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Study on Surface Roughness and Chip Formation During Milling Operation of Mild Steel Using Vegetable Based Oil as a Lubricant S. A. Adam, N.A. Shuaib, M.R.M. Hafiezal, S. N. Suhaili Abstract— The purpose of this project research is focused on the study of surface roughness and chip formation during milling operation of Mild S teel using vegetable based oil as a lubricant. Experimental set up designed by using milling machine. Taguchi Method of Orthogonal Array with factorial design of experiments used to analysis the response. S urface roughness and chip formation predicting models was developed by using experimental data and analysis of the present lubricant and vegetables based oil. In the development of predictive models, cutting parameters of cutting velocity, feed rate, and depth of cut were considered as model variables. Further, the Analysis of Variance (ANOVA) technique was used to analyze the influence of process parameters and their interaction during machining. From the analysis, it is observed that cutting speed is the most significant factor on the surface roughness followed by depth of cut and feed rate. While S unflower Oil gives the lowest surface roughness by setting of the best combination parameter. Besides, the most influenced factor that contributes to larger chip formation is depth of cut. Even the lower cutting speed and smaller feed rate had been used, the size still become larger when it comes to the increasing of depth of cut.
Index Term — Chip Formation, Lubricants, Machining, S urface Roughness, Taguchi Method of Orthogonal Array
I. INTR ODU C TIO N Surface roughness determines how a real object interacts with its environment. Rough surfaces usually wear more quickly and have high friction coefficient than smooth surfaces. Roughness is often a good predictor of the performance of mechanical components, since irregularities in the surface may form nucleation sites for cracks or corrosion [1]. Although roughness is usually undesirable, it is difficult and expensive to control in manufacturing. Decreasing the roughness of surface will usually increase its metal cutting costs exponentially. Surface quality is an important requirement for many machine parts. The purpose of the metal cutting process is not The objectives of this study include; to study the effect of using vegetables based oil as lubricant on surface roughness and chip formation during milling operation of Mild Steel and to analyze T his work was supported in the part by University Malaysia Perlis under ST G Grant No: 9001-00386. S. A. Adam, N. A. Shuaib, and M. R. M. Hafiezal are with the School of Manufacturing Engineering, University Malaysia Perlis, Pauh Putra Campus, Jalan Arau-Changlun, 02600 Arau, Perlis, Malaysia (phone: 604-9885035; fax: 604-9885034; e-mail:
[email protected]). S. N. Suhaili is with MDC Precast Industries Sdn. Bhd., Mukim Rasa, Selangor, Malaysia (e-mail:
[email protected])
the result using Design of Experiments (DOE) method; Taguchi Method (Orthogonal Array). Importance of this study is to focus on environmental effects. Due to growing environmental concerns, vegetable oils are finding their way into lubricants for industrial and transportation applications [4]. Since the application of conventional cutting fluids creates some environmental problems like environmental pollution, water pollution, and biological problems to operators, the solution need to be analyze to solve the problem. Vegetables oils indeed offer significant environmental benefits with respect to resource renewability, biodegradability, as well as providing satisfactory performance in a wide array of applications. So, this paper highlights the effect of vegetable oils as a lubricant to the surface roughness and chip formation by manipulating several parameters. II. SCOPES OF RESEARCH W ORK This study focused on the effect of vegetable oil as lubricant towards surface roughness and chip formation during milling operation. Vegetable oil such as sunflower oil and soybean oil will be applied on mild steel metal. Then, analysis using Design of Experiment (DOE) was done by using Taguchi Method (Orthogonal Array) and Analysis of Variance (ANOVA ).
III. SELECTION OF VEGETABLE OIL One potential alternative is the use of environmentally compatible or environmental friendly oils that are produced from vegetables. To achieve this classification, oil must be nontoxic and must biodegrade rapidly if spilled [2]. Vegetable based oils are triglycerides or natural esters that come from agricultural crops. These oils are natural products and therefore their chemical composition varies somewhat from one crop to another. They have some undesirable characteristics. Vegetable oils have many good natural properties including good lubricity, good resistance to shear, a high flash point, and a high viscosity index [3]. The information contained is based on background investigations, literature searches, and consultations with technical specialists. Based on this and limited field observations, vegetable based oils are an attractive. The cleanliness and non-toxic characteristics of the vegetable bas ed oils make them worth trying [5]. Sunflower Oil and Soybean Oil has been chosen as lubricants in this research according to their properties. A recent investigation performed by Alauddin et al. [7] has revealed that when the cutting speed is increased,
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International Journal of Engineering & Technology IJET-IJENS Vol:13 No:01 productivity can be maximized and surface quality can be improved. IV.
SELECTION OF ORTHOGONAL A RRAY AND FACTOR LEVELS Two levels of each factor are conducted in an L4 2³ array where the selection of the array is because of its suitability for three factors with two levels. Array selector is based on three parameters and two levels used in this experiment. So, the links to orthogonal array would be L4 array. Table I shows the L4 orthogonal array, where as Table II shows the control factors and levels for each factors.
T ABLE III I NDEP ENDENT VARIABLE WITH TWO LEVEL
Parameters Cutting Speed, A (m/min) Feed Rate, B (in/min) Depth of Cut (mm)
A 1 1 2 2
B 1 2 1 2
Experiment 1 2 3 4
A 30 30 50 50
High 50 12.0 0.2
S/N = -10 log (MSD) Where MSD =
C 1 2 2 1
T ABLE II CONTROL FACTORS AND LEVELS FOR FACTOR A, B, AND C
Factors B 7.2 12.0 7.2 12.0
Low 30 7.2 0.1
The equation to obtain the values of S/N is shown below. In this case, the S/N equation is based on the Taguchi smaller-the better loss function, as the idea is to minimize response.
T ABLE I L4 ORTHOGONAL ARRAY
Experiment 1 2 3 4
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C 0.1 0.2 0.2 0.1
V. EXPERIM ENTAL SETUP The experiments have been carried out on conventional milling machine. The experiments were performed on material of Mild Steel. The types of vegetable oils used as lubricants include sunflower oil and soybean oil. The constant parameters involved are depth of cut (d) mm, feed rate (f) mm/rev, cutting speed (Vc) m/min, and concentration of lubricants (c) (vegetable oils). The non-constant parameters would be types of vegetable oils. For the investigations of surface roughness, measurement surface roughness tester used. The surface roughness was taken on surface texture. Predicted values for constant parameters are; the lower and higher cutting speed (Vc) values are selected of 30 m/min and 50 m/min respectively. The levels of cutting speed are taken according to the value recommended by cutting speed for mild steel using face mill cutter. The range is 30 – 50 m/min. The lower and highest values of feed rate (f) are considered of 7.2 in/min and 12 in/min respectively. For the depth of cut (DOC), the higher value is 0.2 mm and the lower value is 0.1 mm. The range of cutting speed is chosen by following the standard parameter of the low carbon steel (mild steel). Face mill with 63mm of diameter has been used in this machining. Table III shows the independent variable. 2³ factorial designs used in this experiment.
MSD = Mean Square Deviation yi = observations n = No of tests in an experiment T ABLE IV SUMMARY RESULTS OF Y1, Y2, Y3 AND S/N VALUES
E
A
B
C
S/N (Y1)
S/N (Y2)
S/N (Y3)
1 2 3 4
30 30 50 50
7.2 12.0 7.2 12.0
0.1 0.2 0.2 0.1
-7.6403 -14.801 -4.2810 3.2864
-1.9728 -14.922 -10.355 -0.2144
-6.0899 -19.0248 -1.3117 -1.4672
The data are also analyzed using Analysis of Variance (ANOVA) where the relative percentage contribution of all factors is determined by comparing with the relative variance [6]. Table IV shows the response S/N ratio for the Oils. Next, the combination of this parameter optimization can be verified experimentally. This requires prediction and confirmation runs of both the optimum response and one of the other experimental combinations. Three work pieces were run at these combinations and measured using the same experimental setup. The confirmation runs were performed by using sunflower oil, soybean oil, and synthetic oil to find the minimum response. In ANOVA calculations, the degree of freedom, (f) for all factors need to be obtained first. The values of variance, (V) for all factors then calculated. F-ratio, F for all factors is calculated afterwards . Last but not least, percentage contributions, P for all factors are calculated.
VI. RESULT AND DISCUSSION. A. Surface Roughness The response of surface roughness using the best parameter setting will be shown in Table V. T ABLE V RESP ONSE OF BEST P ARAMETER SETTING
Block Label a b c
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Reading 1,Ra 0.593 0.588 1.170
Reading 2, Ra 0.472 1.246 0.995
Reading 3, Ra 0.713 0.990 1.127
Ave. Ra 0.593 0.941 1.097
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International Journal of Engineering & Technology IJET-IJENS Vol:13 No:01 So, by observing the data of the average response, Sunflower Oil gives the lowest surface roughness by setting best combination parameters. The calculated results are shown in Table VI and Table VII for Sunflower and Soybean Oil respectively. T ABLE VI ANOVA T ABLE FOR SUNFLOWER OIL
Factors
f
S
V
F
Cutting S peed , A Feed Rate, B Depth of Cut, C
1
7.7953
7.7953
-
P (%) 59.92
1
1.138486
1.138486
-
8.75
1
4.076513
4.076513
-
31.33
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From the S/N ratio response as shown in Table IX, the best combination of parameters can be identified by selecting the highest value from each factor. In this case, the most significant factor that has an effect on surface roughness is cutting speed (A) followed by depth of Cut (C), and less significant is feed rate (B). Table X shows the summary of best combinations of parameter. The result can also be observed from the graphs shown in Fig. 1-3. T ABLE X BEST SETTING COMBINATION
Factor Cutting Speed, A Feed Rate, B Depth of Cut, C
Values 50 m/min 7.2 in/min 0.1 mm
T ABLE VII ANOVA T ABLE FOR SOYBEAN OIL
Factors Cutting S peed, A Feed Rate, B Depth of Cut, C
f
S
V
F
P (%)
1
1.5738
1.5738
-
11.68
1
1.0496
1.0496
-
7.79
1
10.847
10.847
-
80.53
A good combination among the cutting speed, feed rate, and depth of cut can provide better surface quality . Table VIII shows the response of lubricant. There are sunflower oil, soybean oil, and synthetic oil. After conducting 12 cutting experiments, the surface roughness readings are used to optimize the surface roughness. The data will be use to analysis the optimum surface roughness by using Taguchi Method of Orthogonal Array. Generally, reduction of cutting speed caused the larger surface roughness. On the other hand, surface roughness increases with increase of feed rate and depth of cut. Hence, smaller values of feed rate and depth of cut must be selected in order to achieve better surface finish during steel milling operation.
Fig. 1. Depth of Cut Levels
T ABLE VIII RESP ONSE OF THE LUBRICANT
Exp A B C 1 30 7.2 0.1 2 30 12.0 0.2 3 50 7.2 0.2 4 50 12.0 0.1 Where; Y1 = Sunflower Oil Y2 = Soybean Oil Y3 = Synthetic Oil
Y1 2.410 5.496 1.637 0.685
Y2 1.255 5.573 3.294 1.025
Y3 2.016 8.938 1.163 1.184
Fig. 2. Cutting Speed Levels
T ABLE IX COMBINATION RESP ONSE OF S/N RATIO FOR Y1, Y2, AND Y3
Level 1 (Y1) 2 (Y1) 1 (Y2) 2 (Y2) 1 (Y3) 2 (Y3)
A -11.2206 -0.4973 -8.4473 -5.2845 -12.5574 -1.3895
B -5.9607 -5.7573 -6.1637 -7.5681 -3.7008 -10.2460
C -2.1770 -9.5410 -1.0936 -12.6382 -3.7786 -10.1683
Fig. 3. Feed Rate Levels
Sum of Squares for all factors is then calculated and showed in Table XI.
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International Journal of Engineering & Technology IJET-IJENS Vol:13 No:01 T able XI ANOVA T able for Synthetic Oil
Factors Cutting S peed, A Feed Rate, B Depth of Cut, C
f
S
V
F
P (%)
1
18.5242
18.5242
-
43.61
1
12.0505
12.0505
-
28.37
1
11.9051
11.9051
-
28.02
B. Chip Formation Chip size measured has been filled in the Table XII. The picture of the chip has been taken and measured by using Scanning Electron Microscope (SEM). After observing all the data, the most influenced factor that contributes to larger chip formation is depth of cut. Even the lower cutting speed and smaller feed rate had been used, the size still become larger when it comes to the increasing of depth of cut. By comparing between the different oil, it can be seen that Sunflower Oil, a, gives the smaller size of chip followed by Soybean Oil, b, and Synthetic Oil, c. T ABLE XII CHIP SIZE OF T HREE DIFFERENT LUBRICANTS
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With the orthogonal array method, a designer can subsequently select the best combination of design variables for achieving optimum or minimum Ra and corresponding machining parameters during steel milling process. The response of surface roughness of the different cutting fluid also can be minimizing. It is to ensure which of the cutting fluid or lubricant most suitable to achieve the best response on surface roughness by setting the best combination of parameter. For this research, it was founded that Sunflower Oil gave the best response on surface roughness by setting the best combination of parameters using conventional milling machine.
VIII. LIM ITATION OF RESEARCH W ORK Sunflower Oil - HOSO, or high oleic sunflower oil is currently available with an oleic acid content of >90% and a very small stearic acid content of 1.0 1.5%. A HOSO type of sunflower was first developed by Soldatov in 1976 [5]. The low stearic acid content improves its low-temperature behaviour. HOSO oil is extremely oxidation and ageing-stable compared to rapeseed oil and oleate synthesized from rapeseed oil. Both rapeseed and soybean oil of standard quality do not meet these criteria and would thus require extensive chemical modification of the polyunsaturated fatty acids , such as by adding suitable additives to improve its properties.
REFER EN C ES
VII. CONCLUSION Taguchi Method of using L4 Orthogonal Array combined with factorial design of 2³ is found to be a successful technique to perform trend analysis of surface roughness with respect to various combinations of design variables (metal cutting speed, feed rate, and depth of cut) and to optimize the response by using the different lubricant. The surface roughness increases with increase in feed rate followed by depth of cut and cu tting speed. It is observed that the predicted and measured values are close to each other. Therefore, the proposed method can be used to predict the corresponding specific surface roughness (Ra) of other steel at different parameters in milling.
[1] L. B. Abhang, M. Hameedullah, “Modeling and Analysis for Surface roughness in Machining EN-31 steel using Response Surface Methodology,” International Journal of Applied Research in Mechanical Engineering, Volume-1, Issue-1, 2011 [2] P. Leskover, J. Grum, “T he metallurgical aspect of machining.1986, Annals of CIRP 35/1, pp. 537-550 [3] G. Byrne, E. Scholta ,“Environmental clean machining processes a strategic approach”, Annals of the CIRP, Vol.42 (1), pp. 471-474, 1993 [4] F. Klocke, G. Eisenblatter, “Dry cutting”, Annals of the CIRP, 1997, Vol. 46 (2), pp.19-526 [5] www.wikipedia.com – vegetable fats and oils [6] M.F, Ghazali, Z. Shayfull, N.A. Shuaib, S.M. Nasir, M. Mat Salleh, “Injection Mould Analysis in Reducing Warpage of Nylon PA66 Side Arms using T aguchi Method and ANOVA,” International Journal of Basic & Applied Sciences IJBAS-IJENS Vol: 11 No: 01, School of Manufacturing Engineering, Universiti Malaysia Perlis, Malaysia [7] Alauddin,M . M.A. EI. Bardie and M.S.J. Hasmi, “Prediction of tool life in end milling by response surface methodology,” Journal of materials processing and technology, vol.71, 1997, pp.456 -465 [8] K. Kadirgama, M. M. Noor, M. M. Rahman, C. H. C. Haron, K. A. AbouEl-Hossein, “Surface Roughness Prediction Model of 6061-T6 Aluminium Alloy Machining Using Statistical Method,” European Journal of Scientific Research, ISSN 1450-216X Vol.25 No.2 (2009), pp.250-256 [9] E.I.Bardie,M.A.“Surface Roughness Model for Turning Grey C.I 1154 BHN,” Proceeding I Mechanical engineering, 1993, pp.43-54 [10] Chinnam, R. B. (2001). Intelligent quality controllers for on-line parameter design. In J. Wang et al (Eds.), Computational intelligence in manufacturing handbook. Boca Raton, FL: CRC Press LLC S. A. Adam received her B.Eng(Hons) in Manufacturing Engineering from International Islamic University Malaysia, Malaysia and Master degree in Innovation and Engineering Design form Universiti Putra Malaysia, Malaysia. Her research interests are in machining, optimization using design of
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experiment, robust design and product development. She is currently working as a lecturer in University Malaysia Perlis, Malaysia. N.A. Shuaib is currently working as a lecturer in Universiti Malaysia Perlis. His research scopes are in mechanical design, manufacturing process and heat transfer. He received the Bachelor of Mechanical Engineering(Hons) in 2007 from Universiti T enaga Nasional(UNIT EN), Malaysia and continued with MSc in Manufacturing Systems Engineering from Universiti Putra Malaysia, Malaysia in 2009. He is a member of a research group under School of Manufacturing Engineering in UniMAP that is actively doing research specifically in design of experiment applied to mechanical design and manufacturing process. Upon professional contribution, N.A.Shuaib involves himself with engineering professional bodies in Malaysia such as Board of Engineers, Malaysia (BEM), Institution of Engineers, Malaysia (IEM) and Malaysian Society for Engineering & T echnology (mSET ) M. R. M. Hafie z al received his Diploma of Engineering T echnology in Mechanical and Manufacturing, Degree of Mechanical Engineering and Master of Mechanical System Design Engineering from University of AixMarseille, France. T he research interests are in modeling and finite element analysis, mechanical design and green technology in mechanical. Currently he is working as a lecturer in University of Malaysia Perlis, Malaysia. S. N. Suhaili received her Diploma of Manufacturing Engineering and B.Eng in Manufacturing Engineering from University Malaysia Perlis. Her research interests are in machining and optimization. She is currently working as an Engineer in MDC Precast Industries Sdn. Bhd.
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