Advanced Materials Manufacturing & Characterization Vol. 7 Issue 2 (2017)
Advanced Materials Manufacturing & Characterization journal home page: www.ijammc-griet.com
Milling Process Parameter Optimization Using Taguchi Method Ashwini A. Rote.1, T. Y. Badgujar.2, D. R. Mahajan.3 1,2,3 Department of Mechanical Engineering, G. N. Sapkal College of Engineering Nashik, Maharashtra, Maharashtra, India Abstract— The present study focused on optimization of end milling process parameters to improve Surface finish and Dimensional deviation of Cam indexing drive casing. The effect of three machining parameters namely Spindle speed, Feed rate and Depth of Cut on Surface finish and Accuracy of dimensional deviation were investigated by using Taguchi Design approach. An orthogonal array, Signal to noise (S/N) ratio and analysis of variance (ANOVA) were employed to analyze the effect of these end milling process parameters. The analysis of results shows that Spindle speed and Feed rate are most influencing parameters on response variables than Depth of cut. Validation of results is done by using regression analysis and experimental production. Keywords — ANOVA; End milling operation; Regression analysis; Surface Roughness; Taguchi Design;
Introduction Robust design is an Engineering methodology to obtain product and process conditions, which are minimum sensitive to various causes of deviation to produce good quality product with less development and manufacturing cost. Taguchi design approach is an important tool for robust design eengineering and experimental planning. It gives simple, systematic and effective approach to optimize cost, quality and performance. Taguchi design is the design of experiment (DOE) approach, which is developed by Dr. Genichi Taguchi. In Taguchi design approach number of factors can be considered at once and the best optimal setting can be obtained with the less number of resources than traditional DOE approach. Taguchi designs consider only the balanced orthogonal array. This research is based on study of end milling parameters. Each parameter is considered at three levels. The objective of the work is to find out the optimum setting of end milling parameters in order to achieve the required surface finish and accuracy of Dimensional deviation. And further validation of work is done by using regression analysis. Literature Review Tushar Y. Badgujar and Vijay P. Wani [2016] concluded that Taguchi method and multiple regression analysis can be used for process optimization.[1,2] Zhang and Chen [2009] proposed that the effect of tool type and spindle speed on surface finish in drilling process was bigger than the effect of feed rate. [3] Vankanti and Ganta [2014] suggested that cutting speed is the most useful factor affecting the circularity of hole in drilling operation.[4] Siddiquee et al.[2014] concluded that cutting parameters like speed, feed and cutting fluid affect the surface roughness in deep drilling operation of AISI 321 austenitic steel bars.[5] Sheth
and George [2016] proposed that Spindle speed, feed rate, depth of cut and the interaction between spindle speed and feed rate are significant parameters of cylindricity and perpendicularity. Cylindricity is minimum at lower spindle speed while perpendicularity is minimum at higher spindle speed. [6] Spindle speed is the important parameter on the surface roughness after that diameter of drill bit and the feed was found to be insignificant parameter. The surface roughness increases as the spindle speed increases.[7] In end milling process use of high cutting speed, low feed rate, and low depth of cut was recommended to obtain better surface finish.[8] Rao and Pawar [2010] determined that the effective optimization of machining process parameters affects the cost and production time of machining of components as well as the quality of the final product. [9] Zhang et al. [2007] shown that the effects of spindle speed and feed rate on surface were bigger than depth of cut for milling process.[10] Feed rate and tool speed was most significant and predominant factor in producing the surface roughness. Whereas the depth of cut and tool speed were predominant factors in generating surface roughness. The tool speed seems to play a vital role in generating surface roughness and vibrations in tangential turn- milling, whereas the depth of cut has played a vital role in case of orthogonal turn-milling. [11] Kopac and krajnik [2007] proposed that minimized feed rates increase the process applicability and tool life. [12] Gholami and Azizi [2014] proposed that time and cost increases with increasing time. Multi-objective optimization can work as a powerful tool that can lead to higher optimization efficiency of machining process. [13] Koklu [2013] proposed that work piece speed is a dominant parameter on the multiple performance characteristics after depth of cut. And surface roughness and roundness error diminish with decreasing speed of work piece, depth of cut and the number of slot.[14] Alagumurthi et al. [2007] proposed that spindle speed has more effect on surface roughness. Taguchi’s method is capable systematically with small number of tests and provides a large amount of information while regression analysis is mathematical tool which can be used to relate control parameters with response variable. [15] Iii. Objective of Study The research is focusing on improving surface finish and Dimensional deviation accuracy of the cam indexing drive casing - Main body 120, in order to reduce vibration and to improve product quality.
Corresponing author Ashwini A. Rote,E-mail address:
[email protected] Doi: http://dx.doi.org/10.11127/ijammc2017.10.10 Copyright@GRIET Publications. All rightsreserved.
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Iv. Experimental Setup Milling is the versatile machining process used to remove material from the surface of work piece by a rotating multiple-tooth cutter. It is the complicated process and many factors affect the quality of the machined surface. In present study, end milling is considered. The operation is performed on Vertical machining centre (VMC-640) at different levels of Spindle speed, Feed rate and Depth of cut.
Table II The basic Taguchi orthogonal array
A. Selection of cutting parameters The standard Taguchi L9 (33) orthogonal array is used as shown in Table 1. It makes the use of three control factors each at three levels. For control factor (A-C) using the combination of levels, nine experimental runs were conducted, as shown in Table 2. For the purpose of the study, the selected parameters are discussed in Table 1 with their appropriate codes and values. The control variables are independent variables while response variables are dependent variables. B. Experimental procedure Using the selected experimental array test were conducted on end milling three axis Computer Numerical Control (CNC) vertical machining centre (VMC-640) which is manufactured by Jyoti CNC automation Pvt. Ltd. It has spindle motor power 13.5 KW with maximum spindle speed of 8000 rev/min. Based on L9 orthogonal array nine experiments for different setting of Spindle speed, Feed rate and Depth of cut were conducted. And each experiment was replicated twice. The tool used for machining the work piece was carbide end mill cutter of 12 mm diameter. Table I Machining Parameters, for orthogonal array Parameter Code Control factors Spindle A Speed,(rpm) Feed rate, B (mmpm) Depth of C cut, (mm) Response variables Surface Ra finish,(µm) Dimensional D Deviation, (mm)
Codes and Level Values used Level 1
Level 2
Level 3
Run
Control Factor A
B
C
1
1
1
1
2
1
2
2
3
1
3
3
4
2
1
2
5
2
2
3
6
2
3
1
7
3
1
3
8
3
2
1
9
3
3
2
S/N graphs were used to determine the optimal parameters. The importance and contribution of each process parameter on the response variables were found out using ANOVA method. And the relationship between the control variables and response variables were determined by using regression modelling. C. Measuring techniques Among the various surface finish parameters the parameter Ra which is most commonly used in industry, was selected in this study. The surface roughness of the work piece was measured by using the Surface roughness tester SRT-6200 and the Diameter of work piece (Main body 120) was measured by Bore gauge. D. Test specimen
3000
3200
3400
1400
1600
1800
0.3
0.5
0.7
_
_
_
_
_
_
Fig.1 Main body 120 Material used for work piece is Cast Iron. Chemical composition of the material is as shown in Table 3.
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Table III Chemical Composition of Test Specimen Chemical
%COMPOSITION
Carbon (C) Silicon (Si) Manganese (Mn)
3.2-3.5 1.8-2.0 0.6-0.9
Phosphorus (P) Sulphsur (S)
0.1-0.15 0.1
V. RESULT AND DISCUSSION A. Taguchi experimentation Experimental method used in this research is the Taguchi method. Taguchi method is a design of experimental (DOE) approach which is produced by Dr. Genichi Taguchi. The Taguchi design method only conducts the balanced experimental combination. The main advantage of Taguchi design is its efficiency in which the multiple factors can be considered at once and the optimal parameters can be identified with less number of experimental resources than traditional DOE approach. In this research work according to the degree of freedom of system L9 orthogonal array is used. Array is design by using Minitab 17 software. Fig. 2 and Fig. 3 show the S/N graphs for surface finish and Dimensional Deviation respectively.
Fig :2
B. Analysis of variance (ANOVA) With the help of ANOVA control parameters are analysed. The results are shown in Table 5. The result shows that spindle speed and Feed rate are the most effecting C. Regression analysis In order to determine relationship between control variables and response variables regression model was used. Regression model was consisting of constants and
parameters as compared to Depth of cut. Percentage effect of Spindle speed and Feed rate on response variables is 48.21% and 36.71% respectively. Coefficients of predictor. Eq. 1 represents the general regression model.
y 0 1 x1 2 x2 12 x1 x2 ......... jk x j xk (1)
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For Surface roughness and Dimensional deviation regression model was developed by using software Minitab 17. Here A, B, C is the factors which represent the spindle speed, feed
Ra 138.503 0.0660 A 0.0458B 5.6657C 1.1*105 A2 1.4*105 B 2 4.936C 2 R 2 0.9706
rate and depth of cut respectively in terms of the coded variables. Surface Roughness-
D 0.1885 3.1904*105 A 0.00021704 B 1.92404C 1.21429*107 AB 0.0003892 AC
(2)
2 Radj 0.9120
Dimensional Deviation-
0.0004142 BC R 0.9972 2
2 adj
R
(3) 0.988
Accuracy of the formed regression model is judge with the help of R-Sq which is the coefficient of determination. For both the models R-Sq (adj) value are very closes to R-Sq values, which represents that non-important terms are absent in the models. Vi. Validation Test The results of the confirmation experiments were compared in Table 6 using the optimal end milling process parameters (A3B3C1) for Surface roughness and (A2B2C2) for Dimensional
Deviation, obtained by the Taguchi method with initial end milling process parameters and also by Regression analysis method.
Vii. Conclusion This paper represents the following conclusions of an experimental research of effect of process parameters on surface roughness and Accuracy of Dimensional deviation in end milling process of cam indexing drive casing. The effect of control variables on surface roughness and accuracy of Dimension deviation was found out by using Taguchi orthogonal array. Optimum end milling conditions to minimize the output characteristics were determined. The experimental results show that surface roughness increases with increase in spindle speed. From S/N ratio and response table it is clear that surface roughness increases with increase in spindle speed. Percentage effect of spindle speed, feed rate and depth of cut on the output characteristics is 48.21%, 36.71% and 11% respectively. Optimum parameter setting for surface roughness is A3B3C1. Optimum parameter setting for accuracy of Dimensional deviation is A2B2C2. Viii. References [1] Tushar Y. Badgujar, Vijay P. Wani, “Optimization of Stamping Process Parameters for Material Thinning With Design of Experiment”, Dr B R Ambedkar National Inst. Techn. Jalandhar-144011, India Dept. Of Industrial and Production Engg. Ivth International Conference on
Production and Industrial Engg.CPIE-2016, Jalandhar144011. 2016. [2]Tushar Y. Badgujar, Vijay P. Wani, “Stamping Process Parameter Optimization with Multiple regression Analysis Approach”, ICMPC, 2017. [3] Saurin Sheth, P.M. George, “Experimental investigation, prediction and optimization of cylindricity and perpendicularity during drilling of WCB material using grey relational analysis”, Elsevier, 2016. [4] Arshad Noor Siddiquee et al., “Optimization of Deep Drilling Process Parameters of AISI 321 Steel using Taguchi Method”, Elsevier, 2014. [5]T. Rajmohan, K. Palanikumar, M. Kathirvel, “Optimization of machining parameters in drilling hybrid aluminum metal matrix composites”, Elsevier, 2012. [6]Vinodkumar Vankanti and Venkateswarlu Ganta, “Optimization of process parameters in drilling of GFRP Composite using Taguchi method”, 2014. [7] J. Prasanna, et al., “Optimization of process parameters of small hole dry drilling in Ti–6Al–4V using Taguchi and grey relational analysis”, Elsevier,2014. [8] A. K. Paridaa, B. C. Routara, R. K. Bhuyan, “Surface roughness model and parametric optimization in machining of GFRP composite: Taguchi and Response surface methodology Approach”, Elsevier, 2015.
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[9] Julie Z. Zhang and Joseph C. Chen, “Surface Roughness optimization in a Drilling Operation Using the Taguchi Design Method”, Taylor and Francis, 2009. [10] Mohammad Hadi Gholami & Mahmood Reza Azizi, “Constrained grinding optimization for time, cost, and surface roughness using NSGA-II”, Springer, 2014. [11] P. J. Pawar, R. V. Rao, and J. P. Davim, “Multi objective optimization of Grinding Process Parameters Using Particle Swarm Optimization Algorithm”, Taylor and Francis, 2010. [12 U_gurKoklu, “Optimisation of machining parameters in interrupted cylindrical grinding using the Grey-based Taguchi method”, Taylor and Francis, 2013. [13] N. Alagumurthi, K. Palaniradja, and V. Soundararajan, “Optimization of Grinding Process Through Design of Experiment (DOE)—A Comparative Study”, Taylor and Francis, 2006. [14] Hamid Baseri, “Simulated annealing based optimization of dressing conditions for increasing the grinding performance”, Springer, 2012.
[15] J.A. Ghani, I.A. Choudhury and H.H. Hassan, “Application of Taguchi method in the optimization of end milling parameters”, Elsevier, 2004. [16] R. Venkata Rao and P.J. Pawar, “Parameter optimization of a multi-pass milling process using non-traditional optimization algorithms”, Elsevier, 2010. [17] Tung-Hsu Hou , Chi-Hung Su and Wang-Lin Liu, “Parameters optimization of a Nano-particle wet milling process using the Taguchi method, response surface method and genetic algorithm”, Elsevier, 2007. [18] Ch. Ratnam et al. “Process monitoring and effects of process parameters on responses in turn-milling operations based on SN ratio and ANOVA”, Elsevier, 2016. [19] J. Kopac and P. Krajnik, “Robust design of flank milling parameters based on grey-Taguchi method”, Elsevier, 2007. [20] Julie Z. Zhang et al., “Surface roughness optimization in an end-milling operation using the Taguchi design method”, Elsevier,2007.
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