World Journal of Condensed Matter Physics, 2011, 1, 33-36
doi:10.4236/wjcmp.2011.12006 Published Online May 2011 (http://www.SciRP.org/journal/wjcmp)
33
New Trends in Corrosion Analysis of Al-Sn Alloy
Duplex System
Chinedu Ekuma, Eziaku Osarolube, Ephraim O. Chukwuocha, Michael C. Onyeaju
Department of Physics, University of Port Harcourt, Port Harcourt, Nigeria.
Received February 15th, 2011; revised March 18th, 2011; accepted March 20th, 2011.
ABSTRACT
The corrosion characterization of binary Al-Sn alloy systems has been statistically analyzed in the light of developed
model equations. It was observed that the modeled corrosion penetration rate values generated using the developed
model equations are in tandem with the experimental values.
Keywords: Al-Sn, Statistical Analysis, Corrosion, Model Equations, Correlation
1. Introduction
It has since been a worldwide problem for man to combat
the menace of material degradation. Many structural
failures and general loss of valuable engineering mate-
rials have been traced to be caused basically by analysis
and not synthesis [1] as over 313 failure cases studied [2]
have shown that well over 56.90% are due to corrosion.
The causes of materials degradation with the associated
environmental variables have been well explained in
many literatures [3-14].
A critical look at all forms of co rrosion show that th ey
are merely a statistical phenomenon hence, the yardstick
behind the adoption of statistics techniques in this
present study. However, even with the somewhat
semi-empirical nature of corrosion (as it show the rela-
tions between available data and measurements that do
not necessarily reveal any relation between cause and
effect), statistical models attempt to determine the fun-
damental relationship between sets of input data (predic-
tors) and targets (predictands) [15,16].
The adoption of statistical analysis in corrosion analy-
sis in metals has been predicted for years but, its usage
has been hampered primarily by the specialty skill
needed in applying this principle which, its impediment
lies basically on the lack of reference frame for its appli-
cation [16] .
In this present study, we will apply the non-linear re-
gression analysis technique to obtain model equations that
will be used to determine the corrosion parameter of in-
terest and other statistical parameters adequate for dis-
cussing and understanding the phenomenon of corrosion
(in this case, Al-Sn alloys of compositions 2.5% and 4.5%
by weight respectively of Sn) in selected media environ-
ments: HCl and NaCl of concentrations 0.5 M and 1.0 M
respectively, using the statistical software SPSS™.
The data of Idenyi et al. [17] has been used for this
analysis.
2. Results
The results of the corrosion penetration rate values for
the experimental and modeled values are as shown in
Tables 1 to 8, while Table 9 is the model equation of the
corrosion parameters of the various Al-Sn alloy systems
in the various concentrations of hydrochloric acid and
brine environments.
3. Discussion of Results
A cursory look at Tables 1-8 clearly reveals that the
modeled corrosion penetration values of the various bi-
nary alloy samples subjected to the varying concentra-
tions (0.5 M and 1.0 M) of hydrochloric acid and brine
environments respectively, are in tandem with the ob-
served experimental values. This is further confirmed by
the nearly perfect coefficient of correlations of all the
composites which is in the range
0.94 1.00R≤≤
(see
Table 9). The implication of this high positive coeffi-
cient of correlation is that the modeled values are in good
agreement with the experimental data. Thus, our model
can be of good usage in studying effect of the studied
environments on Al-Sn binary alloy system on expanded
time scale. In order to further confirm the dependence of
the corrosion penetration rate mainly on the exposure

New Trends in Corrosion Analysis of Al-Sn Alloy Duplex System
34
Table 1. CPR Data for Al – 2.5% Sn in 0.5 M HCl Envi-
ronment.
Time (Hrs) Experimental CPR
(mm/yr) Modeled CPR (mm/yr)
12 2.08 1.763
24 1.25 1.334
36
0.90
1.083
48
0.73
0.905
60 0.62 0.767
72
0.55
0.654
84 0.51 0.559
96
0.47
0.477
108 0.44 0.404
120
0.43
0.338
132
0.42
0.28
144 0.39 0.226
Table 2. CPR Data for Al – 2.5% Sn in 1.0 M HCl Envi-
ronment.
Time (Hrs) Experimental CPR
(mm/yr) Modeled CPR (mm/yr)
12
3.12
2.815
24
2.08
2.201
36
1.66
1.843
48
1.46
1.588
60
1.25
1.391
72
1.14
1.229
84
1.07
1.093
96 1.01 0.975
108 0.92 0.870
120 0.85 0.777
132 0.79 0.693
144
0.74
0.616
Table 3. CPR Data for Al – 2.5% Sn in 0.5 M NaCl Envi-
ronment.
Time (Hrs)
Experimental CPR
(mm/yr)
Modeled CPR
(mm/yr)
12
3.74
3.201
24
2.29
2.488
36
1.66
2.071
48 1.46 1.775
60 1.37 1.545
72 1.35 1.358
84
1.22
1.199
96
1.12
1.062
108
1.02
0.941
120
0.94
0.832
132
0.87
0.734
144
0.81
0.645
time (though alloy composition and other physical fac-
tors play a vital role in corrosion experiments), the coef-
ficient of determination of the various samples in their
different environments were also determined. It can be
verified (see Tabl e 9 ) that the range of the coefficients of
determination is 2 0.88 0.99R≤≤ . This shows that ap-
proximately 92.64% of the total variation in the corr osion
Table 4. CPR Data for Al – 2.5% Sn in 1.0 M NaCl Envi-
ronment.
Time (Hrs) Experimental CPR
(mm/yr) Modeled CPR (mm/yr)
12
2.50
2.140
24
1.46
1.646
36
1.18
1.357
48
0.99
1.152
60
0.87
0.993
72
0.80
0.863
84
0.71
0.753
96
0.65
0.657
108
0.62
0.573
120
0.58
0.498
132
0.55
0.430
144
0.52
0.368
Table 5. CPR Data for Al – 4.5% Sn in 0.5 M HCl Envi-
ronment.
Time (Hrs) Experimental CPR
(mm/yr) Modeled CPR (mm/yr)
12 3.12 2.549
24 1.66 1.890
36 1.18 1.505
48
0.94
1.231
60
0.79
1.019
72
0.73
0.846
84
0.64
0.699
96
0.60
0.573
108
0.54
0.461
120
0.5
0.360
132
0.46
0.270
144
0.43
0.187
Table 6. CPR Data for Al – 4.5% Sn in 1.0 M HCl Envi-
ronment.
Time (Hrs) Experimental CPR
(mm/yr) Modeled CPR (mm/yr)
12 3.33 2.724
24
1.77
2.012
36 1.25 1.596
48
0.99
1.30
60 0.85 1.071
72
0.75
0.884
84 0.65 0.725
96
0.6
0.588
108 0.55 0.467
120
0.51
0.359
132 0.47 0.261
144
0.44
0.172
penetration rate in the whole environments is accounted
for by the corresponding variation in the exposure time.
The remaining 7.36% may be due to alloy composition
and other factors not incorporated in the model equations.
This is overwhelmingly significant and it further con-
firms that the developed model equations will be a good

New Trends in Corrosion Analysis of Al-Sn Alloy Duplex System
35
Table 7. CPR Data for Al – 4.5% Sn in 0.5 M NaCl Envi-
ronment.
Time (Hrs)
Experimental CPR
(mm/yr)
Modeled CPR
(mm/yr)
12
3.95
3.941
24
3.02
3.074
36
2.77
2.568
48
2.18
2.208
60
1.87
1.929
72
1.63
1.702
84
1.43
1.509
96
1.27
1.342
108
1.16
1.195
120
1.08
1.063
132
1.00
0.944
144
0.95
0.835
Table 8. CPR Data for Al – 4.5% Sn in 0.5 M NaCl Envi-
ronment.
Time (Hrs)
Experimental CPR
(mm/yr)
Modeled CPR
(mm/yr)
12
3.53
3.483
24
2.91
2.656
36
2.15
2.173
48
1.66
1.83
60
1.37
1.564
72
1.18
1.346
84
1.04
1.162
96
0.94
1.003
108
0.86
0.863
120
0.79
0.737
132
0.78
0.623
144
0.75
0.520
Table 9. The Modeled Corrosion Parameters for the Various Al-Sn Alloys in Different Media Concentration.
MEDIA CONCEN-
TRATION
COEFFICIENT OF CORRELA-
TION (R - VALUES)
COEFFICIENT OF DETERMINATION
(R
2
- VALUES)
MODEL EQUATIO NS
Al – 2.5% Sn IN VARIOUS CONCE NTRATIONS OF HCl
0.5 M 0.94935 0.90126 3.299919 0.618580CPR In
τ = −
1.0 M 0.97840 0.95726
5.014043 0.885003CPR In τ = −
Al – 4.5% Sn IN VARIOUS CONCE NTRATIONS OF HCl
0.5 M 0.93906 0.88183 4.910416 0.950385CPR In τ = −
1.0 M 0.94087 0.88524
5.275761 1.026965CPR In τ = −
Al – 2.5% Sn IN VARIOUS CONCENTRATIONS OF NaCl
0.5 M 0.95146 0.90527 5.757208 1.028719CPR In
τ = −
1.0 M 0.95772 0.91722
3.912334 0.713126CPR In τ = −
Al – 4.5% Sn IN VARIOUS CONCE NTRATIONS OF NaCl
0.5 M 0.99582 0.99165 7.045895 1.249661CPR In
τ = −
1.0 M 0.98574 0.97168
6.445930 1.192448CPR In τ = −
predictor of the corrosion trend in the various duplex
Al-Sn alloy systems being investigated.
3. Conclusion
The statistical analysis of the corrosion behaviour of
Al-Sn duplex alloy system has been investigated. It can
be observed that the modeled values of CPR obtained
from our model equations correlates well with the expe-
rimental data. This is attributed to the nearly p erfect pos-
itive coefficient of correlation that obtained in the analy-
sis which is consistent for all the alloy compositions.
REFERENCES
[1] C. E. Ekuma, “Effects of Zinc Addition on the Corrosion
Susceptibility of Al-Alloys in Selected Media Concentra-
tion,” B.Sc. Project, EBSU, Nigeria, 2006.
[2] R. E. Ul ick, “The Corrosion and Oxidation of Metals, 2nd
Supplementary Vol. (Special Edition),” Prentice Press,
New York, 1976.
[3] J. C. Zoccola, H. E. Townsed, A. R. Borzillo and J. B.
Horton, “Atmosphere Corrosion Behaviour of Alumi-
nium-Zinc Alloy Coated Steel,” STP 646, American So-
ciety for Testing and Materials, Philadelphia, 1978, pp.
165-184.
[4] S. H. Zhang and S. B. Lyon, “The Electrochemistry of
Iron, Zinc and Copper in Thin Layer Electrolytes,” Cor-
rosion Science, Vol. 35, No. 1-4, 1992, pp. 713-718.
doi:10.1016/0010-938X(93)90207-W
[5] N. E. Idenyi, S. I. Neif e and U. Uzor, “The Corrosion
Behaviour of Recrystalized Mild Steel in Various Tetrao-
xosulphate (vi) Acid [H2SO4] Concentrations,” Journal of
Corrosion Science and Technology, Vol. 1, No. 1, 2004,
pp. 54-55.
[6] N. E. Idenyi and S. I. Neife, “The Corrosion of Alumi-
nuim Base Alloys in Caustic Soda Media,” Journal of
Corrosion Science and Technology, Vol. 1, 2005, p. 10.
[7] C. E. Ekuma, N. E. Idenyi and A. E. Umahi, “The Effects
of Zinc Additions on the Corrosion Susceptibility of Alu-
minium Alloys in Various Tetraoxosulphate (VI) Acid
Environments,” Information Technology Journal, Vol. 7,
No. 2, 2007, pp. 237-241.
[8] C. E. Ekuma, N. E. Idenyi, F. K. Onwu and A. E. Umahi,
“The Influence of Media Concentrations on the Passiva-
tion Layer Characteristics of Al-Zn Alloys in Brine Envi-
ronment,” A sian Journal of Scienc e Research, Vol. 1, No.

New Trends in Corrosion Analysis of Al-Sn Alloy Duplex System
36
2, 2007, pp. 113-121.
doi:10.3923/ajsr.2008.113.121
[9] C. E. Ekuma, N. E. Idenyi and C. D. Ossi, “The Effects of
Manganese Addition on the Corrosion Susceptibility of
Cast Al-Alloys in Acidic Environment,” Trends in Ap-
plied Science Research, Vol. 2, No. 2, 2007, pp. 170-174.
[10] F. E. Goodwin, “Mechanism of Corrosion of Zinc and
Zinc -5% Aluminum Steel Sheet Coatings, in Zinc-Based
Steel Coatings Systems: Metallurgy and Performance,” In:
G. Krauss and D. K. Matlock, Eds., The Minerals Metals
Materials Society, Warrendale, Pennsylvania, 1990, pp.
183-193.
[11] A. I. Onuchukwu, “The Trend of Chemical Induced Cor-
rosion,” Journal of Corrosion Science and Technology,
Vol. 2, 2004, p. 138.
[12] N. E. Idenyi, C. E. Ekuma and I. O. Owate, “The Influ-
ence of Alloy Composition on the Passivation Layer
Characteristics of Al-Zn Alloys Syst ems,” Proceedings of
Materials Science and Technology 2006 Meeting and Ex-
hibition, Cincinnati, 15-19 October 2006.
[13] K. Nisancioglu, “Corrosion of Aluminium Alloys,” Pro-
ceedings of ICAA3, Trondheim, NTH and SINTEF, Vol. 3,
1992, pp. 239-259.
[14] C. E. Ekuma and N. E. Idenyi, “The Inhibition Characte-
ristic of Brine on the Corrosion Susceptibility of Al-Zn
Alloy Systems,” Journal of Applied Sciences, Vol. 6, No.
8, 2006, pp. 1751-1755. doi:10.3923/jas.2006.1751.1755
[15] N. E. Idenyi, C. E. Ekuma, J. B. Idenyi and A. E. Umahi,
“The Analysis of the Effect of Manganese Addition on
the Corrosion Susceptibility of Al-Mn Alloys in H2SO4
Environments,” Proceedings of 29th Annual International
Conference of Chemical Society of Nigeria (CSN), Ikeja,
25-29 September 2006.
[16] C. E. Ekuma and N. E. Idenyi, “Statistical Analysis of the
Influence of Environment on Prediction of Corrosion
from its Parameters,” Research Journal of Physics, Vol. 1,
No. 1, 2007, pp. 27-34.
[17] N. E. Idenyi, S. I. Neife and S. O. Agha, “The Effects of
Tin Addition on the Corrosion Susceptibility of
Al-Alloys,” International Journal of Science Laboratory
Tech- nology (JOSCILT), Vol. 2, No. 2, 2004, pp. 23-27.