International Journal of Mechanical & Mechatronics Engineering IJMME-IJENS Vol:17 No:06 17
170506-8484-IJMME-IJENS © December 2017 IJENS I J E N S
Experimental Study and Prediction of Erosion-
Corrosion of AA6066 Aluminum Using Artificial
Neural Network
Osama M. Irfan1,2, Hanafy M. Omar 1
1Mechanical Engineering Department, Qassim University, Saudi Arabia. 2Production Engineering Department, Beni-Suef University, Egypt.
Abstract-- Erosion-Corrosion is a serious problem as it
accelerates the degradation of the material due to the relative
motion of a corrosive fluid on the exposed surface. Usually,
erosion-corrosion occurs in pipelines carrying fluids containing
solid particles. Alloys and composite materials are widely used in
various industrial applications due to their excellent properties.
The significance of 6xxx aluminum alloys attributed to the
progressive increase in using them as matrices for metal matrix
composites, due to their excellent formability and relatively good
corrosion resistance. Hence, mechanical and surface
characterization of the alloy and processing procedure are
important for that approach. Time of experiment, slurry velocity,
impact angle, subjected area, and erodent concentration are very
important factors influencing erosion-corrosion characteristics.
The main objectives of this work are to study experimentally the
erosion-corrosion behavior of AA6066 aluminum alloy and
develop a nonlinear predictive model for the erosion-corrosion
characteristics under different conditions. Artificial Neural
Network (ANN) was employed where the model consists of a three
layered feedforward back propagation neural network
(FFBPNN). A good agreement between the predicted values and
the experimental results were achieved.
Index Term-- Erosion-Corrosion; AA6066 Aluminum; Slurry
Pot; Artificial Neural Network
1. INTRODUCTION
Corrosion is a gradual damage of metal surface due to chemical
reaction while erosion is a material loss due to the affecting of
solid particles. Erosion-Corrosion is the effect whenever hard
solid particles are existing in a gas or liquid medium impacting
an object for a long time at a considerable velocity [1].
Erosion-Corrosion is a serious problem as it accelerates the
degradation of the material due to the relative motion of a
corrosive fluid on the exposed surface. The combined effect of
these two processes are complex where both processes can
supplement each other in accelerating the total wear rate [2].
Usually, erosion-corrosion occurs in pipelines carrying fluids
containing solid particles, turbines, pump impeller blades, high-
speed vehicles, aircraft engine blades, water turbines, and
missile components [3]. In comparison with pure metals, alloys
have additional properties and benefits, like easy workability,
high strength as well as lighter weight [4]. Aluminum alloys
and composites are widely used in rotor blades, pump impeller
blades, pipelines, water turbines, aerospace industries, and
military applications. As these parts often operate in harsh
environments, erosion-corrosion is considered as an important
characteristic of the materials used in these conditions[5]. The
6xxx aluminum alloys are widely used as matrices for metal
matrix composites, due to their good formability and relatively
erosion-corrosion resistance [6-10]. Hence, mechanical and
surface characterization of the alloy and processing procedure
are important for that approach. Many researchers found that
slurry velocity, impact angle, erodent concentration and its size
are very important factors influencing erosion-corrosion
characteristics. For example, erosion-corrosion of carbon steel
was studied by Guo et al. [11]. While Neville et al. [12] studied;
the erosion-corrosion of WC based metal matrix composites.
J.G. Chacon-Nava et al. [13] studied the erosion of alumina and
silicon carbide. The results revealed that the high hardness of
ceramics through higher densifications leads to a better erosion
resistance. Furthermore, erosion-corrosion of a carbon steel and
stainless steel was investigated by Dong et al. [14]. The results
indicated that erosion is the dominating variable in the
synergism of the galvanic couple. When the flow velocity
increases, the pure erosion and corrosion-enhanced erosion
controlled the overall erosion-corrosion process. J. R. Shadley
et al. [15] studied the erosion-corrosion of carbon steel pipes
in an environment containing carbon dioxide. The results
showed that erosion and corrosion can be predicted and it is
known whether the critical velocity is above or below a specific
flow velocity.
Artificial neural networks (ANNs) are commonly used in many
areas of engineering and science. ANNs are applied in
prediction, optimization, and estimating properties of different
materials. More details of ANN principles, methods and
techniques are discussed in various literatures [16-24]. An
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artificial neural network (ANN) model to predict the erosion
behavior of two typical steel boilers was developed by S.K. Das
et al. [25]. It has been found that the ANN predictions of erosion
rate had an excellent agreement with the measured data. S.K.
Das [26] developed mathematical models to realize and
characterize the how the silica (SiO2) content in the fly ash
affects the erosion behavior of several boiler grade steels.
Furthermore, a probabilistic modeling methodology was
applied for further investigations of erosion-corrosion of some
boiler grade steels [27 and 28]. C. Syamsundar et al. [29]
proposed artificial neural networks (ANNs) in concurrence
with genetic algorithm (GA) to predict the erosion wear with
respect to operating parameters for 16Cr–5Ni steels. Zmak, I.
and Curkovic, L. [30] applied ANN models to evaluate the
corrosion behavior of a high-purity alumina in an acidic
solution. It has been reported that the artificial feed-forward
neural network (AFFNN) is an accurate and beneficial tool for
estimation of high-purity alumina.
However, the synergistic effect of various factors on erosion-
corrosion behavior of aluminum alloys has not been studied
sufficiently. It is still required to study the effect of many
factors such as flow velocity, time of experiment, impact angle
and projected area on erosion-corrosion behavior of metals and
alloys. Moreover, there are few publications exploring
specifically the erosion-corrosion of aluminum (6xxx) alloys.
The aim of the current paper is to investigate experimentally the
erosion-corrosion behavior of AA6066 aluminum and predict
the operating parameters in seawater environment by using
ANN.
The paper is organized as follows: the experimental setup and
procedure is presented in section 2 while the discussion of the
experimental results is illustrated in section 3; the micrographs
using SEM are shown in section 4 followed by the predicated
model in section 5 then the conclusion in section 6.
2. EXPERIMENTAL WORK
2.1 Materials and Methodology
The material used in the present work is AA6066 aluminum
alloy. This alloy is designed for applications requiring high
mechanical and erosive–corrosive properties. The
compositions of AA6066 aluminum are presented in Table .
The alloy was received as-extruded rods. Finger-shaped
samples were cut to a diameter of 12 mm and a length of 60
mm. Other sets of samples with different diameters and lengths
are also used. Before erosion-corrosion testing, the samples
were polished using standard emery paper of 400 and 600 grits
ensuring that no scratches existed on the surface and the
average roughness of the specimens’ surfaces was found to be
about Ra= 0.62±0.06 μm.
Table I Chemical Composition of AA 6066 Aluminum Alloy
Element Ti Zn Cr Mn Cu Fe Mg Si Al
Composition, (wt. %) 0.1 0.2 0.25 1 1 0.5 1.2 1.3 Bal
The effect of four parameters; experimental time, slurry flow
velocity, impact angle, and subjected area were studied. Based
on the limitation of motor capacity, only three velocity levels
were selected (1.5m/s, 2m/s, and 3m/s). Details of experimental
factors and their levels are presented in Table .
Table II
Range of Experimental Parameters and variables used in the experiment
Experimental Time, (hours) 6 12 24 48
Slurry flow velocity, (m/s) 1.5 2 2.5 3
Impact Angle, (o) 30 45 60 90
Subjected Area, (mm2) 240 360 480 720
Medium Simulated sea water
Erodent solid particles Silica sand (250±100µm)
Temperature 38±3 oC
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Two different media, namely 3.5 wt.% sodium chloride
solution (simulated seawater) and 3.5 wt.% sodium chloride
containing 20 wt.% of silica sand particles were used for the
experiments. Natural silica sand with average sizes of 250±100
µm particles were used as an erodent. The shape and size of
silica sand used for experiments were investigated by an optical
microscope and shown in Fig. 1.
Fig. 1. Shape and size of erodent (silica sand, 250-400 µm)
2.2 Experimental Test Rig
Erosion-corrosion tests were conducted by using a slurry pot
tester according to ASTM Standard G119-93 [31]. Mainly, the
setup consists of a drilling machine that was modified to suite
the experiments as shown in Figure 2. The drilling machine has
a driving motor with a capacity of 1.5kW. The samples were
bolted vertically on a polymer disc in such a way that they
receive slurry impact during the disc rotation. The disc (shown
in Fig. 3) was equipped with a shaft at its center to be connected
to the spindle of the drilling machine through a rigid coupling.
The motor speed can be adjusted to get various rotation speeds.
A stainless steel pot with a capacity of 11 liters was used for the
slurry. The pot had baffles to prevent the settlement of the solid
particles and allow good mixing of the slurry.
Fig. 2. Set up of Slurry pot for erosion-corrosion Tests
Fig. 3. Samples bolted on polymer disc
A water solution with 3.5% NaCl (to simulate seawater) was
used as a fluid medium for pure corrosion experiments.
Erosion-corrosion experiments were conducted by similar
solution that used in pure corrosion with adding silica sand. The
concentration of silica sand was maintained of 20wt. %. The
experiments were performed at various times, velocities,
impact angles, and subjected areas at a temperature of 38±2 oC.
The mass loss for different conditions was calculated by
measuring the weight of samples before and after the
experiments. A precision digital balance with a resolution of
±0.01mg was used for this purpose. The measurements were
repeated three times for each sample and the mean value of
mass loss was calculated. In order to get specific results, mass
loss per unit area was computed.
3. DISCUSSION OF THE EXPERIMENTAL RESULTS
The corrosion and erosion-corrosion characteristics with
different conditions of time, velocity, impact angle, and
subjected area are shown in Table 3.
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Table III
Experimental Results T
est
No.
Inputs Outputs
Tes
t N
o.
Inputs Outputs V
elo
city
(m/s
ec)
An
gle
(deg
.)
Are
a
(m
m2)
Co
rro
sio
n
(gm
/mm
2)x
10
-6
Ero
sio
n –
Co
rro
sio
n
(gm
/mm
2)x
10
-6
Vel
oci
ty
(m/s
ec)
An
gle
(d
eg.)
Are
a
(m
m2)
Co
rro
sio
n
(gm
/mm
2)x
10
-6
Ero
sio
n –
Co
rro
sio
n
(gm
/mm
2)x
10
-6
After 6 Hours After 12 Hours
1 1.5 30 240 0 0.28 28 1.5 30 240 0.33 0.42 2 1.5 30 480 0 0.28 29 1.5 30 480 0.32 0.47
3 1.5 30 720 0 0.29 30 1.5 30 720 0.39 0.59 4 1.5 45 240 0 0.33 31 1.5 45 240 0.29 0.53
5 1.5 45 480 0 0.33 32 1.5 45 480 0.39 0.63
6 1.5 45 720 0 0.35 33 1.5 45 720 0.55 0.97 7 1.5 90 240 0 0.31 34 1.5 90 240 0.39 0.79
8 1.5 90 480 0 0.31 35 1.5 90 480 0.42 0.81 9 1.5 90 720 0 0.31 36 1.5 90 720 0.51 0.93
10 2 30 240 0.03 6.32 37 2 30 240 0.29 0.78
11 2 30 480 0.03 6.91 38 2 30 480 0.32 23.52 12 2 30 720 0.04 8.72 39 2 30 720 0.39 22.87
13 2 45 240 0.04 8.24 40 2 45 240 0.33 21.79 14 2 45 480 0.04 9.62 41 2 45 480 0.39 24.21
15 2 45 720 0.05 10.44 42 2 45 720 0.44 37.12 16 2 90 240 0.14 6.83 43 2 90 240 0.31 20.97
17 2 90 480 0.07 7.22 44 2 90 480 0.32 26.21
18 2 90 720 0.15 9.21 45 2 90 720 0.41 24.21 19 3 30 240 0.15 11.98 46 3 30 240 0.47 27.04
20 3 30 480 0.18 14.86 47 3 30 480 0.51 28.66 21 3 30 720 0.2 16.39 48 3 30 720 0.52 30.28
22 3 45 240 0.17 13.22 49 3 45 240 0.49 31.9
23 3 45 480 0.19 15.76 50 3 45 480 0.59 33.52 24 3 45 720 0.21 17.23 51 3 45 720 0.64 43.26
25 3 90 240 0.21 16.57 52 3 90 240 0.42 32.64 26 3 90 480 0.19 15.67 53 3 90 480 0.47 35.21
27 3 90 720 0.21 16.49 54 3 90 720 0.59 38.86 After 24 Hours After 48 Hours
55 1.5 30 240 0.59 38.97 82 1.5 30 240 1.99 90.21
56 1.5 30 480 0.59 40.86 83 1.5 30 480 1.54 91.01 57 1.5 30 720 0.68 43.12 84 1.5 30 720 1.59 91.87
58 1.5 45 240 0.51 41.23 85 1.5 45 240 1.41 91.98 59 1.5 45 480 0.62 46.87 86 1.5 45 480 1.61 92.02
60 1.5 45 720 0.76 54.98 87 1.5 45 720 1.67 92.11
61 1.5 90 240 0.61 39.52 88 1.5 90 240 1.99 90.98 62 1.5 90 480 0.69 42.86 89 1.5 90 480 1.59 91.09
63 1.5 90 720 0.71 45.36 90 1.5 90 720 1.61 92.01 64 2 30 240 0.62 46.21 91 2 30 240 1.62 92.98
65 2 30 480 0.64 47.23 92 2 30 480 1.64 93.87 66 2 30 720 0.74 52.34 93 2 30 720 1.66 98.23
67 2 45 240 0.69 53.24 94 2 45 240 1.68 99.14
68 2 45 480 0.79 60.21 95 2 45 480 1.68 103.87 69 2 45 720 0.91 83.24 96 2 45 720 1.68 116.23
70 2 90 240 0.68 48.23 97 2 90 240 1.67 94.95
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71 2 90 480 0.71 53.18 98 2 90 480 1.68 97.17
72 2 90 720 0.86 78.34 99 2 90 720 1.72 110.23 73 3 30 240 0.85 80.21 100 3 30 240 1.98 106.26
74 3 30 480 0.97 87.56 101 3 30 480 2.12 110.12
75 3 30 720 1.13 90.76 102 3 30 720 2.26 126.35 76 3 45 240 0.98 91.33 103 3 45 240 2.11 122.31
77 3 45 480 1.22 105.56 104 3 45 480 2.23 133.75 78 3 45 720 1.58 134.61 105 3 45 720 2.88 188.84
79 3 90 240 0.89 82.21 106 3 90 240 1.99 109.22 80 3 90 480 1.11 89.64 107 3 90 480 2.98 114.13
81 3 90 720 1.98 90.88 108 3 90 720 2.88 129.35
3.1 Effect of Time
The time effect on the mass loss due to corrosion of AA6066
aluminum alloy at different velocities is shown in Figure 4. It
is clear that the interaction effect of time and flow velocity has
a considerable effect on the chemical corrosion. At low
velocities (1.5 m/s and 2 m/s), the mass loss is insignificant. A
maximum mass loss per unit area of ≈ 3 x10-6 gm/mm2 was
observed after 48hours at a velocity of 3 m/s. The effect of time
variation on the mass loss due to erosion-corrosion at flow
velocities of 1.5 m/s, 2 m/s, and 3 m/s is shown in Figure 5.
Fig. 4. Time effect on corrosion
at different flow velocities
Fig. 5. Time effect on erosion-corrosion
at different flow velocities
It is noticed from Figure 4 and Figure 5 that the mass loss due
to erosion -corrosion is much higher than that for corrosion
only. The increase in velocity and time accelerate the metal
removal rate of the passive film of the material surface and this
accelerates the erosion-corrosion effect. The increased velocity
of the slurry flow results in increasing the velocity of impact of
the abrasive particles that suspended in the slurry. However, at
low velocities (1.5m/s and 2m/s) the erosive solid particles are
not suspended completely in the water. After 48 hours, the
mass loss per unit due to erosion-corrosion is ≈180 x10-6
gm/mm2 at a flow velocity of 3m/s. The mass loss due to
erosion-corrosion is almost 60 times that obtained due to
corrosion only.
3.2 Effect of Exposed Area
Figure 6 shows the mass-loss as a function of the subjected area
that exposed to the flow of simulated seawater without erosive
solid particles (corrosion). It can be seen that with increasing
the exposed area, the mass-loss increased slightly. For the
specimens that tested in simulated seawater containing erosive
solid particles as shown in Figure 7, the mass-loss increased
significantly in the same range of exposed areas. For small-
exposed area, the mass losses due to both corrosion and
erosion-corrosion are relatively low. With increasing the
exposed area, the effect of erosion-corrosion is much more than
the effect of corrosion only. This occurs due to the severity of
erosive/abrasive attacks on the surface. These results are in
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consistent with the previous findings reported that the erosion-
corrosion is the main reason of the material removal [6].
Fig. 6. Effect of subjected area on corrosion at different times
Fig. 7. Effect of subjected area on erosion-corrosion at different
times
4. MICROGRAPHS BY SCANNING ELECTRON MICROSCOPE
(SEM)
The surface of AA6066 aluminum samples was examined to
detect the changes occur due to erosion-corrosion at various
conditions. Scanning Electron Microscopy (SEM - JEOL-JSM-
6510LV) was used for this purpose. SEM examinations were
performed for specimens tested for four testing times; 12, 24,
36, and 48 hrs. Fig. 8 (a, b, c, and d) shows the SEM pictures at
maximum harsh conditions of experiments; v= 3m/s and sand
concentration= 20wt.%. Fig. 8 shows the roughening of the
surface with formation of craters that considered as a main
erosion-corrosion mechanism. Plastic deformation is clearly
noticeable on the surface. However, material cutting,
destroying and localized fractures are also dominant due to
erosion-corrosion for all experimental times.
These micrographs show a similar mechanism obtained in [3,
10, and 12] for similar materials. Fig. 8-d shows pits formed
due to erosion-corrosion after 48 hours. This indicates that for
longer time of experiments corrosion attack causes the
formation of pits that increase in size and quantity with
increasing the duration time of experiments. It is clear that
erosion dominated the overall erosion-corrosion behavior of
AA6066 aluminum alloy. Moreover, a clear difference of the
surface is observed due to erosion-corrosion at various times.
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Fig. 8. SEM images of AA6066 surface subjected to erosion-corrosion at different times (magnfication: x1000)
(v= 3m/s, silica sand concentartion=20wt.%, Impact angle 45o)
5. PREDICTION USING ANN
Artificial neural network (ANN) is a nonlinear broad class of
models that mimic the function of biological neurons inside the
human brain. They are very sophisticated modeling techniques
capable of modeling extremely complex functions. ANN user
gathers representative data, and then invokes training
algorithms to automatically learn the structure of the data.
Although the user does need to have some heuristic knowledge
of how to select and prepare data, how to select an appropriate
neural network, and how to interpret the results, the level of
user knowledge needed to successfully apply neural networks
is much lower than would be the case using some more
traditional nonlinear statistical methods. ANN consists of many
simple elements called neurons. The neurons interact with each
other using weighted connection similar to biological neurons
[16-20]. Inputs to artificial neural net are multiplied by
corresponding weights. All the weighted inputs are then
segregated and then subjected to nonlinear filtering to
determine the state or active level of the neurons, Fig. 9.
Neurons are generally configured in regular and highly
interconnected topology in ANN. The networks consist of one
or more layers between input and output layers. These layers
are called hidden layers [32]. There is no clear-cut methodology
to decide parameters, topologies, and method of training ANN.
Therefore, optimization techniques are developed to get the
values of these parameters by minimizing the total prediction
error that is defined as:
𝐸(𝑊) =∑[𝑌 − 𝑉(𝑊)]2
Where:
Y is the output vector
V is the predicted output vector
W is the network weights vector W=W(w1, w2,….,wn)
(a) 12 hours (b) 24 hours
(c) 36 hours (d) 48 hours
Craters
Small Pits Bigger Pits
Craters
(a) 12 hours (b) 24 hours
(c) 36 hours (d) 48 hours
Craters
Small Pits Bigger Pits
Craters
(a) 12 hours (b) 24 hours
(c) 36 hours (d) 48 hours
Craters
Small Pits Bigger Pits
Craters
(a) 12 hours (b) 24 hours
(c) 36 hours (d) 48 hours
Craters
Small Pits Bigger Pits
Craters
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Fig. 9. Structure of ANN used in this work
The adjustment of the weights is done by the method of backpropagation. The updating formula for W can be given by the
gradient descent method as follows:
old
new old WW W E W
Where α is the learning parameter and is generally taken between 0 and 1.
The training algorithm can be explained as follows:
Define the network architecture ((Hidden layers, neurons in each hidden layer)
Define the learning parameter
Initialize the network with random weights
If the convergence criterion is not met, do the following
For i = 1 to # training data points
Feed forward the ith observation through the net
Compute the prediction error on ith observation
Back propagate the error and adjust weights
Next i
Check for Convergence
End Do
The training stops when the global minima of the error surface
is reached [32]. The system under consideration in this work
has four inputs and two outputs, Fig. 9. The Matlab neural
network toolbox is used to develop a nonlinear ANN model that
describes the relation between the inputs and the outputs. We
randomly choose 80% of the available experimental data to
train the ANN and use the remaining 20% to test the accuracy
of the model. The Matlab function ‘trainlm’, which is based on
Levenberg-Marquardt optimization technique, is the fastest
backpropagation algorithm in the toolbox. Therefore, it is
X1 (Time)
X2 (Speed)
X3 (Angle)
X4 (Area)
Y1 (Corrosion)
Y2 (Erosion-Corrosion)
(Input Layer)
(Hidden Layer)
(Output Layer)w1
w2
wn
Input (Xi) Output (Yi)
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chosen to train the network by updating the weight and the bias
states.
To investigate the effect of the number of the hidden layers on
the accuracy of the model, the maximum error between the
experimental and the predicted values for the two outputs is
obtained as shown in Fig. 10 and Fig. 11. It is observed that the
minimum values of the maximum errors for the two outputs
occurred when the number of hidden layers are 5 or 10. Since
the data used for training and testing are chosen randomly, it is
expected to get different results at each run. Therefore, we
repeated the training process many times for different number
of hidden layers and observe the error for the erosion-corrosion
because it is more important than the corrosion. From these
runs, we found that the topology with 10 hidden is the best for
our model since the maximum error for this topology is smallest
for both the training and the testing experimental points.
Fig. 10. Effect of the number of hidden layers on the first output
Fig. 11. Effect of the number of hidden layers on the second output –First run
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Fig. 12. Effect of the number of hidden layers on the second output –Second run
Fig. 13. Effect of the number of hidden layers on the second output –Third run
The details of the developed prediction model with 10 hidden
layers are illustrated in the following figures. The variation of
the mean squared error (MSE) versus the number of epochs is
shown in Fig. 14, which indicates that MSE converges
exponentially. The best results are obtained after nearly 80
epochs. The error histogram is shown in Figure 15 which
shows that the errors are small and bounded.
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Fig. 14. Variation of MSE with number of epochs
Fig. 15. Error histogram
Figures 14-15 show the two outputs predicted by ANN
compared to the experimental values. It can be observed that
the most predicted values are within 5% of the experimental
values. It is obvious that the accuracy of predicating the points
used for training is better than the accuracy of predicting the
points used for testing.
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Fig. 16. The experimental and predicted values of corrosion for 10 hidden layers
Fig. 17. The experimental and predicted values of erosion-corrosion for 10 hidden layers
Fig. 18 and Fig. 19 show the experimental and predicted values
versus the test numbers for the topology of 10 hidden layers. It
is observed that the predicted values are almost coincide with
the experimental values for the points used for training.
However, the predicted values of the testing points are slightly
away from the experimental values. Therefore, we conclude
that the topology of 10 hidden layers represents the best
topology, which give results that are nearly matched with the
experimental values. It has also the ability to predicate the
corrosion and erosion-corrosion values without the need to
perform extra experimental work.
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Fig. 18. Experimental and predicted corrosion values versus the test number for 10 hidden layers.
Fig. 19. Experimental and predicted erosion-corrosion values versus test number
for 10 hidden layers
6. CONCLUSION
A set of experiments were conducted to investigate the effect of
different parameters on erosion-corrosion behavior of AA6066
aluminum. The slurry pot method was applied for the
experiments. Finger shaped specimens were subjected to
corrosion in simulated seawater for 12 hours, 24 hours, 36
hours, and 48 hours. Other specimens were subjected to
erosion-corrosion at three different impact velocities (1.5 m/s,
2m/s, and 3 m/s). Silica sand with 20wt. % concentration was
used as erodent. Furthermore, ANN was used to predict the
operating erosion-corrosion parameters in seawater
environment. The following conclusions can be drawn from the
study:
1. Erosion is the governing contributor to erosion-corrosion
behavior of AA6066 aluminum especially at high flow
velocities, while the contribution of corrosion is slight.
2. The mechanism of erosion-corrosion of the tested alloy is a
combination of corrosion and erosion. This occurs because
of the impact of solid particles and abrasion action on the
surface.
3. Increasing the velocity and time of experiment produced
higher mass loss for AA6066 aluminum. Significant
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increase in mass loss was observed after 48 hours at a
velocity of 3m/s. For corrosion, the mass loss increases of
between 0.5x10-6 gm/mm2 and 3 x10-6 gm/mm2, while for
erosion-corrosion condition the mass loss increases of
between 48x10-6 gm/mm2 and 180 x10-6 gm/mm2. The
reason of the higher mass loss for erosion-corrosion
condition is the increase in kinetic energy of the erosive
solid particles. This leads to higher shear stresses, which
causes more mass loss.
4. The SEM micrographs of the surface showed a formation
of craters and pits which increase in size and quantity with
increasing the time and velocity. The obtained SEM pictures
clarify the reason why AA6066 aluminum suffered
considerable higher mass loss due to erosion-corrosion.
5. By using ANN, a nonlinear model was developed to predict
the corrosion and erosion-corrosion behavior of AA 6066
Aluminum.
6. When using a topology of 10 hidden layers, good agreement
between the predicted and the experimental values was
achieved. Therefore, the proposed model can be used to
predict the erosion-corrosion for a broad range of operating
conditions without the need to conduct experimental work
that will certainly save a considerable amount of money,
time and effort. The same technique can be extended to
predict the erosion-corrosion behavior of other materials.
ACKNOWLEDGEMENTS The authors would like to acknowledge the support received
from Deanship of Scientific Research, Qassim university for
funding this work under the grant No. 1118-qec-2016-1-12-S.
In addition, the support and advices provided by Prof. El-
Badrawy Abu-Elnasr at Taif University are gratefully
acknowledged.
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