IJRET: International Journal of Research in Engineering and Technology
eISSN: 2319-1163 | pISSN: 2321-7308
EXPERIMENTAL STUDY ON HARDNESS FOR SINTERED SiCP REINFORCED AMMCS USING THE RESPONSE SURFACE METHOD Sujit Das1, P.K.Bardhan2, R.Behera3, S. Patra4, G. Majumdar5, B .Oraon6, G. Sutradhar7 1
Research Scholar, Jadavpur University, Kolkata, West Bengal, India Professor, Dept. of Mechanical Engineering, JIS College of Engineering, Kalyani, West Bengal, India 3 Professor, Dept. of Mechanical Engineering, Seemanta Engineering College, Mayurbhanj, Odisha, India 4 Cwiss, IIT Kharagpur, West Bengal, India, 5 Professor, Jadavpur University, Kolkata, West Bengal, India 6 Professor, Jadavpur University, Kolkata, West Bengal, India 7 Professor, Jadavpur University, Kolkata, West Bengal, India 2
Abstract Limited efforts are being made so far to synthesize Al-SiC particle composite using powder metallurgy route and characterize the composites in terms of microstructure, mechanical properties e.g. hardness. The composite shows uniform distribution of SiC particles and good interface bonding between SiC particles and the metallic matrix. The variation of hardness with respect to the variation of process parameters viz. compacting pressure, sintering temperature and sintering time has been observed. To achieve optimum hardness of PMMCs, the percentage of reinforcement materials, compacting pressure, sintering time are considered as the controllable process parameters and related response variable is hardness. A 23 full factorial design of experiments (DOE) was used to collect experimental data to statistically analyze the effect of the process parameters on the hardness of the sintered Al-SiCp composites. It is observed that the weight percentage of SiCp, compacting pressure (Ton), sintering time (minute) strongly influence the response variables, hardness. A second order responses surface model (RSM) has been used to develop a predicting equation of hardness based on the data collected by a statistical design of experiments known as central composite design (CCD). The analysis of variance (ANOVA) shows that the observed data fits well into the assumed second order RSM model.
Keywords: Composite, Powder metallurgy, Sintered components, Hardness, Response surface method, Central composite design. --------------------------------------------------------------------***-----------------------------------------------------------------1. INTRODUCTION The Development of Technology in automotive manufacturing process has required new solutions adapted for invention of lightweight, non-pollutant materials with a low cost production. The materials which accomplish in many parts are the light weight composites, discontinuous reinforced with ceramic particles, like aluminium based composites [1]. These composites combine the characteristics of aluminium and aluminium alloys matrix (low density in comparison with ferrous materials, good corrosion resistance and machinability) with the characteristics of ceramic particles (e.g. SiCp , TiCp, B4Cp, Al2O3, SiO2, etc.) which improve in special mechanical, tribological and thermal expansion characteristics [1]- [3]. Powder metallurgy (P/M) is one of the best manufacturing process in this regards. Powder metallurgy components are increasingly utilized for soft magnetic materials, automotive and structural applications [4]. Aluminium metal matrix composites (Al-SiCp) MMCs reinforced with hard ceramic particles have emerged as a potential material especially for wear-resistant and weight critical applications such as cylinder blocks, cylinder liners, pistons, brake drums, connecting rods, and so on [5]. It is also observed that the green density and sintered density is a function of powder
type and compaction pressure [6]. Investigations also revealed that porosity and densification of P/M product highly influence the hardness and other mechanical properties [7]. Several theoretical & experimental studies reported that response variables of these components depend upon the process parameter viz, compacting pressure, sintering temperature and sintering time. Some other literature demonstrated that the hardness of a component could be modified by varying the alloy elements, particle size and so forth [8-11]. There is also some effect of sintering temperature and sintering time on the hardness of the P/M products which may be due to change of metallic phase and variation in grain size respectively. Present study examines the variation of hardness due to changes in process parameters of sintered aluminium metal matrix composites. The samples were produced by variation of process parameters as per the design of experiment and the response surface method was used to study the variation of hardness. A 23 full factorial design of experiments was used to collect experimental data for statistical analysis of the effect of process parameter on the hardness of the sintered aluminium metal matrix components. A second order responses surface model has been used to develop a predicting equation of
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hardness based on the data collected by a statistical design of experiments known as central composite design [12-14].
2. EXPERIMENTAL PROCEDURES 2.1 Production of Metal Matrix Composite In this experiment, air atomized aluminium powder (average particle size of 400 mesh) reinforced with SiC particles (average size of 400 mesh) (Figure 1) was used as the test material and. composites of have been fabricated using powder metallurgy technique (Figure 2) are shown. Al powder and SiC powder were blended on a pot mill (diameter 40 mm, height 35 mm) at a constant speed of 1500 rpm for 1.5 hour to produce a homogeneous powder
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mixture. The powder blending parameters are listed in Table 1. The homogeneous powder Al-SiCp blend was compacted in a closed cylindrical die using 120 Ton hydraulic press (Make: Lawrence & Mayo) for green stage product. The die was lubricated with Zn-stearate. The total experiment is performed according to the design of experiment (DOE). The grain refinement of metal matrix-based composites reinforced by tough particles can interprete by the increased effective extrusion ratio with increasing volume fraction of incompressible reinforcements and the available data have been analyzed by response surface method using Minitab software (version 14).
Table 1: Powder blending parameters Mixture
Filling (vol.%)
400mesh pure Al,400mesh SiC and Binder (Zinc Stearate)
50 75 100
of
mixer
Fig 1: Various steps involved in synthesis of Al- SiCp composites in P/M technique
Operation
R.P.M
Time (min)
Blending Blending Blending Rest Blending
1500 1500 1500
20 20 20 10 20
1500
Fig 2: Tubular Vacuum Furnace To avoid the oxidation of aluminium powder at high temperature, the degassing and sintering procedures of the green compacts were incorporated together in tubular vacuum furnace (diameter of hot zone 75 mm, length of hot zone 150 mm and maximum temperature 1450째C) using argon as an inert atmosphere (Fig. 2). The stepped heating procedures of the degassing and sintering have introduced into the experiment.
2.2 Microstructural Examination The P/M samples sintered at a fixed temperature of 530째C for fixed sintering time of 40 minutes under different compacting pressures, have been prepared and the microstructures (Figure 3) were examined under microscope
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IJRET: International Journal of Research in Engineering and Technology
(Olympus, CK40M). The brighter regions of the figure indicate aluminium matrix, while the darker portions
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indicate SiCp in the specimen.
Fig 3 Microstructure of Al-SiCp P/M composite specimen at different pressure (a) Compacting pressure 93.63586Ton, Sintering temperature 530°C Sintering time 40 mins; (b) Compacting pressure 80Ton, Sintering temperature 530°C, Sintering time 40 mins; (c) Compacting pressure 60Ton, Sintering temperature 530°C, Sintering time 40 mins; (d) Compacting pressure 40Ton, Sintering temperature 530°C, Sintering time 40 mins. (e) Compacting pressure 26.36414Ton,Sintering temperature 530°C and Sintering time 40 mins.
experiments are given in Tables 2 and 3. It should be mentioned in all the cases the hold values are mean value of the range of variation corresponding to each variable. Average values are preferred because of the inherent nature of the RSM model.
3. RESULTS AND DISCUSSION In this experiment Al-SiCp powder mixtures of different composition are compacted, sintered at an inert atmosphere, at a fixed temperature for different time duration. The samples are compacted under different pressure range (4093.63586 Tons). The results obtained through the
Process parameters (Independent variables)
Table 2 Symbols, levels and values of process parameters Symbols Levels Actual Coded
Coded
Actual
weight percentage of SiCp Compacting pressure(Ton)
Z1
X1
2
5
8
-1
0
+1
Z2
X2
40
60
80
-1
0
+1
Sintering time (Mins)
Z3
X3
30
40
50
-1
0
+1
Table 3 Observed Hardness values for different settings of process parameters based on 23 full factorial design Sint Std Run Pt Wt.% Comp. Blocks Time Result Order Order Type SiCp Pr. (Ton) (Mins) Hardness(VHN) 1
55
1
1
2
40
30
2.712
85.144
2
59
1
1
8
40
30
2.892
94.216
3
1
1
1
2
80
30
2.702
85.4
4
3
1
1
8
80
30
3.02
110.6
5
35
1
1
2
40
50
2.7144
85.1728
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eISSN: 2319-1163 | pISSN: 2321-7308
6
9
1
1
8
40
50
2.9304
96.0592
7
4
1
1
2
80
50
2.724
85.48
8
19
1
1
8
80
50
3.084
115.72
9
27
-1
1
-0.04538
60
40
2.70027
85.00054
10
44
-1
1
10.04538
60
40
3.132
119.56
11
13
-1
1
5
26.36414
40
2.7405
85.6075
12
2
-1
1
5
93.63586
40
2.862
94.72
13
32
-1
1
5
60
23.18207
2.76
87.4
14
52
-1
1
5
60
56.81793
2.834
90.36
15
15
0
1
5
60
40
2.808
89.31
16
54
0
1
5
60
40
2.809
89.36
17
58
0
1
5
60
40
2.807
89.32
18
29
0
1
5
60
40
2.808
89.34
19
47
0
1
5
60
40
2.808
89.35
20
21
0
1
5
60
40
2.809
89.38
21
14
1
1
2
40
30
2.713
85.134
22
38
1
1
8
40
30
2.893
94.216
23
25
1
1
2
80
30
2.71
85.44
24
10
1
1
8
80
30
3.01
110.62
25
41
1
1
2
40
50
2.7145
85.1738
26
17
1
1
8
40
50
2.9303
96.1592
27
18
1
1
2
80
50
2.724
85.58
28
45
1
1
8
80
50
3.086
115.74
29
37
-1
1
-0.04538
60
40
2.70026
85.0154
30
23
-1
1
10.04538
60
40
3.131
119.66
31
49
-1
1
5
26.36414
40
2.74054
85.6175
32
42
-1
1
5
93.63586
40
2.863
94.72
33
48
-1
1
5
60
23.18207
2.75
87.34
34
6
-1
1
5
60
56.81793
2.833
90.46
35
50
0
1
5
60
40
2.807
89.51
36
36
0
1
5
60
40
2.809
89.56
37
20
0
1
5
60
40
2.808
89.35
38
39
0
1
5
60
40
2.809
89.38
39
22
0
1
5
60
40
2.809
89.34
40
46
0
1
5
60
40
2.806
89.4
41
33
1
1
2
40
30
2.714
85.154
42
31
1
1
8
40
30
2.8921
94.216
43
12
1
1
2
80
30
2.73
85.34
44
16
1
1
8
80
30
3.04
111.6
45
40
1
1
2
40
50
2.7115
85.1728
46
26
1
1
8
40
50
2.9306
96.0592
47
5
1
1
2
80
50
2.7245
85.48
48
7
1
1
8
80
50
3.0845
116.72
49
8
-1
1
-0.04538
60
40
2.70025
85.12354
50
43
-1
1
10.04538
60
40
3.132
120.56
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eISSN: 2319-1163 | pISSN: 2321-7308
51
11
-1
1
5
26.36414
40
2.7405
85.6275
52
28
-1
1
5
93.63586
40
2.863
94.92
53
56
-1
1
5
60
23.18207
2.77
87.35
54
53
-1
1
5
60
56.81793
2.834
90.46
55
34
0
1
5
60
40
2.809
89.35
56
30
0
1
5
60
40
2.808
89.46
57
57
0
1
5
60
40
2.807
89.29
58
24
0
1
5
60
40
2.807
89.41
59
51
0
1
5
60
40
2.806
89.45
60
60
0
1
5
60
40
2.808
89.48
HCCD (Hardness) = 89.3690 + 16.5935x1 + 6.4900x2 + 1.5144x3 + 13.5038x12 +1.2193x22 - -0.0878x32 + 12.7640x1x2 +2.4209 x1x3 +1.1789x2x3
4. MATHEMATICAL MODELING From the results of ANOVA a mathematical model has been proposed for the evaluation of hardness HCCD of the powder metallurgy components. The proposed model is expressed as
Where HCCD: response, i.e., hardness in central composite design.
Surface Plot of Hardness(VHN) vs Com.Pr,, Wt.% SiCp Hold Values Sint.Time(Min) 40
140 120 H ar dness( V H N) 100 80 80
60 0
40 5 Wt.% SiC p
10
C om.P r ,
20
Fig 4: Surface Plot of hardness (R1) vs. compacting pressure (x2) and wt% of SiCp (x1) for a fixed value of sintering time (x3).
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Surface Plot of Hardness(VHN) vs Sint.Time(Min), Wt.% SiCp Hold Values Com.Pr, 60
120 H ar dness(VH N)
110 100 90
50 40 30
0 5
10
Wt.% SiC p
Sint.T ime(M in)
20
Fig 5: Surface Plot of hardness (R1) vs. sintering time (x3) and wt% of SiCp (x1) for a fixed value of compacting pressure (x2).
Surface Plot of Hardness(VHN) vs Sint.Time(Min), Com.Pr, Hold Values Wt.% SiCp 5
100
H ar dness(V H N)
95 90 50
85
40 20
40
30 60
C om.P r ,
80
Sint.T ime(M in)
20
Fig 6: Surface Plot of hardness (R1) vs. sintering time (x3) and compacting pressure (x2) for a fixed value of percentage weight of SiCp (x1).
Hardness of SiCp (x1) was observed to increase due increase in the percentage weight and compacting pressure (40- 93.63586 Tons) at a fixed sintering time of 40 minutes (Figure 4). Identical nature of variation is noted in simultaneous increase of sintering time (x3) and weight percentage of SiCp (x1) for a fixed value of compacting pressure (x2) (Figure 5). Hardness (R1) shows linear increase with sintering time (x3) and compacting pressure
(x2) for a fixed value of weight percentage of SiCp (x1) (Fig. 6). In this case, the range of variation of the parameters is similar to that of previous two cases.
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Normal Probability Plot (response is Hardness(VHN))
99.9 99 95
Percent
90 80 70 60 50 40 30 20 10 5 1 0.1
-3
-2
-1
0 Residual
1
2
3
Fig 7: Plot between observed hardness data and predicted hardness for RSM model.
This is also evident from the findings that co-efficient of determination (R-Square) value is 99.12 %. Hence, it may be concluded that the prediction made by this developed model corroborates well with the experimental observations.
5. CONCLUSIONS Hardness of Silicon carbide particle reinforced aluminium metal matrix composites (Al-SiCp) are influenced by sintering temperature, SiCp percentage weight and compacting pressure. A mathematical model has been used to predict the hardness variations by response surface method (RSM) using the experimental data. The model shows increase in density due to change in percentage weight of SiCp (x1) and sintering time for a fixed value of compacting pressure (x2). The response variable, density (R1) shows linear increase when it is plotted against sintering time (x3) and compacting pressure (x2) for a fixed value of wt% of SiCp (x1). The prediction of hardness variation from the mathematical model developed in this study matches closely with the observed data (R2 = 99.12%) which represents a highly reliable design of experiments.
ACKNOWLEDGEMENTS Authors thankfully acknowledge the financial support provided by U.G.C, New Delhi under Major Research Project Grant [F.No.–32-88/2006 (SR) dated 09.03.2007.
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