2018 International Conference on Advances in Sustainable Engineering and Applications (ICASEA), Wasit University, Kut, Iraq.
Influence of Mechanical Mold Vibration on EDM Parameters of Aluminum-Alumina Composite Rand A. Abdulwahid
Haydar Al-Ethari
Saad H. Al-Shaafaie
Materials Engineering College University of Babylon Babylon, Iraq
[email protected]
Materials Engineering College University of Babylon Babylon, Iraq
[email protected]
Materials Engineering College University of Babylon Babylon, Iraq
[email protected]
are the critical output parameters of EDM. For effective machining, MRR should be maximized while EWR and Ra should be minimized, this causes the need for optimizing the input parameters [4].
Abstract— Application of mechanical vibration during solidification of melt is one of the techniques of enhancement the properties of composite. Mechanical mold vibration was applied in amplitude and frequency range of (0-1mm) and (8-25Hz) respectively to pure aluminum reinforced with 5wt% Al2O3 particles. The composite was prepared by two-step stir casting method. Electro-discharge machining (EDM) was used in this study. Most of mechanical and physical properties were significantly enhanced with vibration. Brinell hardness was increased by (38%) while porosity and grain size were reduced by (83%) and (43%) respectively. Results indicate that by using vibration material removal rate and electrode wear rate were increased by 18% and 20% respectively, while surface roughness was decreased by 23%. Response surface methodology approach was used to determine an optimum combination of machining parameters to get maximum material removal rate with minimum electrode wear rate and minimum surface roughness.
The investigations on the machining aspects of MMCs with particulate reinforcement have been carried out and reported. Mouangue Nanimina, A. et al. [5] studied the effects of EDM on Al6061- 30% Al2O3 metal matrix composites. They selected Ip, Ton and Toff as machining parameters and MRR and TWR as responses. They found that the high current and pulse on time increase the material removal rate. More tool wears are observed at low peak current and pulse on time. Rajesh Kumar Bhuyan et al.[3] investigated the effect of process parameters such as Ton, Ip and Fpon (MRR), (TWR) and (Ra) during machining of Al12% SiC MMCs. Response surface methodology (RSM) is used to develop the mathematical model and to correlate the process parameters with the response.
Keywords— Composite material; mechanical mold vibration; EDM; response surface methodology (RSM).
The present study focused on studying the effect of mechanical mold vibration on the physical, mechanical and machining properties of Al-5wt% Al2O3 composite prepared by two-step stir casting method. Accordingly, quantitative mathematical models have been designed to study influence of (Ip),(Ton) and (Toff) on (MRR), (EWR) and (Ra) by using response surface methodology (RSM).
I. INTRODUCTION Conventional materials have limitations in achieving good combination of strength, stiffness, toughness and density etc. To overcome these limitations and to meet the ever increasing demand of modern day technology, composites are most promising materials of recent days [1]. The Al2O3 reinforced aluminum base composites have been progressively more used in the automotive, aircraft and aerospace industry because of their high strength to weight ratio, good castability and better tribological properties. Applying mechanical vibration into a molten alloy during solidification has been developed as an advantageous method to obtain fine grains, globular structures, increase fluidity, reduce hot cracking and minimize the porosity and segregation problems of conventional casting [2].
II. EXPERIMENTAL DETAILS A. Materials Used Chemical composition of pure aluminum wire used to prepare the Al base composite is shown in Table I. TABLE I. Si % 0.036
The reinforcements in the metal matrix composite (MMCs) make the material difficult to machine. So there is a need for a non-conventional type of machining that produces a good surface finish with the required dimensional accuracy. Electric Discharge Machining(EDM) being a non-contact type process, it can produce products with good dimensional accuracy, complexity, and a good surface finish. The performance of EDM is affected by several input parameters like pulse current (Ip), pulse on time (Ton) and pulse off time (Toff) [3]. Material removal rate (MRR), tool wear rate (TWR) and surface roughness (Ra)
Fe % 0.219
CHEMICAL COMPOSITION OF ALUMINUM WIRE. Mn % 0.003
Cr % 0.003
Ni% 0.001
V% 0.007
Ti % 0.002
Al % Bal
Magnesium was added during the preparation of the composite in order to improve the wettability of alumina particles by reducing its surface tension. Aluminum oxide with an average particle size of (11.80 µm) was used as reinforcement. B. Fabrication of Al-Al2O3 Composite Samples with 1.5 wt% Mg and 5wt% Al2O3 were prepared. Two-step stir casting method was used via electric furnace and
978-1-5386-3540-7/18/31.00$©2018 IEEE
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2018 International Conference on Advances in Sustainable Engineering and Applications (ICASEA), Wasit University, Kut, Iraq. four-blade steel stirrer under a shield of argon gas. The melt was poured in preheated steel mold with a cavity of 20 mm in diameter x 150 mm in height.
calculated. The average linear intercept calculated through (2) as recorded in [8].
C. Mechanical Vibration Set up The mechanical vibrations were provided using a vibrating device built specially for the present work. Fig. 1 shows a sketch for this device. The device, provides four different amplitudes of vibration due to shaft with an eccentricity of values (0.25, 0.5,0.75, and 1 mm). The shaft can rotate with three rotating speed (500, 1000, and 1500 rpm ) to get the required frequency. Due to the eccentricity, a table where the die is attached to the device will vibrate vertically with an amplitude equal to the designed eccentricity.
Ɩ = L/NM
(2)
Where: L = line length (μm); M = magnification; N = intersection number. E. Machining Test All experiments were performed using a die sinking EDM machine (CHMER model CM 323+50N). The machining operation was used to make a blind hole in the flat surface of workpiece. Tool electrode (positive polarity) of electrolytic pure copper with diameter of 10 mm and 30 mm height for each experiment. Specimens of 20mm diameter and 10mm thickness were used in the machining operation. Commercial grade EDM oil is used as dielectric fluid. The machining time is10 min recorded in the timer of EDM machine. A gap of (1mm) between the cutting tool and the workpiece at constant voltage of (240 V) was used during all experimental runs. The specimens were machined according to a program designed via response surface methodology to get the optimum machining conditions. A regression model was developed for predicting response factors. Minitab (Version 17) software was used in this work. The machining variables and their levels are given in Table II.
Fig. 1. Sketch of vibrating device.
TABLE II.
D. Mechanical and physical Tests Brinell hardness test was carried out according to ASTM (E10-15a) [6] with a ball indenter diameter of (2.5mm), and load of (31.25g) for (10 second). The hardness was measured at top ,center and bottom part of each sample cast with vibration. An average of three hardness measurements was recoded for each specimen.
MACHINING VARIABLES AND THEIR LEVELS.
Parameter
Designation
Unit
Pulse current Pulse on-Time Pulse offTime
Ip (Ton) (Toff)
Amp µsec µsec
Coded/Actual levels -1 0 1 4 8 12 100 150 200 25 50 75
Material removal rate (mm3/min) is defined as ratio of the differences in weight of the work piece before and after machining to density of work piece and machining time. Mathematically it is expressed as (3) as recoded in [9]. MRR = (Wiw – Wfw) / ρw t (3)
Porosity test of the final composite samples has been determined at top, center and bottom part of each sample according to (1) as recorded in [7].
Where: Wiw= initial weight of work piece (gm); Wfw = final weight of work piece (gm); ρw= density of work piece (gm/mm³); t = machining time (min). The calculation is made on the basis that the density of the work piece is (2.62 gm /cm³) which measured experimentally by the Archimedes principle as (4) as mentioned in [10].
Porosity (apparent) % = (Ww-Wd / Wsat-Ws) *100 (1) Where : Wd = dry weight of the samples; WW = wet weight of the sample (the sample was weighted after immersing it for 24 hours in distilled water); Wsat = saturated weight ( the sample was weighted after immersing it for 5hrs. in pure water at 80ºC); Ws = suspended weight (weighting the suspended sample in distilled water). To measure the grain size, specimens with (20mm) in diameter and (10 mm) in height, were cut from samples solidified with and without vibration. The samples were prepared for testing in terms of grinding, polishing, and etching operations. An optical microscope with magnification of 200X was used to capture the microstructure. The measuring process was carried by linear intercept technique. Through this technique, draw lines on a photomicrograph, and the grain boundary intercepts number, “N” extension this line is
ρ = [weight in air / (weight in air – weight n water)] * ρwater (4) ρwater = density of water (g/cm3). Electrode wear rate (mm3/min) is defined as ratio of the differences in weight of the electrode before and after machining to density of electrode and machining time. Mathematically it is expressed as (5) as recoded in [11]. EWR = (Wie – Wfe ) / ρe t
222
(5)
2018 International Conference on Advances in Sustainable Engineering and Applications (ICASEA), Wasit University, Kut, Iraq. Where: Wie= initial weight of electrode (gm); Wfe = final weight of electrode (gm); ρe= density of electrode (gm/mm³), t = machining time (min). The calculation is made on the basis that the density of electrode (8.96 gm /cm³) which calculated in the same way as for the MRR. Surface roughness (Ra in µm) was measured using surface roughness tester type (TR210). The average of five measurements was recorded in each test. III. RESULTS AND DISCUSSION A. Mechanical and Physical Test Results The Brinell’s hardness number of the samples prepared without the effect of mold vibration was 53 kg/mm2. These results are in agreement with reference [12]. Table III TABLE III.
Fig. 2. Al-Al2O3 (A): solidified without vibration; (B): solidified with vibration (amplitude of 0.5mm and frequency of 25 S-1).
B. Machining Tests Results
RESULTS OF HARDNESS AND POROSITY TESTS. Location of sample
Amplitude (mm)
Frequency (S-1)
0
0 8 17 25 8 17 25 8 17 25 8 17 25
0.25
0.5
0.75
1
Top BHN (kg/mm2) 53 54 55 55 63 66 72 59 63 65 58 59 63
Porosity (%) 4 3 2.9 2.4 0.92 1 0.9 1.9 1.5 1.2 2 2.3 1.3
Center BHN Porosity (kg/mm2) (%) 53 4 55 2.9 56 2.8 58 2.3 64 0.98 68 0.9 73 0.8 60 1.8 64 1.3 66 1 59 1.9 62 2 65 1
demonstrate the results of the hardness and porosity test for the samples solidified under the effect of mechanical vibration. The results show that the bottom, the center, and the top part record nearly the same value of the hardness number for all samples. The best value of hardness and porosity were recorded for the sample prepared under a vibration of (25S-1) frequency and an amplitude of (0.5mm). So this condition was selected for preparing all of the samples used in the machining test. Fig. 2 shows the microstructure of grain size measurement for sample solidified with and without vibration. The result indicated that the average grain size of sample solidified without vibration was (56µm), while by applying mechanical vibration this average reduced to (32µm). It is clearly that the use of mechanical vibration lead to grain refinement due to high cooling rate which causes the grain refinement as a change in size and distribution of particles of composite material.
Bottom BHN Porosity (kg/mm2) (%) 53 4 56 2.8 58 2.5 62 2 65 1 70 0.7 74 0.4 63 1.2 66 1 68 0.9 62 1.8 64 1.3 67 0.92
Average value BHN (kg/mm2) 53 55 56 58 64 68 73 61 64 66 60 62 65
Porosity (%) 4 2.9 2.7 2.2 0.96 0.86 0.7 1.61 1.16 1.03 1.9 1.7 1.07
Table IV demonstrate the results of the responses based on experimental design. Regression models were developed to predicting the responses using Minitab (Version 17) software. The regression models of MRR, EWR and Ra after elimination of non-significant parameters which being significant within 95% of confidence intervals with their coefficient of multiple determination are presented in (6-8). The differences between measured results and predicted results based on these models are illustrated in Fig. 3. These figures indicate that the developed models are capable to representing the system under the given experimental domain. MRR = 40.0942 + 24.5484 Ip + 8.5241 Ton ‒ 3.7244 Toff + 5.7273 Ip2 ‒ 2.0432 Ton 2 + 4.8470 Ip × Ton ‒ 2.1095 Ip × Toff ‒ 0.7558 Ton × Toff (6) R2= 93.87 EWR = 0.43170 + 0.23190 Ip + 0.03850 Ton ‒ 0.03870 Toff ‒ 0.03620 Ip2 ‒ 0.02100 Ton × Toff (7) R2 = 93.76% Ra = 2.99830 + 2.09410 Ip + 0.78760 Ton ‒ 0.07840 Toff + 0.81980 Ip2 + 0.49812 Ip × Toff (8)
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2018 International Conference on Advances in Sustainable Engineering and Applications (ICASEA), Wasit University, Kut, Iraq. R2= 93.30% TABLE IV. Run No. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
EXPERIMENTAL RESULTS OF RESPONSE.
Ip (Amp) 0 0 -1 0 0 -1 1 0 0 -1 -1 1 0 -1 1 1 0 0 1 0
Ton (µsec) 0 0 1 0 0 -1 1 0 0 -1 1 1 1 0 -1 -1 -1 0 0 0
Toff (µsec) 0 -1 -1 0 0 -1 1 0 0 1 1 -1 0 0 1 -1 0 1 0 0
MRR (mm3/min) 40.165 42.750 25.305 40.165 40.159 16.880 74.360 40.170 40.148 14.166 21.250 88.535 46.335 20.592 49.570 59.040 30.888 35.920 72.172 40.156
EWR (mm3/min) 0.402 0.500 0.251 0.445 0.412 0.151 0.599 0.403 0.452 0.110 0.154 0.721 0.510 0.152 0.600 0.610 0.379 0.383 0.607 0.431
Ra (µm) 3.018 3.131 2.125 3.030 3.025 1.420 6.909 3.024 3.015 1.320 1.980 7.439 3.483 1.775 4.606 4.392 2.322 2.908 6.215 3.027
Fig. 4. Main effect plots for: (A): MRR; (B): EWR; (C): Ra.
MRR tends to increase with the increase in Ip by about 250 %. It can be attributed that MRR is proportional to the product of energy per pulse and pulse frequency. Increasing the pulse current at a constant frequency increase the energy of the pulse and, ultimately, a higher MRR. Also when Ton increase, MRR increased by about 50%. The short Ton causes less vaporization, whereas long pulse duration causes the plasma channel to expand. The expansion of the plasma channel causes less energy density on the workpiece, which is insufficient to melt and vaporize the workpiece. On the contrary, the increase in Toff decreases the MRR by about 16% , as with long Toff the dielectric fluid produces the cooling effect on electrode and work piece causes low machining speed resulting in decreases the MRR. The results are in agreement with reference [13]. EWR increases considerably with increase in Ip reach up to about 300% . This increment is due to higher current density available at the working gap, at higher pulse current conditions, generates a large amount of heat. This rapidly overheats the electrode and increases EWR. EWR increase by about 34% with Ton increase due to increasing in spark energy. With high values of pulse duration, a higher number of negatively charged particles in motion strike the positive tool electrode thus increasing the rate of melting in electrode material. EWR decrease by 23% with increase in pulse off time because of the same reason as that mentioned for the MRR. This results are in agreement with reference [14].
Fig. 3. Scatter plots of the experimental and predicted values of : (A) MRR; (B) EWR; (C) Ra.
Ra increased by 266 %as the Ip increases due to that the increase in Ip causes an increase in the discharge strikes the surface of the sample more intensely, and creates an impact force on the molten material in the crater, causing more molten material to be ejected out of the crater, and the Ra of machined surface increases as it is concluded in reference [15]. Similarly Ra increased by 50 % as the Ton increased from low to high level alone at constant middle values of other parameters. While the Ton is increased the melting isotherms which penetrate further into the interior of the material, and the molten zone extends further into material and this produces a greater white layer thickness. Accordingly, as the Ton increases the Ra increases that is supported by reference [16]. Finally, the Ra tends to decrease when the Toff increase by 22 % due to that long Toff yields low
The effects of pulse current, pulse on time and pulse off time on metal removal rate, electrode wear rate, and surface roughness are shown in Fig. 4 and discussed below:
224
2018 International Conference on Advances in Sustainable Engineering and Applications (ICASEA), Wasit University, Kut, Iraq. metal removal so that smaller and shallow craters are attained. The long Toff provides better cooling effect and enough time to flush away the molten material and debris from the machining gap. This result is in agreement with reference [11].
(ANOVA) was used to determine the percentage of contribution of each machining condition on the response as shown in Table VI. It is clear that the pulse current (Ip) is the most significant parameter that control the machining process.
Table V represents the Analysis of variant (ANOVA) results with 95% confidence level. From ANOVA, it can be noted that all the quadratic regression models either more significant (pvalue = 0) or significant (0 < p-value < 0.05), except lack of fits for EWR and Toff for Ra (p-value = 0.459 and 0.160 respectively) turn out to be insignificant and thus all the models adequately represent the experimental data. TABLE V. Source For MRR Regression Linear Ip Ton Toff Square Ip×Ip Ton×Ton Interaction Ip×Ton Ip×Toff Ton×Toff Residual Error Lack-ofFit Pure Error Total For EWR Regression Linear Ip Ton Toff Square Ip×Ip Interaction Ton×Toff Residual Error Lack-ofFit Pure Error Total For Ra Regression Linear Ip Ton Toff Square Ip×Ip Interaction Ip×Ton Residual Error Lack-ofFit Pure Error Total
TABLE VI. RESPONSES.
CONTRIBUTION PERCENTAGE OF MACHINING CONDITIONS ON
Responses MRR (mm³/min) EWR (mm³/min) Ra (µm)
ANOVA RESULTS FOR MRR, EWR AND RA. DF
Seq SS
Adj SS
Adj MS
F
P
8 3 1 1 1 2 1 1 3 1 1 1 11
7234.34 6891.55 6026.24 726.60 138.71 114.67 101.31 13.36 228.12 187.95 35.60 4.57 9.70
7234.34 6891.55 6026.24 726.60 138.71 114.67 104.97 13.36 228.12 187.95 35.60 4.57 9.70
904.29 2297.18 6026.24 726.60 138.71 57.34 104.97 13.36 76.04 187.95 35.60 4.57 0.88
1025.15 2604.19 6831.61 823.71 157.25 65.00 119.00 15.14 86.20 213.07 40.36 5.18
0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.003 0.000 0.000 0.000 0.044
6
9.70
9.70
1.62
26125.22
0.000
5 19
0.00 7244.05
0.00
0.00
5 3 1 1 1 1 1 1 1 14
0.577656 0.567576 0.537776 0.014823 0.014977 0.006552 0.006552 0.003528 0.003528 0.007235
0.577656 0.567576 0.537776 0.014823 0.014977 0.006552 0.006552 0.003528 0.003528 0.007235
0.115531 0.189192 0.537776 0.014823 0.014977 0.006552 0.006552 0.003528 0.003528 0.000517
223.55 366.09 1040.60 28.68 28.98 12.68 12.68 6.83 6.83
0.000 0.000 0.000 0.000 0.000 0.003 0.003 0.020 0.020
9
0.004892
0.004892
0.000544
1.16
0.459
5 19
0.002343 0.584891
0.002343
0.000469
5 3 1 1 1 1 1 1 1 14
55.4625 50.1172 43.8525 6.2031 0.0615 3.3604 3.3604 1.9850 1.9850 0.3905
55.4625 50.1172 43.8525 6.2031 0.0615 3.3604 3.3604 1.9850 1.9850 0.3905
11.0925 16.7057 43.8525 6.2031 0.0615 3.3604 3.3604 1.9850 1.9850 0.0279
397.71 598.97 1572.30 222.41 2.20 120.48 120.48 71.17 71.17
0.000 0.000 0.000 0.000 0.160 0.000 0.000 0.000 0.000
9
0.3903
0.3903
0.0434
1365.20
0.000
5 19
0.0002 55.8530
0.0002
0.0000
225
Percentage of contribution % Ip(Amp) Ton (µsec) Toff (µsec) 86.13 11.54 2.18 93.06 1.65 2.86 86.30 12.88 0.11
C. Optimization of Machining Conditions The response optimizer option within the design of experiments module of Minitab statistical software package, release 17, was utilized. It can be concluded that machining process with pulse current of (6.42 Amp), (166.66 µsec) pulse on time and (34.09 µsec) pulse off time gives maximum value of material removal rate of (35.2919 mm³/min) and minimum values of electrode wear rate of (0.3766 mm³/min) and minimum surface roughness of (2.5476 µm). Response was within the satisfactory region. D. Confirmation of the Optimum Results The confirmation experiments is very important step and an indispensable part of every optimization attempt to validate the optimum machining conditions that resulted from response surface methodology approach. Verification experiment was performed at the obtained optimal input parametric setting to compare the actual MRR, EWR, and Ra with those as optimal responses got through desirability approach. Table VII summarizes the comparison of the experimental (MRR, EWR, and Ra) with their predicted values and their percentage of relative verification errors using optimal machining conditions. TABLE VII.
CONFIRMATION TEST RESULTS AND PERCENTAGE ERRORS.
Response
Experimental
Predicted
Error %
MRR (mm³/min) EWR (mm³/min) Ra (µm)
34.447 0.357 2.412
35.291 0.376 2.547
2.2 5.3 5.6
The error percentages are within the range of (2.2 to 5.6) %. So the developed predicted models can be successfully utilized to predict the MRR, EWR and Ra for any collection of the Ip, Ton and Toff values within the range of the conducted experiments. E. Effect of Vibration on Machinability Sample of Al- 5wt% Al2O3 solidified without the effect of mold vibration was machined under optimum machining condition (Ip=6.42 Amp, Ton = 166.6 µsec and Toff =34.06 µsec) to investigate the influence of mechanical vibration on machining properties. Table VIII illustrates the machining properties of sample solidified with and without the effect of vibration with optimum machining conditions.
2018 International Conference on Advances in Sustainable Engineering and Applications (ICASEA), Wasit University, Kut, Iraq. TABLE VIII. SAMPLES.
MACHINING RESULTS OF VIBRATED AND UN VIBRATED
Sample solidified with vibration Sample solidified without vibration
MRR (mm³/min) 34.447
EWR (mm³/min) 0.357
Ra (μm) 2.412
29.25
0.298
3.125
[2]
[3]
[4]
The results show that the MRR and EWR increased in the case of sample solidified with vibration. This is due to the fact that mechanical vibration reduced porosity and enhance the mass feeding of liquid phase therefore obtained homogenous metal structure and this leads to a higher value for the removed material.
[5]
[6] [7]
Electrode wear rate also increase due to increasing in the material removal rate which means a higher number of negatively charged particles in motion strike the positive tool electrode thus increasing the rate of melting in electrode material. On the contrary, the surface roughness tend to decline in the case of vibration sample due to the declining in the size of particles from 56µm to 32µm .Small grain size means more homogeneous structure and free of pores, cavities and blisters, which result in an enhancement in the machined surface.
[8] [9]
[10]
[11] [12]
IV. CONCLUSION 1. By mechanical vibration, brinell hardness was increased by (38%), while grain size of particles and porosity reduced by (43%) and (83%) respectively.
[13]
2. The optional setting of process parameters for optimal MRR, EWR, and Ra is pulse current (6.42 Amp), pulse on time (166.66 µsec) and pulse off time (34.09 µsec).
[14]
[15]
3. Due to solidification under mechanical vibration, MRR and Ra had been improved by (18%) and (23%) respectively, while EWR increase by (20%). [16]
4. From ANOVA analysis, Ip has a greater influence on the MRR, EWR and Ra followed by T on and Toff. 5. Response surface methodology (RSM) can be applied to develop a mathematical model for prediction of machining properties of aluminum-alumina composite within the parameter range of this study. ACKNOWLEDGMENT The authors wish to acknowledge the entire staff of Materials engineering college / University of Babylon / Iraq for their extended help during experimental activities. REFERENCES [1]
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