Modeling and multi-response optimization of machining performance while turning hardened steel with self-propelled rotary tool

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1 Adv. Manuf. (015) 3:84 95 DOI /s z Modeling and multi-response optimization of machining performance while turning hardened steel with self-propelled rotary tool Thella Babu Rao A. Gopala Krishna Ramesh Kumar Katta Konjeti Rama Krishna Received: 9 March 014 / Accepted: 31 October 014 / Published online: 30 November 014 Ó Shanghai University and Springer-Verlag Berlin Heidelberg 014 Abstract There are many advanced tooling approaches in metal cutting to enhance the cutting tool performance for machining hard-to-cut materials. The self propelled rotary tool (SPRT) is one of the novel approaches to improve the cutting tool performance by providing cutting edge in the form of a disk, which rotates about its principal axis and provides a rest period for the cutting edge to cool and allow engaging a fresh cutting edge with the work piece. This paper aimed to present the cutting performance of SPRT while turning hardened EN4 steel and optimize the machining conditions. Surface roughness (R a ) and metal removal rate (r MMR ) are considered as machining performance parameters to evaluate, while the horizontal inclination angle of the SPRT, depth of cut, feed rate and spindle speed are considered as process variables. Initially, design of experiments (DOEs) is employed to minimize the number of experiments. For each set of chosen process variables, the machining experiments are conducted on computer numerical control (CNC) lathe to measure the machining responses. Then, the response surface methodology (RSM) is used to establish quantitative relationships for the output responses in terms of the input variables. Analysis of variance (ANOVA) is used to check the T. B. Rao (&) K. R. Krishna Department of Mechanical Engineering, GITAM University, Hyderabad 5039, Andhra Pradesh, India baburao_thella@yahoo.co.in A. G. Krishna Department of Mechanical Engineering, University College of Engineering, JNTUK, Kakinada , Andhra Pradesh, India R. K. Katta Productionisation & Technology Transfer, Defence R&D Laboratory, Kanchanbagh, Hyderabad , Andhra Pradesh, India adequacy of the model. The influence of input variables on the output responses is also determined. Consequently, these models are formulated as a multi-response optimization problem to minimize the R a and maximize the r MMR simultaneously. Non-dominated sorting genetic algorithm- II (NSGA-II) is used to derive the set of Pareto-optimal solutions. The optimal results obtained through the proposed methodology are also compared with the results of validation experimental runs and good correlation is found between them. Keywords Self-propelled rotary turning Empirical modeling Response surface methodology (RSM) Multiobjective formulation Optimization Non-dominated sorting genetic algorithm-ii (NSGA-II) 1 Introduction During the past few years there has been tremendous development in the field of metal cutting to improve the cutting tool performance. At present, machining of hardened steel becomes more prominent as it has a wide variety of applications in making automobile, aerospace and mold die components. Typically machining processes like abrasive grinding, polishing, etc., have been used to machine these materials due to their hardness, however the present advancements in the machine tools and cutting tool materials make turning as an economical machining method instead of grinding hardened steel to produce the components with high quality machined surface [1]. Despite of having outstanding machinery during machining hardened steels, the chip formation accompanied by the heat generations at the tool chip interface has greatly affected the mechanical and physical properties of the cutting tool.

2 Modeling and multi-response optimization of machining performance 85 High temperatures tend to accelerate thermal softening of the tool and subsequent tool wear, which are not desirable because they decelerate the quality of the machined surface as well as the tool life []. In metal cutting, there are many approaches to decrease the impact of the heat generation on the cutting tool performance. The rotary tool is one of the novel approaches have been proposed to improve the cutting tool performance by minimizing the impact of heat generation. It is provided with a disk type cutting edge which rotates about its principal axis. The idea of rotating edge provides a rest period for the cutting edge to cool and allow engaging a fresh cutting edge at every time with the work piece. While machining, the insert rotation is generated by a self-propelling action induced by the chip formation [3]. Venuvinod et al. [4] reported that the use of rotary tools could reduce the cutting tool temperature to a greater extent than that of conventional stationary tools while machining mild steel. Lei et al. [5] observed significant enchancement in the tool life when adopting the rotary tools for high-speed machining of titanium alloys. Increased rotational speed slightly minimized the cutting forces but simultaneously led to the increased tool wear. Li and Kishawy [6] developed a force model for the SPRT to predict coefficient of friction at the tool chip interface, and reported that the possible rise of feed and cutting velocity could lower the friction coefficient. Corresponding to the investigation of Kishawy [7], the feed rate was found to have similar impact as the cutting speed on the rotary tool flank wear progression in hard turning for the cutting speed between 139 m/min and 446 m/min. Joshi et al. [8] proposed a toollife model describing the effect of process, tool and material dependent parameter on the magnitude of flank wear of a rotary carbide tool. Ezugwu [9] revealed that the SPRT technique achieved superior wear resistance and extraordinary improvement in tool life relative to the conventional tooling for machining difficult-to-cut nickel and titanium based super alloys. This is possible when: (i) properly applied, (ii) the reduction in relative cutting speed, (iii) the use of entire cutting edge, which lowers the cutting temperature associated with improved heat transfer as a result of the rotation of the tools during machining. However, chipping of the insert edge is found as another dominant failure mode in SPRT technique due to the thermal and mechanical shock induced by the continuous shifting of the tool edge during machining. The inclination angle of SPRT lowers the cutting force by raising rotational speed and effective rake angle. However the feed force increases with the increased feed resistance at higher inclination angles. Wang et al. [10] concerned with the prediction of cutting force for SPRT using artificial neural networks. The increasing of cutting parameters such as feed rate, depth of cut, cutting velocity and inclined angle value leads to the increasing of forces. When the operating parameters get up to a certain degree, the forces increase slowly. This phenomenon perhaps is a result of a lot of cutting heat generated by friction. The experimental investigations in the literature have been mainly concentrated in understanding various aspects related to the performance of SPRT for machining difficultto-cut materials. Most of these investigations are made to study and predict the tool wear and cutting forces, however few of them were considered the R a and made to derive the optimal machining conditions. In self-propelled rotary turning operation, the factors such as tool material, tool geometry and orientation of the tool against the work piece are some of the significant parameters on the machining performance along with the usual turning process dependent parameters such as cutting speed, feed and depth of cut. It is well known that establishing an efficient and reliable machining method to machine difficult-to-cut materials is very expensive in terms of cost and time. Therefore, prediction and optimization of a machining process become most important for machining hard-to-cut materials (like hardened steel) and making the process more effective and economical. Keeping up with the expanding demands of high-performance components made up of hardened steel, the present investigation is focused to evaluate the SPRT performance while machining hardened EN4 steel in turning process and model its performance characteristics. R a and r MMR are considered as the process performance characteristics, while the depth of cut, horizontal inclination angle of the SPRT, feed rate and spindle speed are considered as the process variables. Initially, the machining experiments are planned according to the DOEs, and the machining experiments are conducted on lathe machine to measure the machining responses for each set of chosen machining variables. The quantitative relationship between the process variables and output parameters is developed using response surface methodology (RSM). Simultaneously, analysis of variance (ANOVA) is used to check the adequacy of the developed models and the significance of the input variables on the output parameters. By using the developed empirical relationships, the problem is formulated as the multi-response optimization problem to minimize the R a and maximize the r MMR simultaneously. Non-dominated sorting genetic algorithm-ii (NSGA-II) is used to simulate and derive a set of Pareto-optimal machining performance characteristics. The obtained optimal results through the proposed methodology are compared with the validation experimental values, and found good correlation between them. Hence, the proposed methodology in the present investigation presents the total performance compliance of the machining process in improving the efficiency of the self-propelled rotary

3 86 T. B. Rao et al. Fig. 1 Flow chart of overall methodology turning process. The overall methodology of the present work is summarized and shown in Fig. 1. RSM RSM is one of the most effective statistical methods introduced by Box and Wilson [11], which is used to develop and analyze the interactive and quadratic effects between the variables. It is a well-known and widely used methodology to develop quadratic models in manufacturing and machining applications [1 17]. The experimentally measured responses in the present investigation are considered for modeling and analysis using RSM. In general, most of the experimental data fit to quadratic models and the general second-order polynomial response is described in Eq. (1). The present problem consists of four input process variables, a general quadratic model with four variables X 1, X, X 3 and X 4, and the response Y can be represented as Y ¼ b 0 þ b 1 X 1 þ b X þ b 3 X 3 þ b 4 X 4 þ b 1 X 1 X þ b 13 X 1 X 3 þ b 14 X 1 X 4 þ b 3 X X 3 þ b 4 X X 4 þ b 34 X 3 X 4 þ b 11 X 1 þ b X þ b 33X 3 þ b 44X 4 þ x; ð1þ where the single X terms are called the main effects and the squared X terms are the quadratic effects which are used to model curvature in the response surface. The cross product terms are used to model interactions between the explanatory variables. 3 NSGA-II Since two objective function are under the consideration, minimizing the R a and maximizing the r MMR are in confliction between them for the process variables, which are needed to optimize simultaneously. The simultaneous optimization of the multiple objectives results in a set of optimal solutions rather than a single optimal solution in the region of the process variables. Therefore, the problem of finding the set of multiple optimal solutions, the classical optimization techniques like min-max method, weighted sum method, goal programming method, etc., cannot be dealed effectively, and they are needed to execute many times to collect the desired Pareto-optimal set of solutions. However, this difficulty can be eliminated using the evolutionary algorithms, as they are efficient in finding multiple optimal solutions. For the present multi-response optimization problem, one of the extensively adopted multi-response optimization algorithms for a variety of machining processes such as NSGA-II developed by Kalyanmoy [18], is used to derive the set of Pareto-optimal solutions. NSGA-II is an improved technique of NSGA, which is incorporated with the properties of a fast non-dominated sorting procedure, and it sorts the population with elitist strategy, a parameter less approach to reduce computational complexity. The steps involved in implementing the NSGA-II are as follows: Step 1 Create a parent population P of size N randomly. Step Create child population Q of size N with the help of operating genetic operators such as selection, crossover, mutation in the parent population. Step 3 Combine both parent and child population to get a new population R of size N. Step 4 Sort the new population R according to the fast non-dominated sorting procedure, to get the sets of nondominated individuals called fronts. Step 5 Rank the created fronts based on their nondominant level. Step 6 Create a new population of size N from the nondominated individuals with the help of a crowded comparison operator to preserve the diversity among the non-dominated solutions. Step 7 Repeat the above procedure till the maximum generation has reached. The flow chart of NSGA-II is shown in Fig.. 4 Experimental setup Rotary turning is a complex process as it is involved with several cutting tool dependent control variables such as tool velocity, insert diameter, inclination angle in addition to the usual turning process dependent parameters. The dimensions of the workpiece and its material also influence the performance of the process. Along with the process usually dependent control factors, the inclination angle is the most important factor affecting performance of rotary

4 Modeling and multi-response optimization of machining performance 87 Table 1 Control factors and their levels Serial No. Variable Levels -1 0?1 1 Depth of cut X 1 /mm Inclination angle X /( ) Feed rate X 3 / (mmr -1 ) Spindle speed X 4 /(rmin -1 ) Fig. Flow chart of NSGA-II turning process. Different definitions of the inclination angle are currently available. Since the cutting edge is an arc, the tangents at different points are at different angles to the tool reference plane, i.e., the edge inclination along the arc varies. This angle can be more precisely defined as the angle between the the cutting edge tangent of the tool nose and the tool reference plane. The circular cutting edge has no apparent tool nose, and the cutting edge angle varies along the cutting edge. The tool nose here refers to the point where the cutting edge is zero. However, based on an extensive literature survey and trial experiments, the variables, namely, depth of cut (X 1 ), inclination angle of the rotary tool (X ), feed rate (X 3 ) and spindle speed (X 4 ) were taken into consideration as the decision (control) variables and their levels are listed in Table 1. The turning experiments were conducted on En- 4 (SAE 4340, 48HRC) hardened steel using a lathe (HMT Type-A4/UP) fitted with variable speed controls. The chemical composition of the workpiece material is w(c) = 0.43%, w(si) = 0.6%, w(mn) = 0.58%, w(cr) = 1.17%, w(ni) = 1.35%, w(mo) = 0.5%, w(p) = 0.08%, w(s) = 0.036%. Initially the DOEs was employed to minimize the number of experimental runs. The design matrix is a three level four factor central composite rotatable factorial design [19] consisting of 7 sets of experimental runs. The horizontal tilt angle (I h ) of the rotary tool was set by rotating the tool holder about the horizontal tool holder shank axis. While conducting the machining experiments dry machining was performed, R a and r MMR were treated as process responses to measure. For each experiment, MITUTOYO surface roughness tester was used to test R a with 0.8 mm cutoff value. Six measurements were taken at different locations in the direction of perpendicular to the cutting direction, and average of them was considered as the response value. The weights of work piece before and after machining were measured. Machining time taken for each cut is automatically displayed by the machine. Therefore the r MMR is calculated as the ratio of the weight loss of the workpiece after each cut to the corresponding machining time. The r MMR is calculated with the help of Eq. (). The measured responses are listed in Table. r MMR ¼ W initial W final g=min: t 5 Analysis and modeling of the process ðþ The experimentally measured responses are taken to develop the empirical models in terms of the choosen process variables such as X 1, X, X 3 and X 4. RSM was used to develop these models. The regression coefficient in the developed models was calculated with the help of a statistical analysis software called Stat-Ease Design Expert 9 [0]. Without performing any transformations on the process responses, for both the R a and r MMR, examination of the fit summary output reveals that the quadratic model is statistically significant. Therefore, it will used for further analysis. The second-order models are suggested for the present problem because of the lower predictability of the first-order model. The ANOVA was adopted to check the significance of the developed models. Tables 3 and 4 are the ANOVA tables for R a and r MMR respectively. F-values in Tables 3 and 4 imply that the postulated models for both R a and r MMR are significant, and the

5 88 T. B. Rao et al. Table Matrix of DOEs and the measured output responses Exp. No. Control factors in code value R a r MMR X 1 X X 3 X 4 /lm /(gmin -1 ) Table 3 ANOVA for R a Source Sum of squares DOEs Mean square F-value Prob [ F Model \ significant X \ X X \ X \ X 1 X 4.90E E X 1 X E E X 1 X 4 4.E E X X 3.5E-4 1.5E X 4 X 4.50E E X 3 X E E X 1 X X 3 X 4 4.8E E E E Residual E-3 Lack of fit E not significant Pure error 1.67E E-4 Cor total Standard deviation: R-squared: 0.98 Mean:.19 Adj. R-squared: 0.97 Coefficient of variation:.58 Pred. R-squared: 0.93 Predicted residual error of sum Adeq. precision: of squares (PRESS): 0.4 R a ¼ :03 þ 0:35X 1 þ 0:058X þ 0:18X 3 0:15X 4 þ 0:017X 1 X þ 0:005X 1 X 3 0:016X 1 X 4 values of Prob [ F less than 0.05 indicate that the model terms are significant for the response [19]. The lack of fit F-value implies that the lack of fit is not significant relative to the pure error, and the non-significant lack of fit is good, as we want the model to fit. The normal probability plots shown in Figs. 3 and 4 show that the residuals are located on a straight line, which means that the error is distributed normally in all the experiments. The developed models for R a and r MMR fairly fit with the observed values. The multiple regression coefficients (R ) [19] in the Tables 3 and 4 for R a and r MMR show that the fitted second order models can explain the variation in R a and r MMR up to an extent of 98.9% and 98.5% respectively. The developed second-order empirical models are reasonably adequate in representing the process. Equations (3) and (4) are the developed second order quadratic models for R a and r MMR in terms of the coded factors. þ 0:00375X X 3 0:013X X 4 þ 0:005X 3 X 4 þ 0:043X 1 þ 0:043X þ 0:073X 3 þ 0:093X 4 ; r MMR ¼ 4:13 þ 1:3X 1 0:035X þ 1:1X 3 þ 0:58X 4 þ 0:063X 1 X þ 0:38X 1 X 3 0:00937X 1 X 4 0:037X X 3 0:011X X 4 0:00687X 3 X 4 0:17X 1 þ 0:36X 0:1X 3 þ 0:5X 4 : ð3þ ð4þ Figures 5 and 6 show the main effect plots for R a and r MMR. These main effect plots can be used to assess the individual influence of input variables on R a and r MMR. From Fig. 5, it can be observed that R a tends to increase for the increased depth of cut, inclination angle and feed rate while it is decreased for increased spindle speed. From Fig. 6, the r MMR increases with the increase in depth of cut, feed rate and spindle speed. The r MMR was found with decreased trend for the increased level of inclination angle from lower level to middle level, further it showed the

6 Modeling and multi-response optimization of machining performance 89 Table 4 ANOVA for r MMR Source Sum of squares DOEs Mean square F-value Prob [ F Model \ significant X \ X X \ X \ X 1 X X 1 X X 1 X E E X X X 4 X E E X 3 X E E E X X X 3 X Residual Lack of fit not significant Pure error E-3 Cor total Standard deviation: R-squared: 0.98 Mean:.19 Adj. R-squared: 0.97 Coefficient of variation:.58 Pred. R-squared: 0.93 PRESS: 0.4 Adeq. precision: Fig. 3 Normal probability plot of the residuals for R a increasing trend. This can be explained that the increase of the cutting insert inclination angle about the work piece decreases the tool work piece contact area, which reduces the cutting forces. However, further increase in inclination angle makes the cutting edge perpendicular to the axis of the work piece and leads to the increase of the cutting Fig. 4 Normal probability plot of the residuals for r MMR forces. Thus, the R a and r MMR were decreased from -1 level to 0, as shown in Figs. 5 and 6. Figures 7 1 show the estimated response surface for R a and r MMR in relation to process variables of the depth of cut, inclination angle, feed and spindle speed. Figures 7 9 show the 3D contour plots representing the significant interactive effect of the process variables on R a. The

7 90 T. B. Rao et al. Fig. 7 Effect of depth of cut and inclination angle on R a Fig. 5 Main effect of control variables on R a Fig. 8 Effect of depth of cut and feed rate on R a Fig. 6 Main effect of control variables on r MMR increased inclination angle orient the cutting edge perpendicular to the axis of the work piece along with the depth of cut, which increases the R a due to the raised cutting forces, as shown in Fig. 7. TheR a was considerably increased with the increase in depth of cut and feed rate (see Fig. 8) while the interactive effects of spindle speed and depth of cut were also significant on R a, as shown in Fig. 9. The considered process variables such as depth of cut, inclination angle, feed and spindle speed are considerably significant for R a individually and interactively. Figures 10 1 represent the significant interactive effect plots of the process variables on r MMR. These plots presented that the r MMR was also significantly affected by interaction of the process variables, and the r MMR was observed with increasing trend for the increase of all the process variables. However, it was inverse in the case of R a. Therefore, the process response is called for Fig. 9 Effect of depth of cut and cutting velocity on R a simultaneous optimization against the process variables to maximize the r MMR and minimize the R a. 6 Formulation and optimization The objective functions in the present problem were defined to minimize the R a and maximize the r MMR simultaneously. By selecting the backward elimination

8 Modeling and multi-response optimization of machining performance 91 Table 5 Feasible bounds of control variables Serial No. Variables Lower limit Upper limit 1 Depth of cut X 1 /mm Inclination angle X /( ) Feed rate X 3 /(mmr -1 ) Spindle speed X 4 /(rmin -1 ) Fig. 10 Effect of depth of cut and feed rate on r MMR Fig. 11 Effect of depth of cut and spindle speed on r MMR Fig. 13 Initial set of solutions Fig. 1 Effect of depth of cut and inclination angle on r MMR procedure, insignificant terms were eliminated to reduce the complexities in the models while solving. The final equations, after eliminating the insignificant terms, are as follows: Fig. 14 Pareto-optimal front r MMR ¼ 4:13 þ 1:3X 1 0:035X þ 1:1X 3 þ 0:58X 4 R a ¼ :04 þ 0:35X 1 þ 0:058X þ 0:18X 3 0:15X 4 þ 0:11X 3 X 3 þ 0:13X 4 X 4 ; ð5þ þ 0:063X 1 X þ 0:38X 1 X 3 0:00937X 1 X 4 0:037X X 3 0:011X X 4 0:00687X 3 X 4 : ð6þ

9 9 T. B. Rao et al. Table 6 Optimum machining conditions Serial No. Depth of cut X 1 /mm Inclination angle X /( ) Feed rate X 3 /(mmr -1 ) Spindle speed X 4 /(rmin -1 ) R a /lm r MMR /(gmin -1 )

10 Modeling and multi-response optimization of machining performance 93 Table 6 continued Serial No. Depth of cut X 1 /mm Inclination angle X /( ) Feed rate X 3 /(mmr -1 ) Spindle speed X 4 /(rmin -1 ) R a /lm r MMR /(gmin -1 )

11 94 T. B. Rao et al. Table 6 continued Serial No. Depth of cut X 1 /mm Inclination angle X /( ) Feed rate X 3 /(mmr -1 ) Spindle speed X 4 /(rmin -1 ) R a /lm r MMR /(gmin -1 ) Table 7 Results of confirmation tests Exp. No. Process variables R a /lm r MRR /(gmin -1 ) Depth of cut X 1 /mm Inclination angle X /( ) Feed rate X 3 / (mmr -1 ) Spindle speed X 4 / (rmin -1 ) Predicted Experimental Predicted Experimental x ¼ lnðx nþ lnðx n0 Þ lnðx n1 Þ lnðx n0 Þ ; ð7þ where x is the coded value of any factor corresponding to its natural value X n ; X n1 is the natural value of the factor at? 1 level; X n0 is the natural value of the factor corresponding to the base level or zero level. From Eq. (7), the following equations can be obtained: x 1 ¼ lnðx 1Þ ln ð0:4þ lnð0:6þ lnð0:4þ ; x 3 ¼ lnðx 3Þ lnð0:96þ ln(1.36) lnð0:96þ ; x ¼ ln ðx Þ ln ð30þ ln ð50þ ln ð30þ ; x 4 ¼ lnðx 4Þ ln ð00þ lnð50þ ln (00) : The objective functions were optimized subject to the feasible bounds of the control variables. The feasible bounds for the variable are listed in Table 5. Once the optimization problem is formulated, it will be solved using an efficient evolutionary algorithm called NSGA-II. The source code of the proposed NSGA-II was implemented using VC?? and run on a Core Duo processor system. During the simulation, the chromosomes of 5 bit length for depth of cut, 10 bit length for horizontal inclination angle, 5 bit length for feed rate and 10 bit length for speed were created randomly. The crossover probability, P c = 0.9 and mutation probability, P m = 0.04 have been used for 100 initial population. Two points crossover and bitwise mutation were considered. Figure 13 shows the initial set of solutions generated randomly. The algorithm was run for 100 times to get more number of points in the optimal front and the obtained Pareto-optimal front is presented in Fig. 14. It can be observed in the Pareto-optimal graph that no solution in the front is better than the other as they are non-dominated solutions. Therefore, the choice of the solution from the derived optimal front can be made based on the production requirements. The optimal combinations of the input variables and their corresponding output responses are listed in Table 6. The obtained values in Table 6 derived by the algorithm are better than the experimentally observed values shown in Table. The experimentally measured R a and r MMR values in Table for the 3 rd experiment are.5 lm and 4.4 g/min respectively. However, the derived optimal value of r MMR was increased to g/min for the same value of R a (see No. 44 in Table 6). Similarly, the 11 th experiment from Table corresponds to the R a value of 1.65 lm and the r MMR of g/min. After optimization, the R a was reduced to 1.49 lm, for the same value of the r MMR (see No.40 in Table 6). The input process variable corresponding to the No. 86 in Table 6 and No. 9 in Table are in correlation approximately for their responses. This indicates that the values derived using the proposed optimization technique are in close agreement with the experimental values for more or less the same parameter settings. 7 Confirmation experiments In order to experimentally validate the derived combinations of the input process variables, three confirmation experiments were performed for R a and r MMR. The same experimental setup is used to conduct the validation experiments. The confirmation test results were compared with those of the proposed method, as listed in Table 7. It can be observed that the calculated error is small and found

12 Modeling and multi-response optimization of machining performance 95 with good correlation between them. Hence, the proposed methodology is proven effectively for the simultaneous optimization of multiple performance characteristics of SPRT process for machining En-4 hardened steel. 8 Conclusions The present work proposed an evolutionary method to derive the optimal machining conditions which helps to achieve high production rate and good surface quality of the machined components while machining En-4 hardened steel using SPRT in turning process. RSM was used to model the responses of R a and r MMR in terms of the chosen process variables. The problem was formulated as a multiobjective optimization problem to minimize R a and maximize r MMR simultaneously in the feasible bounds of the control variables. The NSGA-II was simulated to solve the formulated optimization problem and derive the set of Pareto-optimal solutions. Consequently, the resulted optimal solutions were confirmed with the confirmation experiments and found good correlation between them. Therefore, the proposed work enables the process engineer to select the suitable machining parameters and obtain the required R a or r MMR for this machining operation. Consequently, the process could be automated based on the chosen optimal values. This can improve the surface quality, production rate and the production cost by reducing the total machining time. References 1. Kishay HA, Wilcox J (003) Tool wear and chip formation during hard turning with self propelled rotary tools. Int J Mach Tools Manuf 43(4): Dessoly V, Melkote SN, Lescalier C (004) Modeling and verification of cutting tool temperatures in rotary turning of hardened steel. Int J Mach Tools Manuf 44: Armarego EJA, Karri V, Smith AJR (1994) Fundamental studies of driven and self-propelled rotary tool cutting processes I. Theor Investig Int J Mach Tools Manuf 34(6): Venuvinod PK, Lau WS, Narasimha RP (1981) Some investigations into machining with driven rotary tools. J Eng Ind 103: Lei ST, Liu WJ (00) High-speed machining of titanium alloys using the driven rotary tool. Int J Mach Tools Manuf 4: Li L, Kishawy HA (006) A model for cutting forces generated during machining with self-propelled rotary tools. Int J Mach Tools Manuf 46(1): Kishawy HA, Pang L, Balazinski M (011) Modeling of tool wear during hard turning with self-propelled rotary tools. Int J Mech Sci 53(11): Joshi SS, Ramakrishnan N, Nagarwalla HE, Ramakrishnan P (1999) Wear of rotary carbide tools in machining of Al/SiCp composites. Wear 30: Ezugwu EO (007) Improvements in the machining of aeroengine alloys using self-propelled rotary tooling technique. J Mater Process Technol 185: Wang SH, Zhu X, Li X, Turyagyenda G (006) Prediction of cutting force for self-propelled rotary tool using artificial neural networks. J Mater Process Technol 180: Box GEP, Wilson KB (1951) On the experimental attainment of optimum conditions (with discussion). J R Stat Soc Ser B 13(1): Kilickap E, Huseyinoglu M, Yardimeden A (011) Optimization of drilling parameters on surface roughness in drilling of AISI 1045 using response surface methodology and genetic algorithm. Int J Adv Manuf Technol 5: Palanikumar K (007) Modeling and analysis for surface roughness in machining glass fibre reinforced plastics using response surface methodology. Mater Des 8: Palanikumar K, Latha B, Senthilkumar VS, Karthikeyan R (009) Multiple performance optimization in machining of GFRP composites by a PCD tools using non dominated sorting genetic algorithm (NSGA-II). Met Mater Int 15(): Kansal HK, Singh S, Kumar P (005) Parametric optimization of powder mixed electrical discharge machining by response surface methodology. Int J Mater Process Technol 169: Sahin Y, Motorcu AR (005) Surface roughness model for machining mild steel. Mater Des 6(4): Ghafari S, Aziz HA, Isa MH, Zinatizadeh AA (009) Application of response surface methodology (RSM) to optimize coagulation flocculation treatment of leachate using poly-aluminum chloride (PAC) and alum. J Hazard Mater 163: Kalyanmoy D, Amrit P, Sameer A, Meyarivan T (00) A fast and elitist multi-objective genetic algorithm : NSGA-II. IEEE Trans Evolut Comput 6(): Montgomery DC (001) Design and analysis of experiments. Wiley, Hoboken 0. Stat-Ease Inc (001) Design-expert software, educational version for windows. Wiley, New York