Experimental Study and Prediction of Erosion- Corrosion of

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



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



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.



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



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



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.



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



Craters


Craters



Craters


Craters



Craters


Craters



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)



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



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.



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.



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.



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



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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