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 Study & Optimization of Parameters for Optimum Cutting condition during Turning Process using Response Surface Methodology Shivraj Singh 1, Harvinder Singh 1, Harry Garg 2*, 1 University College of Engineering, Punjabi University Patiala, Punjab India, 147002 2 Central Scientific Instruments Organisation(CSIR-CSIO), Chandigarh, India, 160030 Abstract In the present study effect of cutting parameters on surface finish are measured and are optimised. The experiments are performed using Al6061 as work material and is machined by using insert CNMG 120408EN- TM (H20TI). Response Surface Methodology (RSM) is used for designing the experiment. Cutting Speed, Depth of cut and Feed are the selected input parameters for turning and surface roughness is output response parameter. For the present investigation the input variables values varies from the 150-250 m/min for speed, 0.1-0.2 mm/rev for feed and 0.1-1.5 mm for depth of cut. Regression equations are generated from the RSM. Their functional relationship & effect on different parameters is studied. ANOVA is applied to know which input parameter has most significant affect on the surface roughness. It was noticed that as spindle speed increases the surface roughness decreases and with increase in feed rate roughness value increases. With increase in the depth of cut up to some extent roughness increase then it starts decreasing. Surface roughness greatly influences the functional aspect and quality. Minimum surface roughness provides better lubrication and minimum wear. Number of investigations are conducted to know the performance of the cutting tool and work material Keywords: Turning, Response Surface Methodology (RSM), ANOVA, Al6061 1 Introduction Manufacturers focuses on the surface finish and product dimensional accuracy during the manufacturing. They always try to reduce manufacturing time, wear rates (of machine and tool used), surface roughness to reduce cost of manufacturing and maintenance cost. From the number of machining operations turning is the one of the common machining process and is widely used in variety of manufacturing industries. Surface finish and quality of the surface depends upon the input parameters selected during the fabrication of the part. Generally the surface roughness depends upon the feed, cutting speed and depth of cut input parameters. The use of Aluminium and some of its alloys are now widely used and the components made up of these alloys replaces the components of the steel because it is difficult to fabricate the part from steel rather than aluminium as aluminium is soft, durable, light weight, non-magnetic and ductile in nature. Also the cost of the Steel is higher than the Aluminium. For the machining of aluminium number of inserts are available. Selection of Insert for machining of part depends upon the strength and hardness of material to be machined. In the present study CNMG 120408 EN-TM is selected for machining because of sufficient strength and low wear rate for machining aluminium alloy and also it is cost effective. Surface roughness greatly influences the functional aspect and quality. Minimum surface roughness provides better lubrication and minimum wear. Number of investigations are conducted to know the performance of the cutting tool and work material For getting the effective information a literature review has been conducted and it goes by crossing the peak level of search and as per its aspects it involves the identifications and articulations of mapping between the literature and our field of research. Although the structure of the reviewing literature can be changed with the different types of standards, but regarding the objective of research the purpose remains invariant. Zhang Z. et al.[1] have reported that CCGT 430.5,431,432 cutting tool inserts were used for machining of Al which is tempered. In order to get better surface quality input parameters are varied and their significance on the quality of surface was measured. Kirby E.D. [2] have investigated the turning operation characteristics of CCGT 432 AF cutting insert over tempered Aluminium. Influence of input parameters and other factors like noise, chuck jaws were studied also. Optimum cutting conditions were generated. Nalbant M. et al.[3] have concluded the study on AISI 300 steel bars using tin coated tools. These steel bars found wide applications in industry. Their quality of surface is highly remarkable. Concluded results shows that surface quality mainly depends upon nose radius and feed rates. Study recommends cutting conditions for machining. Hascalik A. et al.[4] have reported the effect of varying cutting parameters over the surface quality. In this study titanium alloys are machined using CNMG inserts. Surface roughness and tool life were measured. DOE helps in evaluating much impact full parameters. Manna. A. et al.[5] have reported that PVD coated tools are widely used in manufacturing sector for 699-1
Study & Optimization of Parameters for Optimum Cutting condition during Turning Process using Response Surface Methodology machining of various hard alloys. In many industries they have replace some further operations of finish by providing much surface quality only by turning. Surface quality depends upon cutting tool specification and its input process parameters. The best parameters combination was predicted using Taguchi method. Lalwani D.I. et al.[6] investigated effect of cutting parameters namely feed, cutting speed and depth of cut to minimize surface roughness in case of MDN 250 steel material using coated ceramic tool. Tzeng C. Jyh et al.[7] performed experiments using SKD11 as work material and is machined by carbide tool coated with titanium nitride. Study conclude the influence of all three input variables that are cutting speed, feed and depth of cut on the surface roughness. Isik Y. [8] made comparison between dry and wet machining conditions. Surface roughness quality, flank wear and cutting forces were measured throughout the tool life. Significance of using fluids for cooling and increasing tool life was predicted. Mohammad R.S.Y. et al.[9] investigates machining characteristics of Al6061 in CNC face milling operation has been done. The output parameters material removal rate and surface roughness were measured. The input parameters impact on response was analysed. Kuram E. et al.[10] investigated effect of cutting fluid evaluated for the thrust force and roughness on AISI 304 stainless steel. Lee H.W. et al.[11] have investigated the influence of turning parameters (feed, speed and depth of cut) on surface roughness. Response Surface Methodology and Artificial Neural network were applied to generate optimum cutting condition combination. Sastry M.N.P. et al.[12] conclude the best process conditions or parametric combination for turning of aluminium bars using HSS tool. The study evaluates the best process environment satisfying requirement of both quality and as well as productivity. Horvath R. et al.[13] studied effect of cutting parameters and surface roughness parameters were correlated to determine the relationship between them in case of aluminium fine turning. A lot of work has been done in this field but with hard materials and high speed tool inserts. Much work is done on the straight machining of parts using Al as work material. As Aluminium is a soft material it is possible to machine it with the inserts having less hardness as compared to ceramic and other hard material inserts. So the production cost also reduces by using these types of inserts. More over in present study we are with the surface quality of specified part, which have applications in the conventional glass cutting machine. 2 Methodology & Design of Experiments A better design of experiment can reduce number of the experiments. In present study Response Surface Methodology is used for designing model of the experiment by selecting three input parameters. Using RSM model is generated in the coded form. Before generating design the ranges of the input parameters over which they work should be well known and number of levels of the factors are decided. In the present study five levels of factors are selected. Table 1 Shows the Coding identifications of cutting Parameters: Parameters Speed (x1) Feed (x2) Depth of cut (x3) Min -1.68 Low -1 Mid 0 High +1 Max +1.68 150 175 200 225 250 0.1 0.125 0.15 0.175 0.2 0.1 0.35 0.8 1.15 1.5 2.1 Experimental Setup The turning operation was performed on the LMW Smart Turn CNC machine using CNMG 120408 EN- TM H20TI as cutting insert. The work materials raw material was in the form of long rod and it was cut down into specimens of size 55mm on Power Hexa machine. Parts to be fabricated in this experiment should have following specifications. Fig. 1 : Mandrel drawing Twenty number of experiments were performed and are numbered according to the designed model. The roughness of fabricated pieces were measured on the Taylor Hobson Profilometer. The roughness values are in the form of R a. 3 Results and Discussions The effect of feed, spindle speed and depth of cut and there interaction on the surface quality(roughness) has been developed using multiple regression model. The regression equations generated is as follows: R a = (1.18176 - (x(1) * 0.03364) + (x(2) * 0.18497) + (x(3) * 0.07453) - (x(1) * x(1) * 0.02838) -(x(2) * x(2) 699-2
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 * 0.02415) - (x(3) * x(3) * 0.05228) + (x(1) * x(2) * 0.07154) - (x(1) * x(3) * 0.14516) -(x(2) * x(3) * 0.07986)) (1) Following are the calculated values of the surface roughness from the profilometer: Table 2 : Experimental values of parameters. Run Order Speed m/min Feed mm/rev D.O.C mm R a µm 1 225 0.175 0.35 1.4249 2 175 0.125 1.15 1.3528 3 225 0.175 1.15 1.1486 4 175 0.175 0.35 1.0751 5 200 0.2 0.8 1.4459 6 200 0.15 0.8 1.0970 7 200 0.15 1.5 1.1031 8 225 0.125 1.15 0.8358 9 250 0.15 0.8 1.0477 10 200 0.15 0.8 1.1761 11 175 0.175 1.15 1.3880 12 200 0.15 0.8 1.2905 13 200 0.15 0.1 0.9213 14 200 0.15 0.8 1.1007 15 200 0.15 0.8 1.1647 16 225 0.125 0.35 0.7841 17 175 0.125 0.35 0.7290 18 150 0.15 0.8 1.1119 19 200 0.15 0.8 1.2690 20 200 0.1 0.8 0.7376 Figure 2(b): Surface plot for cutting speed & depth of cut RSM also generates the surface and contours plots having influence of input variables on output along with the interactions of input variables on the selected output parameter. Following are the surface and contour plots generated from values of the table given above: Figure 2(c): Surface plot for feed & depth of cut Surface plots shows that as the cutting speed and feed increases, roughness increases but if cutting speed increases and feed is decreased then roughness decreases. In the interaction of cutting speed and depth of cut, surface roughness increases with increase of depth of cut at lower cutting speed. In the interaction of feed and depth of cut, roughness value increases as the values of both parameters increases. Contour plots represents the variation in the output response by the variation of interaction between number of input parameters in different colours. Figure 2(a): Surface plot for cutting speed & feed 699-3
Study & Optimization of Parameters for Optimum Cutting condition during Turning Process using Response Surface Methodology ANOVA technique was used to know the influence of input variable on output response. Following are the main effect plots generated by ANOVA: Figure 3(a): Contour plot for cutting speed & feed Fig.4 Main Effect plot. Figure 3(b): Contour plot for cutting speed & depth of cut Figures 2,3,4 indicates that cutting speed contributes less towards the surface finish. It is observed from the experiments performed that minimum roughness is achieved from experiment no. 17 with 200m/min, 0.15mm/rev and 0.8mm values of cutting speed, feed and depth of cut respectively. In the experiments having maximum values of the feed and depth of cut has much value of the surface roughness, the values of the roughness reaches up to 1.4459µm and 1.1031 µm. The interaction among feed rate and axial depth of cut has also a significant influence on roughness. Figure 3(c): Contour plot for feed & depth of cut In the interaction of feed and speed represents that roughness increases with increase in values Similarly other results are same as given above. We can easily predict the value of R a at particular value of input parameters. As feed rate decreases from 0.15mm/rev the roughness starts decreasing and when depth of cut exceeds 0.8mm the roughness value starts decreasing. From the experimental study it is observed that cutting speed has very less impact on the surface roughness. The interaction of the cutting speed with depth of cut evaluates that high values of both these parameters improves the surface quality but from the main effect plot between R a and D.O.C., Ra and cutting speed it implies that D.O.C. has much impact on the R a rather than cutting speed. 699-4
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 3: Analysis of variance of the measuring points Analysis of variance analyse the adequacy of the model. Model is analysed at 95% level of confidence. The models P value less than 0.05 indicates significant terms of the model. If the P-value is greater than 0.100 for a term then it indicates that model term is not significant. The value of R-square Adj is 89.55% for this model which indicates that model is adequate and the relationship generated input parameters and response variable is satisfactory. 4 Conclusion An experimental study has been done for finding machined surface roughness. A mathematical equation has been generated between input parameters and response. Following conclusions are made from the present study: 1) From the above figures 2,3,4 it can be seen that there is variation in roughness with variation in input parameters. 2) As feed rate increases surface roughness parameter (R a ) increases gradually. 3) With increase in depth of cut value of roughness first increases in beginning and then it starts decreasing. 4) Cutting speed doesn't have much significance on the surface roughness value. While the results concluded from this experimental study may be generalized to considerable extent. The study is limited to the extreme range of values of input parameters specified. Acknowledgement The Authors are thankful to Director CSIR-CSIO, Chandigarh & OMEGA activity PSC/0202/1.3 & UCOE, Punjabi University Patiala for providing their kind assistance during experimentation, analysis and time to time guidance during research work. References 1) Zhang Z., Kirby E.D., Joseph C. Chen and Jacob Chen Optimizing surface finish in a turning operation using the Taguchi parameters design method, International Journal Advanced Manufacturing Technology (2006) 30: 1021-1029 2) Kirby E.D. A parameter design study in a turning operation using the Taguchi method, The Technology Interface/ Fall (2006) 3) Nalbant M., Gokkaya H. and Sur G. Application of Taguchi method in the optimization of cutting parameters for surface roughness in turning, Materials and Design (2007) 28: 1379 1385 4) Hascalik A. and Caydas U. Optimization of turning parameters for surface roughness and tool life based on the Taguchi method, International Journal Advanced Manufacturing Technology (2008) 38: 896-903 5) Manna A. and Salodkar S. Optimization of machining conditions for effective turning of E0300 alloy steel. Journals of Material Processing Technology (2008) 203: 147-153 6) Lalwani D.I., Mehta N.K. and Jain P.K. Experimental investigation of cutting parameters influence on cutting forces and surface roughness in finish hard turning of MDN250 steel, Journals of Materials Processing Technology (2008) 206: 167-169 7) Tzeng C. Jyh, Yu-HsinLinb, Yung-KuangYanga and Ming-Chang Jengc Optimization of turning operations with multiple performance characteristics using Taguchi method and Grey relation analysis, Journals of Material Processing Technology (2009) 209: 2753-2759 8) Isik Y., An experimental investigation on effect of cutting fluid in turning with coated carbides tool, Journal of Mechanical Engineering (2010) 56: 3 9) Mohammad R.S.Y. and Chavoshi S.Z. Analysis and estimation of state variables in CNC face milling of Al6061, Prod. Engg. Res. Devel (2010) 4: 535-543 10) Kuram E., Ozcelik B., Demirbas E., and E. Sık, Effects of the cutting fluid types and cutting parameters on surface roughness and thrust force, WCE (2010) 2078-0958: 978-988 11) Lee H.W. and Kwon W.T. Determination of the minute range for RSM to select the optimum cutting conditions during turning CNC Lathe, Journals of mechanical science and technology (2010) 24(8):1637-1645 12) Sastry M.N.P. and Devi K.D. "Optimization of performance Measures in CNC Turning using Design of experiment (RSM),Universal Research 699-5
Study & Optimization of Parameters for Optimum Cutting condition during Turning Process using Response Surface Methodology publications. An International Journal (2011); 1(1): 1-5 13) Horvath R. and Kiss A.D. "Analysis of Surface Roufhness parameters in the Aluminium Fine turning with diamond tool", Proceedings of the 9th International Conference, Smolenice, Slovakia (2013); 275-278 Nomenclature: Sr. Name Units no. 1 Speed (x1) m/min. 2 Feed (x2) mm/rev 3 Depth of cut (x3) mm 4 Surface roughness (Ra) µm 699-6