Parametric optimization of surface roughness in turning inconel718 using tag

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Parametric optimization of surface roughness in turning inconel718 using tag

  1. 1. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 4, July - August (2013) © IAEME 125 PARAMETRIC OPTIMIZATION OF SURFACE ROUGHNESS IN TURNING INCONEL718 USING TAGUCHI METHODS AND ITS PREDICTION BY REGRESSION ANALYSIS Prasad.K.K1 , Dr. D. Chaudhary2 1 Professor, Department of Mechanical Engineering, GNDEC, Bidar, Karnataka, India. 2 Professor & Head, Department of Mechanical Engineering, GNDEC, Bidar, Karnataka, India. ABSTRACT Machining is a complex process wherein many variables are involved which are having a bearing on the quality of the end products. Surface roughness is one of the most specified quality characteristics which affect the functional behavior of parts. An excellent surface finish significantly improves fatigue strength, corrosion resistance, creep life and also affects several other functional attributes. There are controllable parameters like cutting speed, feed, depth of cut, nose radius and uncontrollable parameters like machine tool vibration, tool wear, workmaterial flaws etc which are having a telling influence on the quality of machined components. There are objectives like surface roughness and tool life which imposes conflicting requirement on parameters, so optimization of parameters assumes greater importance in machining. In addition, there is a need for tools that will allow the prediction of quality characteristics in advance to maximize the gain from machining operations. The turning being the most widely used machining process, this work focused on parametric optimization of surface roughness while turning components on CNC lathe. Since the effect of the parameters on resulting surface roughness have not been quantified yet particularly when machining difficult-to-machine materials like INCONEL718 super alloy using uncoated carbide turning inserts, this work concentrated on those aspects. The experiment was performed based on L27 Taguchi Orthogonal Arrays and optimal parameter setting was determined using Signal-to-Noise (S/N) ratio, Lower-The-Better criterion. The significance of the parameters was determined by employing Analysis of Variance (ANOVA) and the mathematical modeling and prediction of the surface roughness is accomplished by Multiple Regression Analysis (MRA). The result obtained indicates that Taguchi method is capable of optimizing process parameters in turning process and the mathematical model obtained as a result of regression analysis can be reliably used for the prediction of surface roughness. INTERNATIONAL JOURNAL OF MECHANICAL ENGINEERING AND TECHNOLOGY (IJMET) ISSN 0976 – 6340 (Print) ISSN 0976 – 6359 (Online) Volume 4, Issue 4, July - August (2013), pp. 125-137 © IAEME: www.iaeme.com/ijmet.asp Journal Impact Factor (2013): 5.7731 (Calculated by GISI) www.jifactor.com IJMET © I A E M E
  2. 2. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 4, July - August (2013) © IAEME 126 KEY WORDS: Surface roughness, optimization, Taguchi methods, Multiple Regression analysis (MRA), Inconel718 1. INTRODUCTION Machining is a term that covers a large collection of manufacturing processes designed to remove unwanted maternal usually in the form of chips, from a workpiece to give the desired geometry, size and finish specialized to fulfill design requirements. Almost every manufactured product has components that require machining, often to great precision [1] The overall value of a product and its acceptance by the customer is typically determined by its performance with respect to multiple measures like how closely they adhere to set product specifications for length, width, diameter, surface finish and reflective properties. Surface finish is one of the most important quality measures that manufacturers must be able to control. Surface roughness of machined components depends on many factors. Some of these factors can be controlled and some cannot. Controllable process parameters include cutting speed, feed, depth of cut, tool geometry (ie, nose radius, rake angle etc). Other factors such as vibrations of tool, workpiece and machine tool, tool wear, variability of work material and tool material etc cannot be controlled easily are called noise factors.[2] Surface roughness refers to the relatively closely spaced or fine surface irregularities mainly in the form of feed marks left by the cutting tool on the machined surface [3]. It plays a very important role in the performance of turned workpiece as a good quality turned surface significantly improves fatigue strength, corrosion resistance and creep life. Surface roughness also affects several functional attributes of parts such as contact causing surface friction, wearing, light reflection, heat transmission, ability of holding and distributing lubricant, load bearing capacity and resistance to fatigue. [4] Surface roughness is specified by the extent of deviation of the finished surface from the ideal surface. There are different ways of representing this deviation. However arithmetic mean of roughness or arithmetic mean deviation of roughness (Ra) is the most commonly used surface roughness measure [5].Ra is the arithmetic mean of absolute values of the evaluation profile deviations (yi) from the mean line and it is evaluated using the equation 1 Ra = (1/n) (∑ yi௡ ௜ୀଵ ----------------------- (01) For the efficient use of machine tools optimum cutting conditions are required to be determined because under-optimized machining conditions will result in loss of quality as well as productivity. There are many optimization techniques employed for optimization of machining parameters which include fussy logic, genetic algorithms, Taguchi techniques, response surface methodology, Ant colony optimization, Artificial Neural Networks etc. A detailed review of optimization techniques can be observed in the article mentioned in reference [6]. This work used Taguchi techniques for achieving optimization because of the simple reason that it enables multiple complex properties to be optimized at minimal cost. Taguchi Design of Experiments (DoE) methods incorporate Orthogonal Arrays (OA) to minimize the number of experiments required to determine the effect of process factors upon the performance characteristics. This approach allows a statically sound experiment to be completed while investigating a minimum number of possible combinations of factors. Using this approach the goal can be accomplished in a timely manner and at a reduced cost with results comparable to full factorial experiment [7].
  3. 3. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 4, July - August (2013) © IAEME 127 2. MATERIAL AND METHODS 2.1 MATERIAL: INCONEL718 Machining of Nickel based super alloys is a challenging task due to more reasons than one. But it is worth to take up these challenges since these alloys are very popular in the industry due to their superior properties. They have excellent high temperature strength, high corrosion and oxidation resistance as well as resistance to thermal fatigue, thermal shock, creep and erosion [8].Among Nickel based alloys, Inconel718 is the most widely employed construction material in the aerospace industry, in particular in hot sections of gas turbine engines. Due to high shear strength, low thermal conductivity, tendency to form Built Up Edge (BUE), chemical reaction tendency at high temperatures, and high abrasive carbide particles in the micro structure and work hardening tendency, this alloy is classified under the category of difficult to machine material. During machining process, the interaction between the tool and workpiece causes severe plastic deformation in the local areas of the workpiece, and intense friction at the tool work interface resulting in excessive tool wear, low productivity and high power consumption [9]. 2.2 Taguchi method Taguchi philosophy provides two tenets (1) reduction in variation (improved quality) of a product or process which represents a lower loss to society, and (2) the proper development of a strategy that intentionally reduce variation.[10] Taguchi method is an experimental technique which is useful in reducing the number of experiments dramatically by using Orthogonal Arrays and also tries to minimize the effects of factors out of control. The greatest advantage of Taguchi method is to decrease the experimental time, to reduce the cost and to find out the significant factors in a shorter time period. The most reliable of Taguchi techniques is the use of parameter design, which is an engineering method for product or process design that focuses on determining the parameter (factor) settings producing the best levels of a quality characteristics (performance measure) with minimum variations[11]. Taguchi converts the objective function values to Signal-to-Noise ratio (S/N ratio) to measure the performance characteristics of the levels of control factors. [9].The S/N ratio takes both the mean and variability into account. In its simplest form, the S/N ration is the ratio of the mean (signal) to the standard deviation (noise).[12] The S/N ratio depends on the criteria of the quality characteristics to be optimized. Depending upon the type of quality characteristics to be optimized, there are three important types of S/N ratios defined. They are (a) Smaller- the- Better Type (STB) --------------------------- (02) (b) Larger-the-Better Type (LTB) ------------------- (03)
  4. 4. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 4, July - August (2013) © IAEME 128 (c) Nominal-the-Best, ------------------------- (04) Where yi is the measured response in ith run, ‘n’ is the number of observations in a row. ‫ݕ‬ത is the average of the observed data and s2 is the variance. Since minimum surface roughness value is desirable, Smaller - the- Better Type of quality characteristics is used (Eqn - 2). [11-13] Analysis of Variance (ANOVA) is used to determine the statistical significance of the control parameters. The optimum combination of cutting parameters is determined with the help of main effect plots. 2.3 MULTIPLE REGRESSION ANALYSIS (MRA) The objective of multiple regression analysis is to construct a model that explains as much as possible, the variability in a dependent variable, using several independent variables. The model fit is called regression model, is usually a linear model, though sometimes non linear models such as log- linear models are also constructed. To include interaction terms, the following model is used in this investigation. Yi = ß0 + ß1 x1 + ß2 x2 +.....+ ßm xm + ß12 x1x2 + ß13x1x3 +....+ ß1m x1 xm + .---(05) Where Yi is the dependent variable and X1 …………… Xm are the independent variables is the error term. The coefficients, ß0,, ß1……………ß m , ß12, ß13……… ß1m are constants.[14] The fitted model can be utilized to estimate the values of the responses. 3. EXPERIMENTAL 3.1 Work Material and Tool Turning experiment was performed on CNC lathe with Inconel 718 rod of 25mm diameter and 100mm length (Fig.1) using uncoated carbide turning insert of Sandvik Coromant make (Fig.2) with ISO specification numbers as given below. 1. CNMG12 04 04-QM H13A 2. CNMG12 04 08-QM H13A 3 CNMG12 04 12-QM H13A FIG. 1 INCONEL718 FIG.2 UNCOATED CARBIDE WORKPIECE MATERIAL TURNING INSERT
  5. 5. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 4, July - August (2013) © IAEME 129 3.2 MEASUREMENT OF SURFACE ROUGHNESS In this investigation, surface roughness (Ra) is measured by MITUTOYO SJ210 SURF TEST, a stylus type profilometer (Fig3) and its specifications are given in table 1. Each surface is characterized by the average surface roughness Ra value. The cut off length λc and the sampling number (N) are selected as 0.8mm and5 respectively, and travel length selected is 4mm. In total four different measurements in the scan direction are taken on the textured surface. The average of those four measurements is used to find out the ultimate Ra values. Table1. Specifications of SURFTEST SJ-210 FIG 3. SURFTEST SJ-210 Portable Surface Roughness Tester Sl.No. Details Values 1 Measurement Range 360µm 2 Stylus Diamond 3 Tip radius 5 µm 4 Measuring Force 4mN 5 Ditector range 21mm 6 Transverse speed 0.25mm/s(measurement) 1mm/s(return) 7 Resolution 0.0016 µm
  6. 6. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 4, July - August (2013) © IAEME 130 4. SELECTION OF STANDARD ORTHOGONAL ARRAY AND ITS CONSTRUCTION 4.1 Basis for Selection of Orthogonal Array Before constructing an orthogonal array, the following requirements must be defined: • Number of factors to be studied • Number of levels for each factor • Specific 2-factor interactions to be estimated 4.2 COUNTING DEGREES OF FREEDOM The first step in constructing an Orthogonal Array to fit a specific case study is to count the total degrees of freedom which tell the minimum number of experiments that must be performed to study all the chosen control factors. To begin with, one degree of freedom is associated with the overall mean regardless of the number of control factors to be studied. In general, the number of degrees of freedom associated with a factor is equal to one less than the number of levels for that factor. The degrees of freedom associated with interaction between two factors are given by the product of the degrees of freedom for each of the two factors. A suitable OA is selected based on the Degree of freedom [13] 4.3 EXPERIMENTAL DESIGN Number of parameters = 4 Number of levels for each parameters = 3 Total degree of freedom (DOF) for 4 parameters = 4× (3-1) = 8 Number of interactions considered (AXB), (AXD), and (BXD) Degree of freedom for interactions= 3X2X2=12 Therefore Minimum number of experiment = Total DOF for parameters +1 = 20 + 1 Minimum number of experiment = 21 L27(3)13 orthogonal array of Taguchi is selected. 5. CONSTRUCTION OF ORTHOGONAL ARRAYS 5.1 Factors with codes and Levels Table2. Parameter combinations for Experiment with four factors and three levels for INCONEL 718 using uncoated carbide tool [15-17] Parameters/Factors Levels 1 2 3 Speed(A) m/min 25 30 35 Feed(B) mm/rev 0.08 0.1 0.12 Depth of cut(C) mm 0.15 0.35 0.55 Nose radius(D)mm 0.4 0.8 1.2
  7. 7. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 4, July - August (2013) © IAEME 131 6. FACTOR ASSIGNMENTS ON L27 ORTHOGONAL ARRAY TABLE3. Factor Assignments For INCONEL 718 and Experimental Results TOOL: UNCOATED CARBIDE CNMG120404-QM H13A, CNMG120408-QMH13A, CNMG120412-QM H13A Expt.N0. Column numbers and factor assignments Ra Measured S/N Ratio Ratio Ra predicted 1 2 3 5 6 8 9 A B A*B D A*D B*D C µm (dB) µm 1 25 0.08 1 0.4 1 1 0.15 0.559 5.0518 0.57984 2 25 0.08 1 0.8 2 2 0.35 0.448 6.9744 0.44355 3 25 0.08 1 1.2 3 3 0.55 0.317 9.9788 0.30297 4 25 0.1 2 0.4 1 2 0.35 0.767 2.3041 0.77100 5 25 0.1 2 0.8 2 3 0.55 0.636 3.9309 0.63245 6 25 0.1 2 1.2 3 1 0.15 0.463 6.6884 0.47157 7 25 0.12 3 0.4 1 3 0.55 0.955 0.3999 0.96199 8 25 0.12 3 0.8 2 1 0.15 0.782 2.1359 0.80314 9 25 0.12 3 1.2 3 2 0.35 0.641 3.8628 0.66482 10 30 0.08 2 0.4 2 1 0.35 0.591 4.5683 0.59534 11 30 .008 2 0.8 3 2 0.55 0.461 6.7260 0.45702 12 30 0.08 2 1.2 1 3 0.15 0.288 10.8122 0.29666 13 30 0.1 3 0.4 2 3 0.55 0.78 2.1581 0.77954 14 30 0.1 3 0.8 3 2 0.15 0.607 4.3362 0.61188 15 30 0.1 3 1.2 1 1 0.35 0.64 3.8764 0.51254 16 30 0.12 1 0.4 2 3 0.15 0.926 0.6678 0.89889 17 30 0.12 1 0.8 3 1 0.35 0.785 2.1026 0.78319 18 30 0.12 1 1.2 1 2 0.55 0.654 3.6884 0.67028 19 35 0.08 3 0.4 3 1 0.55 0.605 4.3649 0.61090 20 35 0.08 3 0.8 1 2 0.15 0.431 7.3105 0.45281 21 35 0.08 3 1.2 2 3 0.35 0.29 10.7520 0.31426 22 35 0.1 1 0.4 3 2 0.15 0.751 5.0518 0.72345 23 35 0.1 1 0.8 1 3 0.35 0.609 6.9744 0.60828 24 35 0.1 1 1.2 2 1 0.55 0.479 9.9788 0.49462 25 35 0.12 2 0.4 3 3 0.35 0.928 2.3041 0.91236 26 35 0.12 2 0.8 1 1 0.55 0.798 3.9309 0.82207 27 35 0.12 2 1.2 2 2 0.15 0.625 6.6884 0.64060
  8. 8. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 4, July - August (2013) © IAEME 132 7. RESULTS AND DISCUSSION 7.1. Analysis Using MINITAB Software After machining work pieces, response values were noted down (Table3) and with the help of MINITAB14 software optimum levels of control factors determined based on S/N ratios. ANOVA was performed to find out the influence of various factors on objective functions. Multiple Regression Analysis (MRA) was performed to construct mathematical models and to estimate Ra. Experiments were conducted on INCONEL718 based on L27 Orthogonal Array using uncoated carbide turning insert. 7.2 Taguchi Analysis: Ra versus SPEED, FEED, DOC, NR Table4. Response Table for Signal to Noise Ratios for Ra Smaller is better Level SPEED FEED DOC NR 1 4.592 7.393 4.841 2.517 2 4.326 4.054 4.377 4.420 3 4.701 2.172 4.400 6.682 Delta 0.375 5.221 0.464 4.165 Rank 4 1 3 2 Table5. Analysis of Variance for S/N Ratio of Ra Source DF SS MS F P Contribution (%) SPEED 2 0.668 0.334 0.375 0.493 0.31 FEED 2 125.859 62.929 70.627 0.000 57.98 DOC 2 1.231 0.6155 0.691 0.144 0.567 NR 2 78.249 39.1245 43.91 0.000 36.05 SPEED*FEED 4 0.459 0.115 0.129 0.610 0.21 SPEED*NR 4 0.231 0.06 0.067 0.723 0.17 FEED*NR 4 5.031 1.258 1.412 0.019 2.32 Error 06 5.343 0.891 01 0.41 Total 26 217.072 S = 0.667293 R-Sq = 97.54% R-Sq (adj) = 94.67% 7.3 Regression Analysis: Ra versus SPEED, FEED, DOC AND NR The regression equation is Ra = 0.0194 - 0.00058 SPEED + 8.62 FEED + 0.0753 DOC - 0.344 NR + 0.0104 SPEED*FEED - 0.000508 SPEED*NR - 0.141 FEED*NR---- (6)
  9. 9. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 4, July - August (2013) © IAEME 133 Table6. Regression Table Predictor Coef T P Constant 0.01937 0.27 0.793 SPEED -0.000578 -0.36 0.725 FEED 8.6222 21.32 0.000 DOC 0.07530 1.85 0.080 NR -0.34362 -16.96 0.000 SPEED*FEED 0.010446 1.42 0.171 SPEED*NR -0.0005082 -1.01 0.327 FEED*NR -0.1414 -0.97 0.344 Analysis of Variance Source SS F P Regression 0.88107 106.91 0.000 Residual Error 0.02237 Total 0.90344 S = 0.0343120 R-Sq = 97.5% R-Sq (adj) = 96.6% FIG.4. Main Effects Plot (data means) for SN ratios MeanofSNratios 353025 8 6 4 2 0.120.100.08 0.550.350.15 8 6 4 2 1.20.80.4 SPEED FEED DOC NR Main Effects Plot (data means) for SN ratios(Ra) Signal-to-noise: Smaller is better
  10. 10. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 4, July - August (2013) © IAEME 134 FIG.5.Interaction Plot (data means) for SN ratios FIG.6 Comparison of Measured and Predicted Values of Ra Table7. Optimum parameter settings Factor Level Value Speed (A) A3 35m/min Feed(B) B1 0.08mm/rev DOC(C) C1 0.15mm NR(D) D3 1.2mm SP EE D 10 5 0 F E ED NR 1.20.80.4 0.120.100.08 10 5 0 353025 10 5 0 SP EED 35 25 30 F EED 0.12 0.08 0.10 NR 1.2 0.4 0.8 Interaction Plot (data means) for SN ratios(Ra) Signal-to-noise: Smaller is better
  11. 11. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 4, July - August (2013) © IAEME 135 7.4. CONFIRMATION EXPERIMENT. Confirmation experiment was conducted with the optimal parameter settings by taking four trials and the results are shown in Table 8.The mean Ra value=0.286 µm Table 8.Results of Confirmation Experiment Factor Level Value Trial No. Ra(µm) Speed (A) A3 35m/min 1 0.286 Feed(B) B1 0.08mm/rev 2 0.285 DOC(C) C1 0.15mm 3 0.289 NR(D) D3 1.2mm 4 0.285 mean 0.286 When factor values are substituted in the mathematical model equation 6, it has given Ra value =0.29 µm. The difference in surface roughness values observed is only 0.004 µm which is negligibly small and hence the model is validated. Ra value of 0.286 µm is lower than the lowest measured surface roughness value observed in table 3indicating optimum factor level A3B1C1D3 is more or less satisfied 7.5. Optimization of Ra The main effects of each parameter on Ra, are plotted on graphs shown in figure 4 for mean values of S/N ratios for each level of control variables. These figures clearly indicates how speed, feed, DOC and tool nose radius changes and affects the modified parameter S/N ratio. Figure shows that, with the increase in speed, the S/N ratio initially decreases by a small value and subsequently it increases leading to a resultant increase of S/N ratio and decrease in Ra value. With the increase in feed, S/N ratio decreases implying an increase in roughness value. With the increase in depth of cut, there is a resultant decrease in S/N ratio and increase in Ra value. With the increase in nose radius, there is a resultant increase in S/N ratio and a reduction in surface roughness. Greater the value of S/N ratio for each parameter minimizes Ra. So the optimum conditions for achieving minimum surface roughness is cutting speed 35m/min(A3),feed 0.08mm/rev (B1), DOC 0.15mm(C1) and nose radius 1.2mm(D3). This implies that maximum cutting speed of 35m/min, minimum feed rate of 0.08mm/rev, minimum depth of cut of 0.15mm and maximum nose radius of 1.2mm optimizes the response. The response table for the average value of S/N ratios for each level of parameters is displayed in table 4 and this is utilized to find out their relative importance and to rank them based on the differences in the average values. It is found that feed is the most important parameter that influences Ra followed by NR and interaction between feed and nose radius. DOC plays the next important role where as speed is having least effect on Ra value. Analysis of Variance (ANOVA) has been performed to investigate the statistical significance of parameters at 95% confidence level and to determine the percentage of contribution of parameters to the process response The significance of each parameter was tested using probability values (p- value).When the p-value in the ANOVA table for S/N ratios is less than 0.05for a confidence level of 95%, it is considered as statistically significant. In addition, the percentage of contribution expresses the importance of the parameters for the response. From the result of ANOVA shown in table 5, it is found that the most significant parameter is feed and its contribution is (57.98%) followed by NR with a contribution of (36.05%). Interaction between Feed and Nose radius is depicted in fig 5 and is having a small contribution of
  12. 12. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 4, July - August (2013) © IAEME 136 (2.32%).Other factors are not having significant effects since ‘p’ values are more than 0.05 for a confidence level of 95%. To establish a mathematical relationship between parameters and responses, the linear regression analyses were performed and the following equations were developed. Ra = 0.0194 - 0.00058 SPEED + 8.62 FEED + 0.0753 DOC - 0.344 NR+ 0.0104 SPEED*FEED - 0.000508 SPEED*NR - 0.141 FEED*NR The significance of each coefficient in the equation and the regression model were analyzed by ANOVA and tested by the probability (p value). Both the regression statistics and ANOVA results for regression models are reported in table 6. Regression statistics indicate that coefficients for feed and nose radius are statistically significant. ANOVA results of the regression shows that regression model for Ra is statistically significant at 95% confidence level (p < 0.05) and at least one of the regressor variables (control factors) is significantly related to response Ra. The predicted values of the responses were compared with the measured values and the result is depicted as a graph in fig.6.The graph shows that the predicted values are in reasonable agreement with measured values. The value of R2 (97.54%) implies that 97.54% of variation in response values can be explained by the variations in the control factors considered. A high value of determination coefficient ensures model adequacy, goodness of fit and high significance of model. This indicates that the regression model for the response can be used for determining and estimating Ra surface roughness value. 8. CONCLUSIONS An investigation has been carried out to assess the effect of control parameters on the surface roughness value in the turning process of INCONEL718 using uncoated carbide inserts. The experiments were performed based on L27 Orthogonal array applying Taguchi’s technique. The input parameters were cutting speed, feed, depth of cut and tool nose radius and the performance characteristic investigated is Surface Roughness. The ANOVA was performed to evaluate the statistical significance of each parameter on the performance characteristic. The relation between input and output parameter is modeled using Multiple Regression Analysis for the estimation of Ra. Mathematical model obtained by regression analysis and the equation (6) is reported in the main text. Summary of the experimental results are tabulated and shown in table7 and results of confirmation experiment are shown in table8. Based on the results of theoretical analysis following conclusion are drawn 1) It is observed that the most significant parameter which affects surface roughness is feed and its contribution is (57.98%) followed by NR with a contribution of (36.05%).Interaction between feed and nose radius is having a contribution of 2.32% and other factors are not having significant effects at a confidence level of 95%. 2) Linear regression model constructed using MINITAB software is used to predict the Surface roughness Ra. 3) A comparison has been made between measured values and predicted values and it is shown in fig.6. 4) Confirmation experiment using optimized parameter settings reported a surface roughness value of 0.286 µm which is in accordance with the roughness value predicted by the mathematical model of MRA within the acceptable range of errors; hence it validates the mathematical model. 5) Eventually it is concluded that Taguchi method is suitable for parametric optimization of turning and MRA can predict responses reliably.
  13. 13. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 4, July - August (2013) © IAEME 137 REFERENCES [1]. ASM International (1999), “ASM Hand Book, Vol.16,Machining” 9th Ed. Metals park, Ohio. [2]. Mummer Nalbant et. al. (2007) “Comparison of Regression and Artificial Neural Network models for surface roughness prediction with the cutting parameters in CNC turning” Int. Journal of Modeling and Simulation Engineering, Vol.12 [3]. B.L.Juneja et. al. (1995) “Fundamentals of Metal Cutting and Machine Tools” New Age International Pvt.Ltd, New Delhi 1995. [4]. Chinnasamy Natarajan & S. Muthu & P. Karuppuswamy(2011) “Prediction and analysis of surface roughness characteristics of a non-ferrous material using ANN in CNC turning” Int. J. Adv. Manuf Technol. [5]. Gerd Wagner (1999). “Widia Tools Booklet on machining” Widia India Ltd. [6]. P.P.Shirpurkar, S.R. Bobde, V.V.Patil, B.N. Kale (2012) “Optimization of Turning Process Parameters by Using Tool Inserts- A Review” International Journal of Engineering and Innovative Technology (IJEIT) Volume2, Issue6, [7]. P Hariharan et. al. (2008) “Optimization of Heat Treatment Factors for Improved Wear Resistance and Impact Strength of Bearing Steel using L16 Orthogonal Array” Journal of Manufacturing Engg. Vol.1 pp 45-50. [8]. Ahmet Hasc-alık , MustafaAy ‘CO2 laser cut quality of Inconel718 nickel–based superalloy’ Int.j Optics & Laser Technology (2013)pp554-564. [9]. Dahu Zhu n, XiaomingZhang, HanDing (2013), “Tool wear characteristics in machining of nickel-based super alloys.”International Journal of Machine Tools & Manufacture” pp 60– 77 [10]. Philip J Rose (1999),”Taguchi Techniques for Quality Engineering”1st Ed. McGraw-Hill Book Company,New York. [11]. Ilhan Asilturk , Harun Akkus (2011),“Determining the effect of cutting parameters on surface roughness in hard turning using the Taguchi method “Int. Journal on Measurement pp 1697– 1704 [12] Mustafa Gunay, Emre Yucel, (2013), “Application of Taguchi method for determining optimum surface roughness in turning of high-alloy white cast iron”, Int. Journal on Measurement pp 913–919 [13]. Madhav S. Phadke(1989), “Quality Engineering using Robust Design” Prentice Hall, Eaglewood Cliffs,New Jersy [14]. Douglas C Montgomery (2009), “Design and Analysis of Experiments” 5 th Ed. John Wiley and sons pvt. Ltd. Singapore [15]. Serope Kalpakjian, Steven R.Schmid (2000), “Manufacturing Engineering and Technology” 4th Ed. Pearson Education, Asia [16]. Gerd Wagner (1999) “Widia Tools Booklet on Machining” Widia India Ltd. [17]. Sandvik Coromant Product Information Brochure (2013). [18]. M Manohar, Jomy Joseph, T Selvaraj and D Sivakumar, “Development of Models using Genetic Programming for Turning Inconel 718 with Coated Carbide Tools”, International Journal of Design and Manufacturing Technology (IJDMT), Volume 4, Issue 1, 2013, pp. 1 - 13, ISSN Print: 0976 – 6995, ISSN Online: 0976 – 7002. [19]. B.Satyanarayana, G. Ranga Janardhana and D. Hanumantha Rao, “Modeling and Optimization of Cutting Parameters in High-Speed Dry Machining of Inconel 718 Alloy”, International Journal of Advanced Research in Engineering & Technology (IJARET), Volume 4, Issue 4, 2013, pp. 242 - 252, ISSN Print: 0976-6480, ISSN Online: 0976-6499.

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