Journal of Minerals and Materials Characterization and Engineering, 2013, 1, 307-314
Published Online November 2013 (http://www.scirp.org/journal/jmmce)
http://dx.doi.org/10.4236/jmmce.2013.16046
Open Access JMMCE
Predictability of Al-Mn Alloy Exposur e Time Based on Its
As-Cast Weight and Corrosion Rate in Sea Water
Environment
Chukwuka Nwoye1*, Simeon Neife2, Ebere Ameh3, Awo Nwobasi4, Ndubuisi Idenyi5
1Department of Metallurgical and Materials Engineering, Nnamdi Azikiwe University, Awka, Nigeria
2Department of Metallurgical and Materials Engineering, University of Nigeria, Nsukka, Nigeria
3Department of Metallurgical and Materials Engineering, Enugu State University of Science & Technology, Enugu, Nigeria
4Department of Technology and Vocational Education, Ebonyi State University, Abakiliki, Nigeria
5Department of Industrial Physics, Ebonyi State University, Abakaliki, Nigeria
Received September 8, 2013; revised October 18, 2013; accepted October 30, 2013
Copyright © 2013 Chukwuka Nwoye et al. This is an open access article distributed under the Creative Commons Attribution Li-
cense, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
ABSTRACT
This paper presents the predictability of aluminium-manganese alloy exposure time based on its as-cast weight and
corrosion rate in sea water environment. The validity of the derived model: 26.67 0.550.29



 is rooted on the
core expression: 0.03750.0206 0.0109

  where both sides of the expression are correspondingly approximately
equal. Statistical analysis of model-predicted and experimentally evaluated exposure time for each value of as-cast
weight and alloy corrosion rate considered shows a standard error of 0.0017% & 0.0044% and 0.0140% & 0.0150%
respectively. The depths of corrosion penetration (at increasing corrosion rate: 0.0104 - 0.0157 mm/yr) as predicted by
derived model and obtained from experiment are 0.7208 × 10−4 & 1.0123 × 10−4 mm and 2.5460 × 10−4 & 1.8240 × 10−4
mm (at decreasing corrosion rate: 0.0157 - 0.0062 mm/yr) respectively. Deviational analysis indicates that the maxi-
mum deviation of the model-predicted alloy exposure time from the corresponding experimental value is less than 10%.
Keywords: Prediction; Exposure Time; Al-Mn Alloys; Sea Water; Alloy As-Cast Weight; Corrosion Rate
1. Introduction
The service performance of metals (or alloy), irrespective
of the exposure environment is largely affected by their
physical and mechanical properties such as hardness,
weldability, toughness, malleability, ductility, resistance
to fatique etc. It is therefore vital to consider these and
other basic properties of metals or alloys when proposing
their application for fabrication and other engineering
purposes.
Researchers [1] have suggested the need to know the
specific corrosion rates of different metals and alloys in
different application environment in order to know the
materials that can withstand outdoor structural applica-
tions.
The stability of metals or alloys in an aggressive envi-
ronment has been reported [2] to basically depend on the
protective properties of organic or inorganic films as well
as on the layer of corrosion products. The scientists con-
cluded that the ability of films to act as controlling barri-
ers against different kinds of corrosion attack is depend-
ent on film properties such as chemical composition,
adhesion, conductivity, solubility, morphology and hy-
groscopicity. Several researchers [1,3] reported that the
highlighted characteristic of films in turn depends on
environmental variables such as atmospheric conditions,
type and amount of pollutants as well as wet-dry cycle,
the chemical composition and metallurgical history of the
metals or alloys and physicochemical properties of coat-
ing.
Malfunctioning of engineering structures and equip-
ment due to corrosion has been reported [4] to stem from
tragic carelessness in plumbing, equipment manufacture
and installation, with possibility of explosion, fire and
spread of toxic materials in living environment. This is
accompanied by some costs such as replacement of cor-
roded equipment, disturbance in processes due to equip-
*Corresponding author.

C. NWOYE ET AL. 308
ment corrosion, shutdown of plants due to replacement of
corroded equipment, impurity in processed products due
to corrosion as well as waste of the products of those
vessels which are attacked by corrosion. The report indi-
cates that about 70 percent of losses can be prevented by
observing related principles and instructions.
One of the main problems in oil and gas industries is
corrosion of pipelines and other engineering structures.
This has always resulted in huge economic setback due
to large sum of money spent in trying to combat it. Based
on the forgoing, there is a great need to develop engi-
neering materials that are corrosion resistant to avoid
abrupt failure of the engineering structures.
Recently, the corrosion characteristics of selected fer-
rous metal samples (plain and alloyed ductile iron, low
carbon steel and austenitic stainless steel) in crude oil
were investigated by using weight loss method [5]. Stud-
ies carried out during this work show that all the materi-
als experience gain in weight within the first 10 days in
the medium. This weight gain is attributed to the forma-
tion of hard and passive phases which acted as strong
protective barriers to corrosion. It was also observed that
the rate of corrosion decreased with increase in the num-
ber of days of exposure for all the coupons, this may be
probably due to the deposition of corrosion products that
tend to shield the corroding surface from further corro-
sion attack, thereby depressing the rate of corrosion. This
result shows that despite of initial low corrosion resis-
tance of plain ductile iron, it can still be considered,
alongside other materials, for application in pipelines and
storage facilities for crude oil.
Studies [6] on the corrosion behaviour of carbon steel
under natural and stagnant seawater conditions have
showed that the alloy is more corrosive in anaerobic stag-
nant sea-water conditions than that in aerobic conditions.
The study also revealed that in both aerobic and anaero-
bic exposures, corrosion was more aggressive on hori-
zontally oriented coupons compared to vertically orient-
ed samples.
The corrosion behaviour of low carbon steel has also
been investigated [7] in natural seawater and various
synthetic seawaters. It was found that the steel corroded
nearly four times faster in a 3.5% NaCl solution than in
natural seawater for an exposure time of 21 days. The
corrosion rate after immersion in synthetic seawaters was
found to be similar to the corrosion rate after immersion
in natural seawater. Calcium carbonate (aragonite) de-
posits were found on the surface of the steel after immer-
sion in natural seawater and the synthetic seawaters.
Some magnesium-containing deposits were also found
after immersion in the natural seawater. These deposits
act as a barrier against oxygen diffusion and thereby
lower the corrosion rate. The morphology of the calcium
carbonate deposits that formed during immersion in the
natural seawater was found to be different from those
formed during immersion in the solution.
It has been shown [8] that MnAl6 formed from Al and
Mn has almost the same electrode potential as aluminium
and this compound is capable of dissolving iron which
reduces the detrimental effect of Mn. Commercial Al-Mn
alloys contains up to 1.25% manganese although the
maximum solid solubility of this element in aluminium is
as high as 1.82%. This limitation was imposed because
the presence of iron as impurity reduces the solubility
and there is a danger that large primary particles of
MnAl6 will form with a disastrous effect on local ductil-
ity.
Polmear [8] reported that Al-Mn alloys belong to the
3xxx series of alloys which are used for the manufacture
of roofing sheets. These sheets are subject to corrosion
because of the presence of moisture and oxygen in the
atmosphere. The corrosion of this alloy is due to the
strong affinity aluminium has for oxygen which results to
its oxidation and subsequent formation of oxide film.
Ekuma et al. [9] reported that with time, this film be-
comes passive to further oxidation and stable in aqueous
media when the pH is between 4.0 and 8.5. It is impor-
tant to state that the passive films can break and fall off,
hence exposing the surface of the alloy to further corro-
sion.
Studies [4] on corrosion management indicate that it
offers preventive strategies in two technical and non-
technical domains. Technical domains as preventive
strategies are highly important. These includes: 1) Up-
grading planning methods and using advanced planning
ones to better corrosion management and so prevent
avoidable corrosion costs. In this vein, planning methods
must change and the best corrosion technologies must be
available for planners. 2) Improving corrosion technolo-
gies via research and development. Corrosion can be
controlled in most industries by using scientific methods
and new technological achievements.
Non-technical domain as preventive strategies includes:
1) Enhancing the employees' awareness about the high
costs of corrosion and saving costs result in correct ap-
plication of existing technologies and corrosion costs.
Thus a lot of corrosion problems are due to lack of
awareness about corrosion management and accountabil-
ity of people in exchanging operations, inspection and
maintenance of management system. 2) Changing guide-
lines, protocols, standards and management methods to
reduce corrosion costs by correct corrosion management,
resulting in effective control of corrosion and safe opera-
tion and increase in shelf life of equipment. 3) Amend-
ing and generalization of employees’ instruction to in-
troduce and identify corrosion control. 4) Changing and
amending wrong belief about not being able to do any-
thing about corrosion and making new decisions in pre-
Open Access JMMCE

C. NWOYE ET AL. 309
venting this phenomenon.
The aim of this work is to ascertain the predictability
of aluminium-manganese alloy exposure time based on
its as-cast weight and corrosion rate in sea water envi-
ronment. The model to be derived is expected to evaluate
and predict directly the exact time (exposure time) at
which the Al-Mn alloy should be exposed in the sea wa-
ter environment putting into consideration its as-cast
weight (alloy initial weight before corrosion) and corro-
sion rate in such environment. The essence of this work
is to determine the exact exposure time for a weighed
Al-Mn alloy whose corrosion rate in the sea water envi-
ronment is already known. This rules out exposure of the
alloy to the corrosive environment longer than necessary;
a situation that could result to very disastrous, undue
corrosion penetration on the alloy and invariably, failure.
The success of this work would eventually reduce abrupt
failure of Al-Mn alloy in sea water environment due to
over exposure.
2. Materials and Methods
Materials used for this work are aluminium of 96% pu-
rity and pure granulated manganese. Details of the ex-
perimental procedure and associated process conditions
are as stated in the previous report [10].
2.1. Model Formulation
Experimental data obtained from research work [10]
were used for this work. Computational analysis of the
experimental data [10] shown in Table 1, gave rise to
Table 2 which indicate that:
e
K
SN

  (1)
Introducing the values of K, S and Ne into Equation (1)
reduces it to:
0.03750.0206 0.0109

  (2)
0.0206 0.0109
0.0375









(3)
26.67 0.550.29

  (4)
where K = 0.0375; Overall Al-Mn alloy-sea water tem-
perature interaction factor (determined using C-NIK-
BRAN [11]). S = 0.0206; First order alloy degradability
Table 1. Variation of corrosion rate with exposure time and
alloy as-cast weight [10].
(β) (mm/yr) (α) (hrs) (γ) (g)
0.0104
0.0140
0.0157
0.0105
0.0062
336
450
504
600
672
12.0754
12.3911
12.4775
13.2012
13.7434
Table 2. Variation of 0.0375α with γ + 0.0206β – 0.0109.
0.0375α(γ) 0.0206β0.0109 γ + 0.0206β – 0.0109
0.00144
0.00193
0.00216
0.00257
0.00288
0.0104
0.0140
0.0157
0.0105
0.0062
0.00021
0.00029
0.00032
0.00022
0.00013
0.0109
0.0109
0.0109
0.0109
0.0109
0.00141
0.00179
0.00192
0.00252
0.00293
Coefficient (determined using C-NIKBRAN [11]); N
e=
0.0109; Film solubility-adhesion ratio in sea water (de-
termined using C-NIKBRAN [11]); (β)= Corrosion rate
(mm/yr); (α) = Exposure time (yr); (γ) = Initial weight of
alloy (kg).
2.2. Boundary and Initial Conditions
Consider solid Al-Mn alloy exposed to sea water envi-
ronment and interacting with some corrosion-induced
agents. The sea water is assumed to be affected by un-
wanted dissolved gases. Range of exposed time consid-
ered: 0.0384 - 0.0767 yrs (336 - 672 hrs). Initial weight
range considered: 0.0121 - 0.0137 kg (12.0754 - 13.7434 g).
Purity of aluminium used: 96%. Concentration of
manganese addition: 4%. Details of experiment and other
process conditions are as stated in the experimental tech-
nique [10].
The boundary conditions are: aerobic environment to
enhance Al-Mn alloy oxidation (since the sea water con-
tains oxygen). At the bottom of the exposed alloy, a zero
gradient for the gas scalar are assumed. The exposed
alloy is stationary. The sides of the solid are taken to be
symmetries.
3. Results and Discussions
The derived model is Equation (4). Computational analy-
sis of Table 1 gave rise to Tables 2 and 3. The derived
model is two-factorial in nature because it is a constituent
of two input process factors: as-cast weight (initial alloy
weight) and corrosion rate. This implies that the pre-
dicted exposure time for the Al-Mn alloy in sea water
environment is dependent on just two factors: as-cast
weight and corrosion rate of the alloy.
3.1. Model Validation
The validity of the model is strongly rooted on Equation
(2) (core model equation) where both sides of the equa-
tion are correspondingly approximately equal. Table 2
also agrees with Equation (2) following the values of
0.0375α and γ + 0.0206β – 0.0109 evaluated from the
experimental results in Table 1 . Furthermore, the derived
model was validated by comparing the exposure time
predicted by the model and that obtained from the ex-
periment [10]. This was done using various analytical
techniques.
Open Access JMMCE

C. NWOYE ET AL. 310
Table 3. Evaluated variation of corrosion rate of Al-Mn
alloy with its as-cast weight and exposure time.
(β) (mm/yr) (α) (yrs) (γ) (kg)
0.0104
0.0140
0.0157
0.0105
0.0062
0.0384
0.0514
0.0575
0.0685
0.0767
0.0121
0.0124
0.0125
0.0132
0.0137
3.2. Computational Analysis
Computational analysis of the experimental and model-
predicted exposure time was carried out to ascertain the
degree of validity of the derived model. This was done
by comparing the depth of corrosion penetration obtained
by calculations involving experimental results, and pre-
dicted directly by the model.
The depth of corrosion penetration for Al-Mn alloy
during the period of exposure in the sea water environ-
ment CD (mm) was calculated from the equation;
D
C

   (5)
Δβ = Change in the corrosion rates β2, β1 within a range
of exposure time: α1 - α2. Δα = Change in the alloy ex-
posure time α2, α1.
Considering experimental results of points (0.0384,
0.0104) and (0.0575, 0.0157) for (
α
1
, β
1
) and ( α
2
, β
2
)
respectively
(during the period corrosion rate was in-
creasing)
, (as in Figure 1 ) and substituting them into
Equation (5), gives 1.0123 × 10
− 4
mm as the depth of
corrosion penetration on the alloy during the actual cor-
rosion process
. Also similar plot (as in Figure 2 ) using
model-predicted results of points (0.0384, 0.0104) and
(0.0520, 0.0157) for ( α
1
, β
1
) and ( α
2
, β
2
) respectively,
during the period corrosion rate was increasing and sub-
stituting them into Equation (5) gives the depth of
cor-
rosion penetration on the alloy as
0.7208 × 10
− 4
mm.
This is the model-predicted depth of corrosion penetra-
tion on the alloy. Furthermore,
substituting experimental
results of points (0.0575, 0.0157) and (0.0767, 0.0062)
for (
α
1
, β
1
) and ( α
2
, β
2
) respectively (during the period
corrosion rate was decreasing)
, (as in Figure 1 ) into
Equation (5), gives
−1.8240 × 10−4 mm as the depth of
corrosion penetration on the alloy.
On other hand, substituting
model-predicted results of
points (0.0520, 0.0157) and (0.0788, 0.0062) for (
α
1
, β
1
)
and (
α
2
, β
2
) respectively, during decreasing corrosion
rate
(as in Figure 2 ) into Equation (5) gives −2.5460 ×
10−4 mm. The negative signs preceding the magnitudes
of the depth of corrosion penetration do not indicate that
the depth of the penetration is negative, but that the cor-
rosion penetration occurred when the alloy corrosion rate
is decreasing and so are discarded. Based on the forego-
ing, the depths of corrosion penetration during the period
corrosion rate was decreasing as obtained from experiments
R
2
= 0.9627
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09
0.01040.0140.0157 0.0105 0.0062
Corrosion rate (mm/yr)
Exposure time (yr)
Figure 1. Coefficient of determination between alloy expo-
sure time and its corrosion rate as obtained from the ex-
periment [10].
R
2
= 0.8722
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09
0.01040.0140.0157 0.01050.0062
Corrosion rate (mm/ yr)
Exposure time (yr)
Figure 2. Coefficient of determination between alloy expo-
sure time and its corrosion rate as predicted by derived
model.
[10] and derived model are 1.8240
× 10−4 and 2.5460 ×
10−4 mm respectively.
3.3. Statistical Analysis
Statistical analysis of model-predicted and experimen-
tally evaluated exposure time for each value of as-cast
weight and alloy corrosion rate considered shows a stan-
dard error (STEYX) of 0.0017% & 0.0044% and
0.0140%
& 0.0150
% respectively. The standard error was evalu-
ated using a Microsoft Excel [12].
Furthermore, as a way validating the derived model,
regression model (highlighted from [12]) was used to
predict the corresponding exposure time for the values of
as-cast weight and corrosion rate considered, and the
results are shown in Figures 3 and 4. On the other hand,
the standard error in predicting the exposure time for
each value of corrosion rate considered is 0.0119%.
The correlations between exposure time and as-cast
Open Access JMMCE

C. NWOYE ET AL. 311
weight as well as exposure time and corrosion rate as
obtained from derived model, regression model and ex-
perimental results [10] were calculated. This was done by
considering the coefficients of determination R2 from
Figures 1-6, using the equation:
2
RR (6)
The evaluated correlations are shown in Tables 4 and
5. The model was also validated by comparing its results
of evaluated correlations between exposure time and as-
cast weight as well as exposure time and corrosion rate
with that evaluated using experimental and regression
model-predicted results. Tables 4 and 5 show that the
correlation results from experiment, derived model (D-
MoD) and R-MoD are in proximate agreement.
3.4. Graphical Analysis
Results predicted by the regression model were plotted;
R
2
= 1
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09
0.012 0.0125 0.013 0.0135 0.014
As-cast weight (kg)
Exposure time (yr)
Figure 3. Coefficient of determination between alloy expo-
sure time and its as-cast weight as predicted by regression
model.
R
2
= 0.9368
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09
0.01040.0140.0157 0.0105 0.0062
Corrosion rate
(
mm/
y
r
)
Exposure time (yr)
Figure 4. Coefficient of determination between alloy expo-
sure time and its corrosion rate as predicted by regression
R
2
= 0.933
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09
0.0120.0125 0.0130.0135 0.01
4
As-cast wei
g
ht
(
k
g)
Exposure time (yr)
Figure 5. Coefficient of determination between alloy expo-
sure time and its as-cast weight as obtained from the ex-
periment [10].
R
2
= 0.9918
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09
0.012 0.0125 0.0130.0135 0.014
As-cast weight (kg)
Exposure time (yr)
Figure 6. Coefficient of determination between alloy expo-
able 4. Comparison of the correlations between exposure
sure time and its as-cast weight as predicted by derived
model.
T
time and alloy as-cast weight as evaluated from experimen-
tal, derived model and re gre ssion pr e dic te d results.
Based on alloy as-cast weight
Analysis ExoD
D D-MoD
R-M
CORRELL 0 .96590.9659 1.0000
able 5. Comparison of the correlations between exposure T
time and alloy corrosion rate as evaluated from experimen-
tal, derived model and re gre ssion pr e dic te d results.
Based on corrosion rate
Analysis ExD D-MoD R-MoD
CORRELL 0 .98120.9339 0.9679
xposure time against alloy as-cast weight and corrosion e
rate respectively along with results from the experiment
and derived model to analyze its spread and trend relative
to results from experiment and derived model.
model.
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C. NWOYE ET AL. 312
Comparative graphical analysis of Figures 7 and 8
3.5. Deviational Analysis
sure time from the experi-
shows very close alignment of the curves from derived
model and experiment. Figures 9 and 10 also indicate a
close alignment of curves from derived model (MoD),
regression model (R-MoD) predicted results as well as
experimental (ExD).
Comparative analysis of expo
ment [10] and derived model revealed deviations on the
part of the model-predicted values relative to values ob-
tained from the experiment. This is attributed to the fact
that the surface properties of the alloy and the physio-
chemical interaction between the alloy and corrosion
induced agents (in the sea water) were not considered
during the model formulation. This necessitated the in-
troduction of correction factor, to bring the model-pre-
dicted corrosion rate to those of the corresponding ex-
perimental values.
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09
0.0120.0125 0.0130.0135 0.014
As-cast weight (kg)
Exposu re time (yr)
ExD
MoD
Figure 7. Comparison of the alloy exposure time (relative to
its as-cast weight) as obtained from experiment [10] and
derived model.
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09
0.01040.0140.0157 0.0105 0.0062
Corrosion rate
(
mm/
y
r
)
Exposed time (yr)
ExD
MoD
Figure 8. Comparison of the alloy exposure time (relative to
derived model.
its corrosion rate) as obtained from experiment [10] and
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09
0.012 0.0125 0.0130.0135 0.014
As-cast weight (kg)
Exposure time (yr) ExD
MoD
ReG
Figure 9. Comparison of the alloy exposure time (relative to
its as-cast weight) as obtained from experiment [10] derived
and regression model.
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09
Exp osure time (yr)
ExD
MoD
0.0104 0.014 0.01570.01050.0062
Corrsion rate
(
mm/
y
r
)
ReG
Figure 10. Comparison of the alloy exposure time (relative
to its corrosion rate) as obtained from experiment [10] de-
predicted corrosion rate from
at of
rived and regression model.
Deviation (Dn) of model-
th the experiment[10] is given by
–100
CR CR
PE
Dn 


CR
E

(14)
Correction factor (Cr) is the negative
i.e.
of the deviation
Cr Dn

 (15)
therefore
–100
CR CR
CR
PE
Cr E

 


(16)
where: PCR = Model-predicted exposure ti
y the corre-
me (yr); ECR =
Exposure time obtained from experiment [10] (yr); Cr =
Correction factor (%); Dn = Deviation (%).
Introduction of the corresponding values of Cr from
Equation (9) into the model gives exactl
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C. NWOYE ET AL. 313
sp
the corre-
sp
of the curve in Figures 13 and 14 is op-
po
onding experimental corrosion rate.
Figures 11 and 12 show that the maximum deviation
of the mode-predicted exposure time from
onding experimental values is less than 10% and quite
within the acceptable deviation limit of experimental
results. These figures show that least and highest magni-
tudes of deviation of the model-predicted exposure time
(from the corresponding experimental values) are 0% and
−9.57% which corresponds to exposure times: 0.0384
and 0.0520 yr, alloy as-cast weight; 0.0121 and 0.0125
kg and alloy corrosion rates; 0.0104 and 0.0157 mm/yr
respectively.
Comparative analysis of Figures 11-14 indicates that
the orientation
site that of the deviation of model-predicted exposure
time (Figures 11 and 12). This is because correction
factor is the negative of the deviation as shown in Equa-
tions (8) and (19). It is believed that the correction factor
takes care of the effects of the surface properties of the
alloy which were not considered during the model for-
mulation. Figures 13 and 14 indicate that the least and
highest magnitudes of correction factor to the model-
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09 4
0.0121 0.0124 0.0125 0.0132 0.0137
As-cast weight (kg)
Exposure tim e (yr)
-12
-10
-8
-6
-4
-2
0
2
Devia tion (%)
Expo time
Deviation
Figure 11. Variation of model-predicted alloy exposure time
(relative to its as-cast weight) with its associated devi ation
from experimental values.
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09
0.01040.0140.0157 0.0105 0.0062
Co rrosion rate
(
mm/
y
r
)
Exposure time (yr)
-12
-10
-8
-6
-4
-2
0
2
Deviation (%)
4
Expo time
Deviation
Figure 12. Variation of model-predicted alloy exposure time
(relative to its corrosion rate) with its associated devi
from experimental values. ation
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09
Exposure time (yr
-4
-2
0
2
4
6
8
Correction factor (%)
)
10
12
Expo time
0.01210.0124 0.0125 0.01320.0137
As-cast wei
g
ht
(
k
g)
Corr.factor
Figure 13. Variation of model-predicted alloy exposure time
(relative to its as-cast weight) with its associated correion
factor. ct
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09 12
0.0104 0.014 0.01570.01050.0062
Corrosion rate (mm/yr)
Exposure time (yr)
-4
-2
0
2
4
6
8
10
Correction f actor (% )
Expo time
Corr.factor
Figure 14. Variation of model-predicted alloy exposure time
(relative to its corrosion rate) with its associated correion
factor.
to exposure times: 0.0384 and 0.0520 yr, alloy
Aluminium-manganese alloy exposure time was evalu-
based on its as-cast weight and cor-
ct
predicted exposure time are 0 and +9.57% which corre-
sponds
as-cast weight; 0.0121 and 0.0125 kg and alloy corrosion
rates; 0.0104 and 0.0157 mm/yr respectively.
It is important to state that the deviation of model pre-
dicted results from that of the experiment is just the
magnitude of the value. The associated sign preceding
the value signifies that the deviation is deficit (negative
sign) or surplus (positive sign).
4. Conclusion
ated and predicted
rosion rate in sea water environment. The validity of the
derived model was rooted on the core expression:
0.03750.0206 0.0109



 where both sides of
the expression are correspondingly approximately equal.
Statistical analysis of model-predicted and experimen-
tally evaluated exposure time for each value of as-cast
weight and alloy corrosion rate considered shows a stan-
dard error of 0.0017% & 0.0044% and 0.0140% &
Open Access JMMCE

C. NWOYE ET AL.
Open Access JMMCE
314
[1] C. E. Ekuma and N. E. Idenyi, “Statistical Analysis of the
Influence of tion of Corrosion
from Its Param of Physics, Vol. 1,
0.0150 % respectively. The depths of corrosion penetra-
tion (at increasing corrosion rate: 0.0104 - 0.0157 mm/yr)
as predicted by derived model and obtained from ex-
periment are
0.7208 × 10
− 4
& 1.0123 × 10
− 4
mm and
2.5460 × 10−4 & 1.8240 × 10−4 mm (at decreasing corro-
sion rate: 0.0157 - 0.0062 mm/yr) respectively. Devi-
ational analysis indicates that the maximum deviation of
the model-predicted alloy exposure time from the corre-
sponding experimental value is less than 10%.
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