Study the Effect of Lubricant Emulsion Percentage and Tool Material on Surface Roughness in Machining of EN-AC Alloy

Transcription

1 Study the Effect of Lubricant Emulsion Percentage and Tool Material on Surface Roughness in Machining of EN-AC Alloy E. Soltani a, H. Shahali b and H. Zarepour a a Faculty Member of Manufacturing Engineering Department, Islamic Azad University, Najafabad Branch, Isfahan, Iran. b MSc in Manufacturing Engineering, Iran University Industries and Mines, Tehran, Iran. Abstract. In this paper, the effect of machining parameters, namely, lubricant emulsion percentage and tool material on surface roughness has been studied in machining process of EN-AC aluminum alloy. EN-AC aluminum alloy is an important alloy in industries. Machining of this alloy is of vital importance due to built-up edge and tool wear. A L 9 Taguchi standard orthogonal array has been applied as experimental design to investigate the effect of the factors and their interaction. Nine machining tests have been carried out with three random replications resulting in 7 experiments. Three type of cutting tools including coated carbide (CD1810), uncoated carbide (H10), and polycrystalline diamond (CD10) have been used in this research. Emulsion percentage of lubricant is selected at three levels including 3%, 5% and 10%. Statistical analysis has been employed to study the effect of factors and their interactions using ANOVA method. Moreover, the optimal factors level has been achieved through signal to noise ratio (S/N) analysis. Also, a regression model has been provided to predict the surface roughness. Finally, the results of the confirmation tests have been presented to verify the adequacy of the predictive model. In this research, surface quality was improved by 9% using lubricant and statistical optimization method. Keywords: Surface roughness, Machining, Lubricant, Emulsion Percentage, Statistical Analysis. PACS: 81.0.wk INTRODUCTION Machining of aluminum alloys has drawn the attention of many researchers for past few years due to special problems such as build-up edge, tool wear and different behavior in comparison with other metals. Among these alloys, EN-AC aluminum alloy is extensively used in manufacture of car pistons due to distinctive properties such as high strength, low coefficient of thermal expansion and lightness [1]. Choosing the suitable machining parameters is a challenging matter in machining of this material. Adhesiveness between work piece and tool produces the build-up edge. Also silicon particles cause tool wear in machining of aluminum alloys and composites. Proper machining parameters is significant towards improvement of the tool life [,3]. Polycrystalline diamond (PCD) and chemical vapor deposition coated

2 (CVD) tools are used in the field of machining of aluminum and composite alloys according to recent researches [3-8]. These tools work well in this field due to natural and chemical properties [3,6,7]. PCD has high hardness up to 3 to 4 times that of SiC and equal to that of natural diamond [3]. Bonding of work piece to these tools doesn't produce any problem because of high chemical stability of PCD material. Instability in high temperature, difficulty in turning operation of tough materials with high tensile strength and sensitivity to machine vibrations are some limitations of PCD tools. Therefore, the conditions of experiments should be controlled completely and equipment should have sufficient rigidity [7]. The tools applied in this research are among the proper tools for machining EN-AC alloy. Despite of this fact that the machining without any lubricant has been advised environmentally, applying the proper lubricants in machining process can significantly make a required finishing surface and recent researches have confirmed it [11-13]. The effect of lubricant percentage on the machining of Al composites has been investigated by Shetty [4]. Although, the percentage of the lubricant emulsion is one of the significant parameters in machining, it seems there are rarely researches in this issue. A Taguchi's standard orthogonal array has been employed as experimental design to investigate the effect of the factors and their interactions. Cost effectiveness and time saving due to reduction in number of experiments as well as increase in machining productivity, and achieving more comprehensive results are some benefits of using orthogonal arrays. This method is considered as an effective strategy for robust design and optimum experimental design in various research works. Signal to noise ratio(s/n) is used to optimize the machining parameters. This method converts the mean square deviation to logarithm. Consequently, results behave linearly and prediction of optimal function is closer to actual value [13,15]. In recent research works, effective factors on surface roughness such as lubricant emulsion percentage and tool type have been studied and appropriate models have been presented to predict the response and optimal conditions using S/N ratio analysis [,1]. However, there are some other factors affecting the surface roughness in machining of the aluminum alloys and composites which have not been investigated quantitatively. In this research, surface roughness has been investigated in machining of EN-AC aluminum alloy through statistical analysis and corresponding parameters have been optimized. TOOLS, MATERIALS AND EQUIPMENT Nine work pieces were prepared using industrial piston parts manufactured by gravitational casting method from EN-AC alloy. Hardness of work pieces was measured and it ranges from 100 to 110 BHN. After facing, cutting and surface machining of all pistons, they were set up on the fixture in order to do the experiments. The diameters of pistons, all, were 85 mm after surface machining and elimination of lameness. The depth of cut was 0.6 mm. According to former researches the federate and the machine spindle speed were selected 0.07 mm/min and 1000 rev/min respectively in order to achieve the best surface quality [15]. Behran-Al was used as lubricant which is recommended for Al alloys machining. The selected

3 lubricant prevents the creation of oxide layers on the machined surface. In this research, percentage of lubricant emulsion was selected at three levels of 3%, 5% and 10%. Also, the lubricant flow rate was set at 1.6 lit/min. The machining inserts were chosen from three special types for machining Al alloys, namely, coated carbide (CD1810: DCGX 11 T3 04-Al), uncoated carbide (H10: DCGX 11T3 04-Al), and polycrystalline diamond (CD10: DCMW 11 T3 04 FP). Cutting tools as well as holders geometry was selected according to finishing operation. Sample of applied cutting tools along with machining experiments are shown in Fig. 1. (a) (b) FIGURE 1. (a): Sample of applied cutting tools; (b): cutting tools during machining experiments DESIGN OF EXPERIMENTS Appropriate design of experiment is vital for higher efficiency, accuracy in analyses and reduction in costs. Taguchi approach is one of the useful and most economical methods of design of experiment used in this study [13,14]. Table 1 shows factors (input parameters) and their levels. Two factors including emulsion percentage and tool type with three levels were considered in this study. Degree of freedom is selected equal to eight (DOF=8) according to the number of factors, their levels and interaction between factors. It necessitates a L 9 orthogonal array as economical one. This array has 4 columns. Tool type and emulsion percentage are assigned to columns 1 and respectively and interactions of factors are assigned to definite columns. The remain empty columns do not affect the orthogonally of array. Twenty seven experiments were carried out with three random replications [1]. Table 1. Factors and their levels Factors Level 1 E (Lubricant emulsion percentage, %) 3% T(tool) 1(CD1810) Level 5% 3(CD10) Level 3 10% (H10)

4 STATISTICAL ANALYSIS ON SURFACE ROUGHNESS Investigation of the Factors Effect Analysis of variance (ANOVA) is used in this research to investigate the effect of various factors on surface roughness. Normal probability plot of residuals is presented to check if the residuals are normally distributed. Accordingly, the distribution of dots around the line if figure -a implies the normality of residuals. Variance constancy and error independency are checked using the plot of residuals versus fitted values. This plot is shown in figure -b. As this figure presents both assumptions are proved to be valid. Hence, the results of this analysis will be reliable. Percent Residual Residual Fitted Value (a) (b) FIGURE. (a) Normal probability plot of residuals; (b) plot of residuals versus fitted values Table presents the results of ANOVA for machining surface roughness. In this table, DF denotes degree of freedom and sequential sum of squares and adjusted mean squares are designated by Seq SS and Adj Ms respectively [13]. Table shows that lubricant emulsion percentage and tool type have the most influence on machining surface roughness respectively. The last column of this table indicates the weight percentage of contribution which illustrates the calculable percentage of the effect of the factors on surface roughness. As it can be resulted from table, the influence of tool on the surface roughness is more than the percentage of lubricant emulsion Source T %E T %E Error Total DF Table. Analysis of variance for machining surface roughness(r a ) Seq ss Adj ss Adj Ms F P percentage of contribution % % % % % S = R-Sq = 94.% R-Sq(adj) = 91.33%

5 Correlation between Factors and Machining Surface Roughness Regression analysis is performed to obtain a correlation between factors and surface roughness. Regression analysis uses least square method to fit a model to experimental data. The second order regression equation is presented as below: R a = T E T E T E (1) In equation (1), Ra is the surface roughness (µm); T and E are experiment factors according the table1. Table 3 presents the results of confirmation tests for equation (1). In this table, R a (Exp) values are obtained from experiments. Also, R a1 is value of machining surface roughness obtained from equation (1). Furthermore, the errors value is presented for comparison and verification. Test 1 3 Table 3. The results of confirmation tests T E R a (Exp) R a1 CD10 15% CD % H10 8% Error( R a1 ) 1.4% 3.14% 7.8% Determination of Optimal Condition S/N (signal to noise) ratio analysis is used to find the optimal factor levels. The S/N ratio statistic is designated by η and is calculated from the following equation: n 1 η = 10 Log10 y i () n i= 1 Where y i is the i th response value and n is the number of replications. The rationale behind S/N ratio analysis is to find a setting of parameters in which signals are predominant. This rationale eventually leads to a situation in which the system is least sensitive to noises. Table 4 shows the η values for each factor level [8]. The higher values of η indicate the optimal level. As it can be seen in this table, for the percentage of lubricant emulsion (E), the maximum value is and hence the second level of E is an optimum value which is 5%. Furthermore, the first level of T (tool), which is PCD (CD10), is as an optimum value in order to obtain the best surface roughness. Table 4. Values of η for factor levels Level T E Delta

6 The surface roughness in optimum condition by using lubricant is (µm), while it is (µm) without using lubricant. The difference between these values is more than 9% and it can significantly indicate the optimization of machined surface quality. CONCLUSIONS Statistical analysis was carried out on surface roughness in machining of EN- AC aluminum alloy and following were concluded: 1. Lubricant emulsion percentage and tool type have consecutively maximum influence on machining surface roughness. Also, interaction between lubricant emulsion percentage and tool type has maximum influence on machining surface roughness.. The regression model with good accuracy was represented to predict the surface roughness. Also, confirmation tests were conducted to verify the adequacy of the model. 3. Signal to noise ratio (S/N) analysis was used to obtain optimal condition of factors. In this way 5% for lubricant emulsion percentage and PCD (CD10) tool type were proposed as optimal levels of factors in order to achieve the best surface roughness. 4- A comparison between surface roughness values (Ra) in machining tests (a) by using lubricant and (b) without using lubricant showed that the surface roughness in machining test by using lubricant emulsion of 5% improved the surface quality more than 9%. ACKNOWLEDGMENTS The authors would like to express their thanks to research affairs of Najafabad University. Also, a special thanks to Mr.Zamani for his close cooperation. REFERENCES 1. A. Maleki, B.Niroumand, A.shafyei: Int. J. Mater Science and Engineering Vol 09 (008), p J. P. Davim: Journal of Materials Processing Technology, Vol. 13, (00), p P. J. Heath: Journal of Materials Processing Technology, Vol. 116, (001), p Raviraj Shetty, Raghuvir Pai, Vasanth Kamath and Shrikanth S. Rao: Journal of Zhejiang University - Science A, Vol. 9, (008), p J. P. Davim, M. Baptista: Journal of Materials Processing Technology, Vol. 103, (000), p S. Durante, G. Rutelli: Journal of Surface and Coatings Technology, Vol , (1997), p S. J. Haghi in: Modern Cutting Tools, Nashre Tarrah, Vol. 1, (005). 8. R. Ranjitk in: Design of Experiment Using the Taguchi Approach, Wiley, New York, (001). 9. M. S. Phadke in: Quality Engineering using Robust Design, Prentice Hall Int. Inc, (1989). 10. J.A. Ghani, I. A. Choudhhury: Journal of Material Processing Technology, Vol. 145, (004), p R. S Pawade, S. S. Joshi: Journal of Material Processing Technology, Vol , (007), p G. Taguchi: Introduction to Quality Engineering, Asian Productivity Organization, Tokyo (1990). 13. D.C. Montgomery in: Design and analysis of experiment, John Wiley & Sons Inc, (000). 14. H. Kucuk in: Model Prediction of Cutting Forces for Aluminum Turning, Marmara University, Turkey, (001). 15. H. Shahali, H.Zarepour: Statistical analysis of machining Parameters on surface roughness in machining LM13 alloy. Annual International Conference on Mechanical Engineering (ISME), Iran, (007), P