients for MRR Term Coef SE Coef T P Constant Speed Feed Depth Speed*Speed Feed*Feed Depth*Depth Speed*Feed Speed*Depth Feed*Depth S = PRESS = 442 R-Sq = 99.69% R-Sq(pred) = 99.2% R-Sq(adj) = 99.55% Table 7Analysis of Variance for MRR Source Regression Linear Speed Feed Depth Square Speed*Speed Feed*Feed Depth*Depth Interaction Speed*Feed Speed*Depth Feed*Depth Error Lack-of-Fit Pure Error Total DF Seq SS Adj SS AdjMS F P

7 The following equation was the final regression model in terms of coded factors for Metal removal rate: surface plot for spindle speed vs feed rate DEPTH OF CUT.5 MRR = 7.5.7*S *F+758.*D+*S *F *D2+62*S*F+.2*S*D+ *F*D Analysis of variance (ANOVA) analysis was carried out to determine the effect of machining parameters on the MRR. An ANOVA table commonly used to summarize the tests performed. An ANOVA table for response surface quadratic model for MRR as shown in Table 7. The P test was used to evaluate the statistical significance of machining parameters for MRR. The P value for these machining parameters is less than.5 (i.e., 95% confidence level). This demonstrates that machining parameters have a significant effect on the MRR the residuals Plot for the MRR as shown in Fig. 7. This illustrate that residuals generally fall on the straight line showing that the errors are distributed normally. The plot of the residuals versus fitted value for Surface roughness as shown in Fig 7. This revealed that they have no obvious pattern and unusual structure. The response surface plots for influence of machining parameters (Speed, feed and depth of cut) on the MRR were shown in Fig. 8, 9 and. It is clear from Fig. 8 If speed is increased metal removal rate increases. If them feed rate is increased metal removal rate increases.it is clear from fig 9. If speed is increased metal removal rate increases. If the depth of cut is increased metal removal rate decreases. From fig the feed is increased metal removal rate increases. If the depth of cut is increased metal removal rate increases. MRR SP EED FEED 4 Fig 8: surface plot for speed vs feed surface plot for spindle speed vs depth of cut FEED 4 24 MRR SP EED 4 DEP T H O F CUT.2 Fig 9: surface plot for speed vs doc surface plot for feed rate vs depth of cut SPEED Plots for MRR Normal Probability Plot Versus Fits 99 Percent Histogram 5 Frequency Versus Order Fitted Value MRR FEED DEPT H OF CUT Observation Order Fig7: plots for MRR IV. CONFIRMATION EXPERIMENT A confirmation experiment was an important process for to validate the predicted optimal values after experimental trails. In the present work confirmation experiment was conducted for the Fig :surface plot for feed vs doc optimal machining parameters such as spindle speed rpm, feed rate mm/min and depth of cut.25 mm. The surface roughness (Ra) value was repeated at least two times and then, the average surface roughness value was recorded by SURFTEST 242

8 SJ-4 instrument. The measured surface roughness value (.85) was very close to the minimum surface roughness (.85) in the Table. Therefore, the confirmation experiment indicated that the selection of the optimal levels for the all the machining parameters produced the low surface roughness. V. CONCLUSIONS In the present study, the better combinations of machining parameters have been selected to provide the lower surface roughness and higher metal removal rate in the milling of the aluminium 775 alloy under wet condition. Quadratic polynomial regression model was developed based on RSM using Box-Behnken design. The developed RSM model was tested through ANOVA. An ANOVA analysis was performed to indicate the influence of three machining parameters on the surface roughness and MRR. The following conclusions were summarized from the above investigations: From the regression analysis, R2 was found to be 97.% and adjusted R2 was 95.66%. Therefore, the surface roughness (Ra) values are adequate to construct the prediction model for surface roughness. From ANOVA results, the feed rate was found to be most significant factor affects surface roughness of milled surface. Depth of cut and spindle speed were other machining parameters affecting the surface roughness. Surface plots clearly show the surface roughness increases rapidly with the increases in feed rate and depth of cut. So, it is recommended to employ smaller feed rate and depth of cut to achieve low surface roughness. From the regression analysis, R2 was found to be 99.69% and adjusted R2 was 99.55%. Therefore, the surface roughness (Ra) values are adequate to construct the prediction model for surface roughness. Surface plots clearly show the metal removal rate increases rapidly with the increases in feed rate and depth of cut. So, it is recommended to employ higher feed rate and depth of cut to achieve high metal removal rate. 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