Analysis of Surface Roughness in Turning with Coated Carbide Cutting Tools: Prediction Model and Cutting Conditions Optimization

Transcription

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 Analysis of Surface Roughness in Turning with Coated Carbide Cutting Tools: Prediction Model and Cutting Conditions Optimization A. J. Makadia 1*, J. I. Nanavati 2 1* Darshan Inst. of Engg. And Tech., Rajkot , ajmakadia@yahoo.com 2 Faculty of Engg. And Tech., Baroda , jinanavati@hotmail.com Abstract This investigation focuses on the influence of cutting parameters (cutting speed, feed rate and depth of cut) and tool geometry (tool nose radius) on the surface roughness in turning of AISI 1040 steel using coated carbide cutting tools. The machining experiments were conducted based on (3 4 ) full factorial design. The results indicated that the feed rate was the dominant factor on the surface roughness; on the other hand both tool nose radius and cutting velocity have statistical significance on surface roughness. Response contour and surface plots are generated for the study of interaction effects of cutting conditions and tool geometry on surface roughness. The analysis of results revealed that combination of low feed rate, high tool nose radius and high cutting speed is necessary for minimizing the surface roughness. Response surface optimization shows that the optimal combination of machining parameters are ( m/min, 0.1 mm/rev, 0.3 mm, 0.91 mm) for cutting velocity, feed rate, depth of cut and tool nose radius respectively. In addition, a good agreement between the predicted and measured surface roughness on the machining of AISI 1040 steel with 95% confidence interval within ranges of parameters studied. Keywords: RSM, Surface roughness, Turning 1 Introduction In modern industry, the goal is to manufacture low cost and high quality products in a short time. Automated and flexible manufacturing systems are employed for that purpose along with computerized numerical control (CNC) machine tools that have become very common in factories and are capable of achieving high accuracy and very low processing time. Turning is the first and most common method for cutting, especially for the finished machined parts. In machining of parts, surface quality is one of the most specified customer requirements where major indication of surface quality on machined parts is surface roughness. Surface roughness is one of the main results of process parameters such as tool geometry (nose radius, edge geometry and rake angle) and cutting conditions (feed rate, cutting speed and depth of cut). Furthermore, it is a significant design specification that is known to have considerable influence on properties such as wear resistance and fatigue strength. The quality of the surface is a factor of importance in the evaluation of machine tool productivity. Hence, it is important to achieve a consistent tolerance and surface finish. When surface finish becomes the main criteria in the quality control department, the productivity of the metal cutting operation is limited by the surface quality. Today, statistical design of experiments is used quite extensively. Statistical design of experiments refers to the process of planning the experiments so that the appropriate data can be analysed by statistical methods, resulting in valid and objective conclusions (1997). Noordin et al. (2004) studied the application of response surface methodology in describing the performance of coated carbide tools when turning AISI 1045 steel. The Taguchi method was used by (2006) to find the optimal cutting parameters for turning operations. Choudhury and El-Baradie (1998) had used RSM and 2 3 factorial design for predicting surface roughness when turning high-strength steel. Fang and wang (2002) developed an empirical model for surface roughness using two level fractional factorial design (2 5-1 ) with three replicates considering work piece hardness, feed rate, cutting tool point angle, cutting speed and cutting time as independent parameters using non linear analysis. Paulo Davim (2001), the cutting velocity has greater influence on the roughness followed by the feed and depth of cut has no significant influence on surface roughness found by using the Taguchi method. Sahin and Motorcu (2005) used 2 3 factorial design for the development of surface roughness model for turning of mild steel with coated carbide tools. Petropoulos et al. (2008) had used multi regression analysis and ANOVA for statistical study of surface roughness in turning of PEEK composite. Galanis and Manolakos (2010) used 2 3 full factorial design for AISI 316L steel with three variables named feed, speed and depth of cut for application of femoral head. Suleyman Neseli et al. and Ashvin Makadia (2011, 2013) used optimization of machining parameters for turning operation based on response surface methodology for AISI 1040 and AISI 410 steel. Lalwani et al. (2008) used RSM for investigations of cutting parameters 20-1

2 Analysis of Surface Roughness in Turning with Coated Carbide Cutting Tools: Prediction Model and Cutting Conditions Optimization influence on cutting forces and surfaces finish in hard turning of MDN250 steel. Kini and Chincholkar (2010) have used two level full factorial design to study the effect of machining parameters on surface roughness and material removal rate in finish turning of glass fibre reinforced polymers. Joseph Davidson et al. (2008) used response surface methodology to study the effect of main flow forming parameters such as the speed of the mandrel, the longitudinal feed and amount of coolant used on surface roughness of flow formed AA6061 tube. Mohamed Dabnum et al. (2005) describe the development of surface roughness model for turning glass ceramic utilizing design of experiment and response surface methodology. Ramesh et al. (2012) used Taguchi method to study the effect of cutting parameters on the surface roughness in turning of titanium alloy using response surface methodology. Gaitonde (2009) used (3 3 ) full factorial design for the analysis of machinability during hard turning of cold work tool steel (AISI D2) using response surface methodology. The aim of the present study was, therefore to develop the surface roughness prediction model and optimization of machining parameters of AISI 1040 steel with the aid of statistical method, using coated carbide cutting tools. By using response surface methodology and (3 4 ) full factorial design of experiment, second-order model has been developed with 95% confidence level. 2 Response Surface Methodology RSM is the collection of mathematical and statistical techniques that are useful for the modeling and analysis of problems in which a response of interest is influenced by several variables and the objective is to optimize the response. In Response Surface Methodology (RSM), the factors that are considered as most important are used to build a polynomial model in which the independent variable is the experiment s response. In order to know the surface quality and dimensional properties in advance, it is necessary to employ theoretical models, making it feasible to do prediction of operating conditions In many engineering fields, there is a relationship between output variables of interest and a set of controllable input variables,,,. In some system, the nature of the relationship between and values may be known. Then, a model can be written in the form =,,, +, (1) where represents error observed in the response. If we denote the expected response as, =,,, = then the surface represented by =,,, (2) is called response surface. In most of the RSM problems, the form of the relationship between the response and the independent variable is unknown. Thus the first step in RSM is to find out a suitable approximation for the true functional relationship between and set of independent variables employed. Usually a second order model is utilized in RSM.The coefficient used in the model below can be calculated by means of least square method: = (3) The second order model is normally used when the response function is not known or nonlinear. 3 Experimental Details In this study, the experiments were planned using 3 4 full factorial design with 81 numbers of experiments. The four cutting parameters are selected for the present investigation is cutting speed (v), feed (f), nose radius (r) and depth of cut (d). Since the multi level variables and their outcome effects are not linearly related, it has been decided to use three level tests for each factor. The machining parameters used and their levels are given in Table 1. All the turning experiments were conducted on a Jobber X L model made by Ace designer CNC lathe machine with variable spindle speed RPM and 7.5 KW motor drive was used for machining tests. The machining tests were carried out in wet conditions using a water-soluble cutting fluid. In this study, ceramic inserts (supplied by Ceratizit) were used, ISO code TNMG EN-TMF, TNMG EN- TM and TNMG EN-TM with different nose radius (60 0 triangular shaped inserts). The inserts were mounted on a commercial tool. In the present investigation, the bar of AISI 1040 steel with the following chemical composition were used as the work material: 0.047% C, 0.140% Si, 0.781% Mn, 0.019% P, 0.037% S, % Cr, % Ni and % Mo. Surface finish of the work piece material was measured by Surf test model No. SJ-400 (Mitutoyo make). The surface roughness was measured at three equally spaced locations around the circumference of the work pieces to obtain the statistically significant data for the test. The result table from the machining test performed as per the 3 4 full factorial design are not shown here. These results are fed into the Minitab-16 for analysis. 4 Result and Discussion The analysis of variance (ANOVA) is used to check the adequacy of the proposed quadratic model. Table 2 shows ANOVA for the response surface quadratic model for surface roughness. The value of p in Table 2 for model is less than 0.05 which indicates that the model is adequately significant at 95% confidence level, which is desirable as it 20-2

3 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 Table 1 Input parameters and their levels Sr. No. Parameters Level 1 Level 2 Level 3 1 Cutting speed (v) Feed (f) Depth of cut (d) Nose radius (r) Table 2 Analysis of variance for second order mode Source DF Seq SS Adj SS Adj MS F value p value Regression Linear Cutting speed (v) Feed (f) Nose radius(r) Depth of cut (d) Square Cutting speed (v)* Feed (f)*feed (f) Nose radius(r)*nose radius(r) Depth of cut (d)*depth of cut (d) Interaction Cutting speed (v)*feed (f) Cutting speed (v)*nose radius(r) Cutting speed (v)*depth of cut (d) Feed (f)*nose radius(r) Feed (f)*depth of cut (d) Nose radius(r)*depth of cut (d) Residual Error Total S = PRESS = R-Sq = 97.11% R-Sq (pred) = 95.67% R-Sq (adj) = 96.50% indicates that the term in the model have a significant effect on the response. Similarly, the main effect of cutting speed (v), feed (f) nose radius(r) and two level interaction of cutting speed and nose radius (v r), feed and nose radius (f r) also square effect of v 2, f 2 and r 2 are significant model terms. Other model terms are not significant. From response surface Eq.4 the most significant factor on the Surface roughness is feed rate. The next contribution on surface roughness is nose radius and cutting speed. The effectiveness of the model has been checked by using the R 2 value. In present work, R 2 value is and the Adj. R 2 is The predicted R 2 value is in reasonable agreement with Adj. R 2 value. The R 2 value in this case is high and close to 1, which is desirable. The empirical quadratic model for surface roughness (Ra) is given below: Ra = v f d r v f d r vf vd vr fd f r d r (4) The diagnostic checking of the model has been carried out using residual analysis and the results are presented in Figs.1 and 2. The normal probability plot is presented in Fig. 1. The figure revealed that the residuals fall on a straight line implying that the errors are distributed normally. Figure 2 shows the standardized residuals with respect to the predicted values. The residuals do not show any obvious pattern and are distributed in both positive and negative direction. This implies that the model is adequate and there is no reason to suspect any violation of the independence or constant variance assumption. The analysis of response variable surface roughness can be explained through counter and surface plots. The typical three-dimensional (3D) surface plots and twodimensional (2D) contour plots for surface roughness 20-3

4 Analysis of Surface Roughness in Turning with Coated Carbide Cutting Tools: Prediction Model and Cutting Conditions Optimization Normal Probability Plot (response is Roughness (Ra)) Versus Fits (response is Roughness (Ra)) Percent Residual Residual Fitted Value Figure 1 Normal probability plots of residual values for Ra Figure 2 Residual vs. Fitted roughness values Contour Plot of Roughness(Ra) vs Feed(f), Depth of cut(d) Nose radius(r) Contour Plot of Roughness(Ra) vs Feed(f), Nose radius(r) Depth of cut(d) Feed(f) Feed(f) Nose radius(r) Figure 3 Estimated contour plots for Ra (Const. d & r) Figure 4 Estimated contour plots for Ra (Const. v & d) Surface Plot of Roughness(Ra) vs, Nose radius(r) Feed(f) 0.1 Depth of cut(d) 0.3 Surface Plot of Roughness(Ra) vs Depth of cut(d), Feed(f) 0.1 Nose radius(r) Roughness(Ra) Nose radius(r) Roughness(Ra) Depth of cut(d) 0.4 Figure 5 3D Surface roughness plots for (Ra Vs r & v) Figure 6 3D Surface roughness plots for (Ra Vs v & d) 20-4

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 Surface Plot of Roughness(Ra) vs Feed(f), Depth of cut(d) 0.3 Nose radius(r) Roughness(Ra) Feed(f) Figure 7 3D Surface roughness plots for (Ra Vs v &f) Figure 8 Comparison of Exp. and predicted values for Ra Table 3 Response optimization for surface roughness parameters Parameters Goal Optimum conditions Lower Target Upper Pre. resp. Desirability v f d r Ra Minimum in terms of the process variable are shown in Figs Eq. (4) is plotted in Figs. 3 and 4 as contours for each of the response surfaces at different values. These response contours can help in the prediction of roughness in the experimental domain (2008, 2010). It is clear from these figures that the surface roughness reduces with the increase of cutting speed. However, it increases with the increase of feed and decreases with increasing tool nose radius. The surface plot shows the influence of different machining variables, keeping the other variable at constant level. Figure 5 illustrates the surface model for surface roughness by varying the two variables nose radius and cutting speed and keeping the two parameters feed and depth of cut at constant level. The figure indicates that the surface roughness decreases with increase in nose radius and cutting speed. Figure 6 shows the effect of cutting speed with respect to depth of cut on surface roughness. From the figure, it has been asserted that the increase of cutting speed reduces the surface roughness while depths of cut have no significant effect on the surface roughness. Figure 7 shows the influence of cutting speed and feed on surface roughness by keeping the nose radius and depth of cut at constant level. From the figure, it can be asserted that the increases in feed increase the surface roughness while increasing cutting velocity reduces the surface roughness. The effectiveness of the model has been checked by validation with experimental results. In order to verify the adequacy of the model developed, five confirmation run experiments have been performed (Fig. 8) at different cutting conditions. The test condition for the first three validation run experiments are among the cutting conditions that are performed previously while the remaining two validation run experiments are the conditions that have not been used previously. The experimental results have been validated by asserting that the predicted values are very close to each other and hence, the developed models are suitable for predicting the surface roughness in machining AISI 1040 steel. One of the most important aims of experiments related to manufacturing is to achieve the desired surface roughness of the optimal cutting parameters. Response surface optimization is an ideal technique for determination of the best cutting parameters in turning operation. Here, the goal is to minimize surface roughness (Ra). RSM optimization results for surface parameters are shown in Table 3. Optimum machining parameters are found to be cutting velocity of m/min, feed of 0.1mm/rev, depth of cut of 0.3 mm and tool nose radius of 0.91mm. The optimized surface roughness parameter is Ra = µm. 5 Conclusion In this paper, application of RSM on the AISI 1040 steel is carried out for turning operation. A quadratic model is developed for surface roughness 20-5

6 Analysis of Surface Roughness in Turning with Coated Carbide Cutting Tools: Prediction Model and Cutting Conditions Optimization (Ra) to investigate the optimum machining parameters. The results are as follows: 1. The established equations clearly show that the feed is the main factor which influences surface roughness followed by tool nose radius and cutting speed. Depths of cut have no significant effect on the surface roughness. 2. 3D and 2D surface counter plots are useful in determining the optimum condition to obtain particular values of surface roughness. 3. Response surface optimization shows that the optimal combination of machining parameters are ( m/min, 0.1 mm/rev, 0.3 mm, 0.91 mm) for cutting velocity, feed rate, depth of cut and tool nose radius respectively. 4. The predicted and the measured values are satisfactorily close to each other which indicates that the developed surface roughness prediction model can be effectively used for predicting the surface roughness during the machining AISI 1040 steel with 95% confident level References Montgomery D.C. (1997), Design and Analysis of Experiments. 4th ed., John Wiley, New York. Noordin M.Y., Venkatesh V.C., Sharif S, Elting S and Abdullah A.( 2004), Application of response surface methodology in describing the performance of coated carbide tools when turning AISI 1045 steel, J Mater Process Technol., Vol. 145, pp Kirby E.D., Zhang Z., Chen J.C. and Chen J. (2006), Optimizing surface finish in a turning operation using the Taguchi parameter design method, Int J Adv Manuf Technol., Vol. 30, pp Choudhury I.A. and El- Baradie M.A. (1998), Tool life prediction model by design of experiments for turning high strength steel, J Mater Process Tech., Vol. 77, pp Feng C.X. and Wang X. (2002), Development of Empirical Models for Surface Roughness Prediction in Finish Turning, Int J Adv Manuf Technol., Vol. 20, pp Paulo Davim J. (2001), A note on the determination of optimal cutting conditions for surface finish obtained in turning using design of experiments, J Mater Process Technol., Vol. 116, Sahin Y. and Motorcu A.R., (2005), Surface roughness model for machining mild steel with coated carbide tool, Mater Design, Vol. 26, pp Petropoulos G., Mata F. and Paulo Davim J. (2008), Statistical study of surface roughness in turning of peek composites, Mater Design, Vol. 29, pp Galanis N.I. and Manolakos D.E. (2010), Surface roughness prediction in turning of femoral head, Int J Adv Manuf Technol., Vol. 51, pp Neseli S., Yaldız S. and Turkes E. (2011), Optimization of tool geometry parameters for turning operations based on the response surface methodology, Measurement, Vol. 44, pp Makadia A.J. and Nanavati J.I. (2013) Optimization of machining parameters for turning operations based on response surface methodology, Measurement, Vol. 46, pp Lalwani D.I., Mehta N.K. and Jain P.K. (2008), Experimental investigations of cutting parameters influence on cutting forces and surface roughness in finish hard turning of MDN250 steel, J Mater Process Technol. Vol. 206, pp Kini M.V. and Chincholkar A.M. (2010), Effect of machining parameters on surface roughness and material removal rate in finish turning of glass fibre reinforced polymer pipes, Mater Design, Vol. 31, pp Davidson M.J Balasubramanian K. and Tagore G.R.N.(2008) Surface roughness prediction of flowformed AA6061 alloy by design of experiments, J Mater Process Technol., Vol. 202, pp Mohamed D.A., Hashmi M.S.J. and El-Baradie M.A. (2005), Surface roughness prediction model by design of experiments for turning machinable glass ceramic, J Mater process Technol., Vol , pp Ramesh S., Karunamoorthy L. and Palanikumar K. (2012), Measurement and analysis of surface roughness in turning of aerospace titanium alloy (gr5), Measurement, Vol. 45, pp Gaitonde V.N., Karnik S.R., Luis Figueira and Paulo Davim J. (2009), Analysis of Machinability during Hard Turning of Cold Work Tool Steel (Type: AISI D2), Mater Manuf Process, Vol. 24, pp Palanikumar K. (2008), Application of Taguchi and response surface methodologies for surface roughness in machining glass fiber reinforced plastics by PCD tooling, Int J Adv Manuf Technol. Vol. 36, pp Tsourveloudis N.C. (2010), Predictive Modeling of the Ti6Al4V Alloy Surface Roughness, J Intell Robot Syst., Vol. 60, pp