International Journal of Engineering Trends and Technology (IJETT) Volume 48 Number 1 June 2017

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1 International Journal of Engineering Trends and Technology (IJETT) Volume 48 Number June 07 Optimization of Process Parameters for Milling Operation using Taguchi Method K.Prasadraju #, M. Satish raja *, V.Praveen #, I.Ajith kumar, #4 # Assistant professor & mechanical department & S.R.K.R Engineering. college Bhimavaram, Ap, India Abstract Quality and productivity play a major role in today's manufacturing market. Due to this surface finish & dimensional accuracy becomes very important. Milling like any metal cutting operation is used with an objective of optimizing surface roughness at micro level and economic performance at macro level. The experiments have been planned using Taguchi s experimental design technique. A L9 orthogonal array, taguchi method and analysis of variance (ANOVA) are used to formulate the experimental layout, to analyses the effect of each parameter on the machining characteristics and to predict the optimal choice for each milling parameter such as spindle, feed and depth. In cutting process, optimization ting parameters is considered to be a vital tool for improvement in output quality of a product as well as reducing the overall production time. Keywords Anova, MRR, surfaceroughness, Taguchimethod.. I.INTRODUCTION Milling is the machining process of using rotary cutters to remove material from a work piece advancing (or feeding) in a direction at an angle with the axis of the tool. It covers a wide variety of different operations and machines, on scales from small individual parts to large, heavy-duty gang milling operations. It is one of the most commonly used processes in industry and machine shops today for machining parts to precise sizes and shapes BASE: The base of the machine is Grey iron casting acculy machined on its top and bottom surface and serves as a foundation member for all the other. COLUMN: The column is the main supporting frame mounted vertically on the base. The column is box shaped. Heavily ribbed inside and houses all the driving mechanisms for the spindle and table feed.. KNEE: The knee is the rigid gray iron casting that slides up and down on the vertical way of the column face. The adjustment of height is effected by elevating screw on the base that also supports the knee. The knee houses the feed mechanism of the table, and in different controls to ope it SADDLE: The saddle is placed on the top of the knee, which slides on guide ways set exactly at 90 to column face. TABLE: The table rest on ways on the saddle and travels longitudinally. The top of the table is acculy finished and T-slots are provided for clamping the work and other fixtures on it. A lead screw under the table engages a nut on the saddle to move the table horizontally by hand or power. OVER HANGING ARM: Over hanging arm is mounted on the top of column extends beyond the column face and serve as a bearing support may be provided nearest to the cutter. More than one bearing support may be provided for the arbor. FRONT BRACE: The front brace is an extra support that is fitted between the knee and over arm to ensure further rigidity to the arbor and the knee. The front brace is slotted to allow for adjustment of the height of the knee relative to over arm. SPINDLE: The spindle of the machine is locates in the upper part of the column and receive power from the motor through belts,. ARBOR: An arbor is considered as an extension of the machine spindle on which cutters are securely mounted and rotated.. PARTS OF MILLING MACHINE: A. Base B. Column C. Knee D. Saddle E. Table F. Over Hanging Arm G. Front Brace H. I. Arbor II. INVESTIGATION OF PROCESS There are number of statistical techniques available for engineering and scientific studies. Taguchi prescribed a standard way to utilize DOE (Design of Experiments) technique to enhance the quality of products and process. DOE using Taguchi approach is a handy statistical tool to improve consistency of performance, to build insensitivity towards uncontrollable factors in optimizing manufacturing process design, solving manufacturing and production problems and in determining the best assembly method etc. It is possible to reduce the time required for experimental investigation and improve process quality by applying Taguchi technique. ISSN: Page

2 International Journal of Engineering Trends and Technology (IJETT) Volume 48 Number June 07 The L 9 Orthogonal array has been selected as one of the processes of the investigation. In this process, there are nine numbers of trials and in each one of the trials, there will be two degrees of freedom. There are three parameters and they are in three levels ( ), each of the parameters is changed in three levels. The response characteristics of the job are measured after completing the experiment. So, the process has to be conducted with L9 interactions. The response characteristic is surface roughness. The optimum process parameters identified are those which give smaller the better. B.TAGUCHI METHOD Taguchi outlined three step approach for assigning nominal values and tolerances to product and process design characteristics: System design Parameter design and Tolerance design System design is the basic prototype design to achieve desired function and parameter design is to specify levels of control factors that are relatively insensitive to noise factors. If parameter design fails to produce adequately low functional variation of product, then tolerance design is helpful. Parameter design experiments can be either physical experiments or computer based simulation trails. Experimenter has to identify list of control parameters and levels of interactions array is selected based on degrees of freedom of all factors and interactions put together C.ORTHOGONAL ARRAYS Taguchi has developed a system of tabulated designs (arrays) that allow for the maximum number of main effects to be estimated in an unbiased (orthogonal) manner, with a minimum number of runs in the experiment. The Taguchi technique involves reducing the variation in a process through robust design of experiments. The overall objective of the method is to produce high quality product at low cost to the manufacturer. Taguchi s parameter design is an important tool for robust design. It offers a single and systematic technique to optimize the design performance, quality and cost D.ANOVA TEST Tests of hypothesis (null hypothesis) deal with testing equality of at most two means. If one is interested in testing equality of several means at a time technique of analysis of variance is used. Based on F-ratios computed in ANOVA the significance of each factor and interaction can be decided. The terminology of ANOVA is largely from the statistical design of experiments. The experimenter adjusts factors and measures responses in an attempt to determine an effect. Factors are assigned to experimental units by a combination of randomization and blocking to ensure the validity of the results III EXPERIMENTALWORK AND ANALYSIS In this investigation an attempt was made to find out the optimum process parameters of MILLING on AISI 04 plates. Process parameters considered are cutting, depth and feed. A rectangular mild steel plate of size 00 mm 8mm 0mm in shaping machine for performing vertical milling. In milling, the and motion of the cutting tool is specified through several parameters. These parameters are selected for each operation based upon the work piece material, tool material, tool size, and more. A.CUTTING SPEED The of the work piece surface relative to the edge of the cutting tool during a cut, measured in surface feet per minute (SFM). The cutting (V c ) for milling is defined as the peripheral of the cutter (V c ) is given Vc = лdn m/min 000 B.FEED RATE The feed (f) in a milling machine is defined as the movement of the work piece relative to cutter axis. It is the at which the work piece is fed into the cutter. tooth (mm per tooth of the cutter) per revolution (mm per revolution of the cutter) per minute (mm per minute) per minute (table feed) Φ = feed per rev* cutter (r.p.m), Ft=feed per tooth, F (mm/min) = f t x Z x N Z = Number of teeth on the cutter periphery. C.DEPTH OF CUT In the milling process, the depth (d) is defined as the thickness of the layer of material of material removed in one pass of the work piece under the cutter. A depth from mm to 8 mm is common for roughing cuts and is less than.5 mm for finishing cuts. D.MATERIAL REMOVAL RATE Material removal (MRR) is the volume of material removed in unit time. For milling MRR is g MRR = B* d* f, mm /min Where, B= width, D= depth F= of feed E.MACHINING TIME Machining time t m is defined as the time required for one pass of width t m =length = L/ f = L/ f t *Z*N min feed ISSN: Page

3 International Journal of Engineering Trends and Technology (IJETT) Volume 48 Number June 07 S.No. Table.0: Milling Parameters and their Levels Control Factors Speed Rate of Cut Units Factors Notation Factor Levels L L L rpm N mm/min F mm dc In the present investigation three factors are considered at three levels which yield to total degrees of freedom of 6. So a L 9 array with total degrees of freedom of 8 is chosen for experimentation [6, 8, and 4]. This L9 array can accommodate the entire -level factor considered is given in Table. which shows L 9 array. Actual level values of parameter are given in Table. Table.: L 9 ( ) Orthogonal Array Trial no Milling parameter levels A B C D Table.: L 9 Orthogonal Array with Actual Values of Factor Levels Milling parameter levels Trial No A B C Mild steel plates are prepared as per the experimental plan given in above Table. For each of the plates made the responses are the surface roughness and material removal which are experimentally determined. After getting the experimental results, the results are analysed to arrive at optimum values of process parameters. Trail no Table.:Experimental Results for the Milling rpm mm/min mm Surface roughness Ra Material Removal Rate g/min IV RESULTS AND DISCUSSIONS I. OPTIMIZATION PROCESS PARAMETERS FOR SURFACE ROUGHNESS Table 4.: Parameter Levels and Response of Surface Roughness Trial Milling parameter levels and response No A B C Surface roughness ISSN: Page

4 International Journal of Engineering Trends and Technology (IJETT) Volume 48 Number June 07 Table 4.: Influence of each Process Parameter on Surface Roughness S.NO. Parameters L L L SPINDLE SPEED FEED RATE DEPTH OF CUT Fig 4.: Influence of each Process Parameter on Surface Roughness A.RESPONSE GRAPH. Response graph for surface roughness is shown in Fig 4.. Quality characteristics for surface roughness is smaller the better. At third level of spindle i.e. rpm, at third level of feed i.e.mm and at second level of depth i.e.mm However, the significant and insignificant parameter will be discriminated based on percentage contribution of each factor toward surface roughness. B.ANOVA The surface roughness values obtained is different, the experimental trial combination given in Table.7, Analysis of variance (ANOVA) is performed and results are given in Table 4.. Percentage contribution of each factor is depicted in the form of bar graph in Fig 4.. It can be observed from Fig 4. that has get major contribution towards variation in surface roughness, next best significant parameters is spindle and next best significant parameters is feed. Hence, spindle and are significant parameter which must be maintained at the levels specified i.e. at level- and at level- other parameter can be maintained at any one of the level values specified based on cost consideration Table 4.: Analysis of Variance (ANOVA) for Surface Roughness Source DOF Seqss Adjms % contribution Error Total C.OPTIMUM CONDITION After performing ANOVA it is observed that the optimum condition for smaller surface roughness is spindle at level-, depth at level- and the values of each factor is given in Table 4.4. Table 4.4: Optimum Condition for Surface Roughness S.No. Factor Name Notations % Contribution Level Description N 9.5 (500) F 9.9 (00) Dc 5.64 (0.50) D.STEPS INVOLVED TO FIND THE OPTIMUM CONDITION USING ANOVA. Total of all the results =. Correction Factor (C.F.) C.F. = Where, T = Total number of results, n = Number of experiments. Total sum of squares, S T S T = - C.F. 4. Factor sum of squares, S A 5. Degree of freedom, f t 6. Factor degree of freedom, f A 7. Variance, V 8. % Contribution, P A SAMPLE CALCULATIONS y A = =.60 y A = =.6 y A = =.6 y B = =.45 y B = =.6 y B = =. y C = =.5 y C = =. y C = =.5 = =.49 Degree of Freedom, f t Factor Degree of Freedom, f A f t = n, f t = 9, f t = 8 f A = No.of Levels, f A =, f A = E.SURFACE ROUGHNESS VALUES AT OPTIMUM CONDITION Grand Average of standard value of surface roughness =.49 Expected surface roughness at optimum condition Y Optimum = =.49+ (.6-.49) + (.-.49) + (.-.49) =.8 (STANDARD VALUE based on the significance of factors and their percentage contribution towards result) ISSN: Page 4

5 International Journal of Engineering Trends and Technology (IJETT) Volume 48 Number June 07 Instead of conducting 7 experimental trial combination by varying one factor at a time i.e. full factorial experiments, with the help of Taguchi s L 9 Orthogonal array optimized condition can be obtained by analysing the results of nine experimental trials. Interestingly the obtained optimum condition does not match with any of the experimental trial combination existing in L 9 array. It can be observed that the surface roughness value obtained at optimum condition is less than any of the surface roughness values obtained experimentally as per L 9 Orthogonal array. II OPTIMIZATION PROCESSPARAMETERS FOR MATERIAL REMOVAL RATE(MRR) Table 4.5: Parameter Levels and Response of Material Removal Rate (MRR) TRAIL NO Milling parameter levels and response MRR A B C Table 4.6: Influence of each Process Parameter on Material Removal Rate Parameters L L L SPINDLE SPEED FEED RATE DEPTH OF CUT Fig 4. Influence of each Process Parameter on Material Removal Rate A.RESPONSE GRAPH This graph indicates the influence of each process parameter on material removal. Response graph for material removal is shown in Fig 4.. Quality characteristics for material removal are the higher the better. At first level of spindle i.e. rpm, at second level of feed i.e.mm and at first level of depth i.e.mm However, the significant and insignificant parameter will be discriminated based on percentage contribution of each factor toward material removal. B.ANOVA The material removal values obtained is different, the experimental trial combination given in Table.7, Analysis of variance (ANOVA) is performed and results are given in Table 4.. Percentage contribution of each factor is depicted in the form of bar graph in Fig 4.. It can be observed from Fig 4. that feed has get major contribution towards variation in material removal, next best significant parameters is and next best significant parameters is spindle. Hence, feed and are significant parameters which are higher percentage of contribution must be maintained at the levels specified i.e., feed at level-, at level- and at level-. Table 4.7: Analysis of Variance (ANOVA) for Material Removal Rate Source DOF Seqss Adjms % contribution.89e E E Error Total 8.00E C.OPTIMUM CONDITION After performing ANOVA it is observed that the optimum condition for higher material removal spindle at level-, depth at level- and the values of each factor is given in Table 4.4. Table 4.8 Optimum Conditions for Material Removal Rate S.No Factor % Level Notations Name Contribution Description N 9.47 (000) F 48.9 (50) Dc 4.8 (0.5) ISSN: Page 5

6 International Journal of Engineering Trends and Technology (IJETT) Volume 48 Number June 07 D.MATERIAL REMOVAL RATE VALUES AT OPTIMUM CONDITION Grand Average of standard value of material removal =0.9 Expected material removal at optimum condition Y Optimum= = ( ) + ( ) + ( ) = 5.97 (STANDARD VALUE based on the significance of factors and their percentage contribution towards result) Instead of conducting 7 experimental trial combination by varying one factor at a time i.e. full factorial experiments, with the help of Taguchi s L9 Orthogonal array optimized condition can be obtained by analysing the results of nine experimental trials. Interestingly the obtained optimum condition does not match with any of the experimental trial combination existing in L 9 array. It can be observed that the material removal value obtained at optimum condition is less than any of the material removal values obtained experimentally as per L 9 Orthogonal array. V. CONCLUSIONS After conducting the experiments and analysing the experimental results the following conclusions are made Taguchi method has been successfully employed for optimizing the process parameters of Milling of mild steel plates. It has been shown that the Taguchi method provides a systematic and efficient methodology for searching the milling process parameters with optimal milling parameters. As per L9 orthogonal array, we have =7 combinations. Instead of 7 experiments, nine numbers of trials were conducted. The optimum value for surface roughness and material removal is not available in the nine numbers of experiments. The optimum values of surface roughness, combinations of parameters and their levels are also predicted by Taguchi method. By the experiment results it was found that the surface roughness quality characteristic is smaller the better but the experimental value is.00mm i.e., at parameters S, F, D and for material removal quality characteristic is bigger the better but experimental value is i.e., 0.98 at S, F, D. After applying Taguchi techniques the predicted values are.8mm and material removal is 5.97mm. The values obtaining after applying Taguchi technique is more effective than the experimental values. By ANOVA techniques, influence of each milling parameter is studied and the prediction of the surface roughness and material removal is done. Analysis of surface roughness and material removal parameters such as spindle, feed and depth against variations in milling. REFERENCES [] Milon D. Selvam, Dr.A.K.Shaik Dawood,Dr. G. Karuppusami, Optimization Of Machining Parameters For Face Milling Operation In A Vertical CNC Milling Machine Using Genetic Algorithm, An International Journal (ESTIJ), ISSN: , Vol-, August (0). [] Anil Choubey, Vedansh Chaturvedi, Jyoti Vimal, The Implementation Of Taguchi Methodology For Optimization Of End Milling Process Parameter Of Mild Steel, International Journal of Engineering Science and Technology, ISSN : , Vol-, (007) [] Nafis Ahmad, Tomohisa Tanaka and Yoshio Saito, Optimization Of Cutting Parameters For End Milling Operation By Soap Based Genetic Algorithm, ICME05- AM-08, August (006). [4] John D. Kechagias, Christos K. Ziogas, Menelaos K. Pappas, Ioannis E. Ntziatzias, Parameter Optimization during Finish End Milling of Al Alloy 508 using Robust Design, ISBN: (0). [5] R. JaliliSaffar, M.R. Razfar, A.H. Salimi and M.M. Khani, Optimization of Machining Parameters to Minimize Tool Deflection in the End Milling Operation Using Genetic Algorithm, World Appl. Sci. J., 6 (): 64-69, 009. [6] S.S.K. Deepak, Applications of Different Optimization Methods for Metal Cutting Operation A Review, Research Journal of Engineering Sciences, Vol. (), 5-58, September (0). ISSN: Page 6