International Conference on Advances in Design and Manufacturing (ICAD&M'14)
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Optimization of Machining Parameters for Magnesium Alloy using Taguchi Approach and RSM R. Viswanathan and S. Ramesh Abstract--- Magnesium alloy is one of the lightest materials which has wide applications in the production of aircraft engines, airframes, helicopter components, light trucks, automotive parts and computers parts for its attractive properties. In this paper, a study on the cutting properties of magnesium alloy AZ91D in dry turning with polycrystalline diamond (PCD) coated cutting tools is presented. Design of experiments has been used to study the effect of the main turning parameters such as cutting speed, feed rate and depth of cut on the surface roughness of Mg AZ91D alloy. A mathematical prediction model of the surface roughness has been developed in terms of above parameters. The effect of these parameters on the surface roughness has been investigated by using Taguchi method and Response Surface Methodology (RSM). It was found that the feed rate had greatest influence on the surface roughness. Cutting speed and depth of cut were found to have lesser influence on performance characteristics in that order. Keywords--- Mg Alloy, Surface Roughness, Turning, Optimization and Surface Roughness Methodology
M
cutting speed (m/min) feed rate (mm/rev) depth of cut (mm) Surface Roughness (µm) sum of squares degrees of freedom mean square error Fisher ratio percentage contribution I.
The turning operation normally removes stock from the material and produces rough surface. Therefore, surface roughness is another important index to evaluate the cutting performance [4]. The average surface roughness (Ra), which is mostly used in industries, has been considered in this study. The type of material machined and the type of cutting tool used also play important roles in the kind of surface characteristics produced [5]. In turning operation the surface roughness depends on cutting speed, feed rate, depth of cut, tool nose radius, lubrication of the cutting tool, machine vibrations, tool wear and on the mechanical and other properties of the material being machined. Even small changes in any of the mentioned factors may have a significant effect on the produced surface [6].
Nomenclature
v f d Ra SS DF MS F C%
chips can easily auto ignite during machining and create critical problems. If such a fire occurs, water should not be used to extinguish a magnesium fire because water is decomposed by magnesium to form hydrogen gas, which is highly explosive; only dry sand or a suitable extinguisher for fires involving metals should be utilized. For the same reasons, the use of water-based coolants can also be a risk factor. Additionally, the use of lubricants or coolants in the machining process can entail undesirable economic and environmental consequences. Therefore, machining of magnesium should be conducted under dry conditions, not only for safety reasons, but also because of economic and environmental considerations.
INTRODUCTION
AGNESIUM is one of the lightest metallic materials, and as such, it is widely used in industries, such as aeronautics, aerospace, automotive, medicine, sports, and portable devices, in applications where the density to resistance ratio must be low [1–3]. Because the melting temperature of magnesium alloys is higher than its autoignition temperature (650°C and 430°C, respectively),
R. Viswanathan, Asst. professor, Dept. Mechanical Engineering, Kongunadu College of Engineering and Technology, Thottiam, Trichy, India Email:
[email protected] S. Ramesh, Professor, Dept. Mechanical Engineering, Vel Tech High Tech Dr.Rangarajan Dr.Sakunthala Engineering College, Chennai, India.
In machinability studies investigations, 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 [7]. Design methods such as factorial designs, response surface methodology (RSM) and taguchi methods are now widely use in place of one factor at a time experimental approach which is time consuming and exorbitant in cost [8] The main objectives of this study were to identify the most important factors and interactions that influence the dry turning of magnesium and subsequently to select the optimal manufacturing conditions that produce minimum surface roughness. To achieve these objectives, the “smaller-thebetter” characteristic method was applied to the average roughness Ra.
ISBN 978-93-84743-12-3 © 2014 Bonfring
International Conference on Advances in Design and Manufacturing (ICAD&M'14)
EXPERIMENTAL PROCEDURE
A. Test Material and its Composition In this study AZ91D magnesium alloy was used as test material and the material AZ91D was provided by Exclusive Magnesium Pvt Ltd. The workpiece used in the experimental turning tests was a cylindrical bar with a diameter of 60mm and a length of 300mm with a composition shown in Table 1. Table 1 Chemical composition of experimental workpiece (wt %)
B. Test Equipments and Experimental Method The machining was conducted under different cutting conditions using a Kirloskar Turnmaster 35 lathe machine with a capacity of 3HP/2.2 kW power. The tests were carried out in dry condition using Polycrystalline diamond (PCD) coated cutting tool SNMG120404 at different cutting speeds, depth of cut and feed rate. The levels were specified for each process parameter as given in the Table 2. The parameter levels were chosen within the intervals recommended by the machine tool manufacturer. The experiments were planned using Taguchi’s orthogonal array in the design of experiments which help in reducing the number of experiments. The experiments were conducted according to a three level, L9 (3 4) orthogonal array was selected. After each test, the surface roughness was measured by surftest211, Mitutoyo surface roughness tester. Table 2: Factors and their level for Experimental Design Level Level Level Factor 1 2 3 Cutting Speed 40 60 80 (m/min) Feed (mm/rev) 0.20 0.25 0.30 Depth of cut 0.50 0.75 1.00 (mm) III.
RESULT AND DISCUSSIONS
The experimental results from Table 3 were analyzed with analysis of variance (ANOVA), which used for identifying the factors significantly affecting the performance measures. Table 3: L9 (33) Orthogonal Array with Experiment Results
A. Optimization of Parameters Taguchi recommends analyzing the means and S/N ratio using conceptual approach that involves graphical method for studying the effects and visually identifying the factors that appear to be significant. There are three quality characteristics in the analysis of S/N ratios for optimization such as LowerThe-Better (LB), Higher-The-Better (HB) and Nominal-TheBest (NB). In this work, response is minimized to obtain optimal parameter, so smaller the better type S/N ratio is used [9, 10]. The surface roughness is individually analysed using MINITAB 16 software. The mean S/N ratio for each level of the machining parameters was calculated and the results are shown in Table 4. Table 4: Response Table for Signal to Noise Ratio Detail Machining parameters S/N ratio Cutting Feed Depth of speed cut Level 1 11.437 10.709 9.966 Level 2 9.369 10.893 10.993 Level 3 9.632 8.816 9.459 Delta 2.068 2.077 1.534 Rank 2 1 3 Based on the main effect diagram (refer fig.1) optimal performance for the minimum surface roughness was obtained for cutting speed at level 1 (40 m/min), for feed at level 2 (0.25 mm/rev), and for depth of cut at level 2 (0.75 mm). Ranking of the machining parameters are also calculated based on difference in the S/N ratio and the rank indicates the dominant machining parameter that affect surface roughness (refer table 4). M a in E ffe c ts P lo t fo r S N r a tio s ( S u r fa c e R o u g h n e s s R a ) D a ta M e a n s V
f
11.4 10.8 10.2
M e a n o f S N r a t io s
II.
338
9.6 9.0 1
2
3
1
2
3
d 11.4 10.8 10.2 9.6 9.0 1
2
3
S ig n a l- to - n o is e : S m a lle r is b e tte r
Fig 1: Main Effect Plot for Surface Roughness (Ra)
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International Conference on Advances in Design and Manufacturing (ICAD&M'14)
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Table 5: Analysis of Variance for Surface Roughness Source
DF
Seq SS
Adj SS
Adj MS
F
P
C%
V
2
7.6991
7.6991
3.8496
16.68
0.057
38.97
f
2
7.9322
7.9322
3.9661
17.18
0.055
40.15
d
2
3.6647
3.6647
1.8324
7.94
0.112
18.55
Residual Error
2
0.4616
0.4616
0.2308
Total
8
19.7577
100 R-Sq = 97.7%
Table 5 shows the results of ANOVA for surface roughness for a level of significance of 5% (0.05). From ANOVA table, it is found that, cutting speed is the significant parameter on surface roughness. B. Development of Surface Response Model The analysis with Taguchi method mentioned above is an analysis only for the main factors that affect surface roughness without any consideration of correlation between factors. Therefore, the researcher has performed Response Surface Regression in the analysis of correlation between factors. Analysis with response optimizer function that finds the best value for the surface roughness at the significant level of 95% by response analysis as follows;
N o r m a l P r o b a b ility P lo t ( r e s p o n s e is R a ) 99
95 90 80 70 60 50 40 30 20 10
The mathematical model suitable for predicting suitable value is Quadratics model by considering the Full Quadratics model as shown in equation 3, which the coefficients of factors that affect response value are as shown in Table 6. Table 6: Coefficient of Factors for Surface Roughness Term Co efficient of Surface Roughness Constant 85.7494
R-Sq(adj) = 90.7%
The residuals could be said to follow a straight line in normal plot of residuals implying that the errors are distributed normally which is shown in fig. 2 and are randomly scattered within constant variance across the residuals versus predicted plot shown in fig. 3.
Pe rce nt
S = 0.4804
2.33
5
1
- 0 .1 0
- 0 .0 5
0 .0 0
0 .0 5
0 .1 0
R e s id u a l
Fig. 2: Normal Probability of the Residuals for Surface Roughness (Ra) V e r s u s F its ( r e s p o n s e is R a )
0.0405
0 .0 5 0
f
-73.7467
0 .0 2 5
d
-3.2840
v*v
-0.0001
f*f
16.6667
- 0 .0 5 0
d*d
0.6133
- 0 .0 7 5
v*f
-0.0100
v*d
-0.0013
R e s id u a l
v
0 .0 0 0
- 0 .0 2 5
0 .2 5 0
0 .2 7 5
0 .3 0 0
0 .3 2 5
0 .3 5 0
0 .3 7 5
Fit t e d V a lu e
With the above coefficients of factors that affect response value above, a mathematical model equation can be built as follows; Mathematical model for forecasting surface roughness Surface Roughness = 85.7494 + 0.0405v -73.7467 f 3.2840 d -0.0001 v * v + 16.6667 f * f + 0.6133 d * d -0.0100 v * f -0.0013 v * d (1)
Fig. 3: Residuals versus the Fitted Values for Surface Roughness (Ra) One of the most important aims of experiments related to manufacturing is to achieve the desired surface roughness of the optimal cutting parameters. To end this, the response surface optimisation is an ideal technique for determination of the best cutting parameters in turning operation. For finding suitable point of the factors, which is the best point for this experiment using Minitab Release 16, Response Optimizer function, the researcher selected Desirability Function to find suitable value of the factors. Here, the goal is to minimise
ISBN 978-93-84743-12-3 © 2014 Bonfring
International Conference on Advances in Design and Manufacturing (ICAD&M'14)
surface roughness (Ra). RSM optimisation results for surface parameters are shown in Fig. 4. Optimum cutting parameters are found to be cutting speed of 40 m/min, feed of 0.2253 mm/rev and depth of cut of 0.7222 mm. The optimised surface roughness parameter is Ra = 0.2091 µm. Optimal High D Cur 1.0000 Low
Cutting 80.0 [40.0] 40.0
Feed Rat 0.30 [0.2253] 0.20
Depth of 1.0 [0.7222] 0.50
Composite Desirability 1.0000
Surface Minimum y = 0.2091 d = 1.0000
Jeong-Du Kim and Keon-Beom Lee, “Surface Roughness Evaluation in Dry-Cutting of Magnesium Alloy by Air Pressure Coolant”, Engineering, Vol. 2, Pp. 788-792, 2010. [6] G. Boothroyd and W.A. Knight, “Fundamentals of Machining and Machine Tools”, third ed., CRC press, Taylor & Francis Group, 2006. [7] D.C. Montgomery, Design and Analysis of Experiments, fourth ed., John Wiley & sons Inc., 1997. [8] Kompan Chomsamutr and Somkiat Jongprasithporn, “Optimization Parameters of tool life Model Using the Taguchi Approach and Response Surface Methodology”, International Journal of Computer Science Issues, Vol. 9, Issue 1, No 3, Pp 120-125, 2012. [9] M. Villeta, EM. Rubio, JM. Saenz De Pipaon and MA. Sebastian, “Surface Finish Optimization of Magnesium Pieces Obtained by Dry Turning Based on Taguchi Techniques and Statistical Tests”, Materials and Manufacturing Processes, Vol. 26:12, Pp. 1503-1510, 2011. [10] S. Ramesh, L. Karunamoorthy, and K. Palanikumar, “Measurement and analysis of surface roughness in turning of aerospace Titanium alloy (Gr 5)”, Interantional Journal of Measurement, Vol. 45, No.5, Pp. 66-1276, 2012. [5]
Fig.4: The Appropriate Value of Each Factor which Effect to Surface Roughness IV.
CONCLUSION
As the parameter testing of turning work pieces as cutting speed, feed rate and depth of cut as the surface response of surface roughness by Taguchi method and Response Surface Methodology as both appropriate value of both methods as shown in Table 7 Table 7: The Comparison of Cutting Parameters by Taguchi Method and RSM Surface Roughness (Ra) µm Cutting Parameters
Taguchi method
RSM Method
Cutting Speed (m/min)
40
40
Feed (mm/rev)
0.25
0.2253
Depth of cut (mm)
0.75
0.7222
0.22
0.2091
ACKNOWLEDGMENT The author wishes to thank Centre for Micro Machining/ Nano Material, Sona College of Technology for providing help and support for the measurement of surface roughness of the work piece material for research work. REFERENCES [1]
[2] [3]
[4]
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Y.K. Yang and C.J. Tzeng, “Die-casting parameter sizing for AZ91D in notebook computer base shell”, Material and Manufacturing Processes, Vol.21(5), Pp489–494, 2006. J. De Damborenea, “New Materials in the 21st Century; Chapter 2: New metallic materials; CNIM-CSIC: Madrid, 2007 (in Spanish). X. Cao, M. Xiao,M. Jahazi, J. Fournier, and M. Alain, “Optimization of processing parameters during laser cladding of ZE41A-T5 magnesium alloy castings using Taguchi method”. Material and Manufacturing Processes, Vol.23(4), Pp. 413–418, 2008 Ashvin J. Makadia , and J.I. Nanavati, “Optimisation of machining parameters for turning operations based on response surface methodology”, Measurement, Vol. 46, Pp. 1521–1529, 2013.
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