International Journal of Materials Lifetime
ISSN (Print): ISSN Pending ISSN (Online): ISSN Pending Website: http://www.sciepub.com/journal/ijml Editor-in-chief: Apply for this position
Open Access
Journal Browser
Go
International Journal of Materials Lifetime. 2015, 2(1), 22-29
DOI: 10.12691/ijml-2-1-4
Open AccessArticle

Implicit Analysis of Al-Mn Alloy Corrosion Rate Dependence on Its Pre-Installed Weight and Exposure Time in Atmosphere Environment

C. Nwoye1, , E. O. Obidiegwu2 and N. E. Idenyi3

1Department of Metallurgical and Materials Engineering, Nnamdi Azikiwe University, Awka, Nigeria

2Department of Metallurgical and Materials Engineering, University of Lagos, Akoka, Nigeria

3Department of Industrial Physics, Ebonyi State University, Abakiliki, Nigeria

Pub. Date: June 17, 2015

Cite this paper:
C. Nwoye, E. O. Obidiegwu and N. E. Idenyi. Implicit Analysis of Al-Mn Alloy Corrosion Rate Dependence on Its Pre-Installed Weight and Exposure Time in Atmosphere Environment. International Journal of Materials Lifetime. 2015; 2(1):22-29. doi: 10.12691/ijml-2-1-4

Abstract

An implicit analysis of the Al-Mn corrosion rate dependence on the pre-installed alloy weight and exposure time in atmosphere environment was carried out. Surface structural analysis of corroded and uncorroded Al-Mn alloys were carried out to evaluate the grain boundary morphology. The response coefficient of the alloy corrosion rate to the combined influence of pre-installed alloy weight ϑ and exposure time ɤ was evaluated to ascertain the viability and reliability of the highlighted dependence. Surface structural analysis of the corroded alloy revealed in all cases widely distributed oxide film of the alloy in whitish form. A two-factorial empirical model was derived, validated and used for the analysis and evaluation. The validity of the model; ζ = Log -1(2.4908 (ϑ/ɤ) - 4.3059 (ϑ/ɤ)2 - 2.5941) was rooted on the core model expression Log ζ + 2.5941 = 2.4908 (ϑ/ɤ) - 4.3059 (ϑ/ɤ)2 where both sides of the expression are correspondingly approximately equal. Results generated using regression model showed trend of data point distribution similar to those from experiment and derived model. Evaluations from generated results indicated that the corrosion penetration depth as obtained from experiment, derived model & regression model were 1.394 x 10-5, 1.886 x 10-5 & 1.394 x 10-5 mm respectively. Standard errors incurred in predicting the corrosion rate for each value of the pre-installed alloy weight and exposure time considered as obtained from experiment, derived model & regression model were 8.21 x 10-5, 9.91 x 10-5 & 2.02 x 10-5 % and 5.46 x 10-5, 1.4 x 10-4 & 5.18 x 10-5 % respectively. Deviational analysis indicates that the maximum deviation of model-predicted corrosion rate from the experimental results is less than 19%. This translated into over 80% operational confidence and response level for the derived model as well as over 0.8 response coefficient of corrosion rate to the collective operational contributions of pre-installed alloy weight and exposure time in the atmosphere environment.

Keywords:
implicit analysis corrosion rate dependence pre-installed weight exposure time atmosphere environment

Creative CommonsThis work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

References:

[1]  Callister Jr, W. D. (2007). Materials Science and Engineering, 7th Edition, John Wiley & Sons Inc., USA.
 
[2]  Ekuma, C. E., and Idenyi, N. E. (2007). Statistical Analysis of the influence of Environment on Prediction of Corrosion from its Parameters. Res. J. Phy., USA, 1(1):27-34.
 
[3]  Stratmann, S. G., and Strekcel, H. (1990). On the Atmospheric Corrosion of Metals which are Covered with Thin Electrolyte Layers. II. Experimental Results. Corros. Sci.., 30:697-714.
 
[4]  Polmear, I., J. (1981). Light Alloys. Edward Arnold Publishers Ltd.
 
[5]  Ekuma, C. E., Idenyi, N. E., and Umahi, A. E. (2007). The Effects of Zinc Addition on the Corrosion Susceptibility of Aluminium Alloys in Various Tetraoxosulphate (vi) Acid Environments. J. of Appl. Sci., 7(2):237-241.
 
[6]  Nwoye, C. I., Idenyi, N. E., and Odo, J. U. (2012). Predictability of Corrosion Rates of Aluminum Manganese Alloys Based on Initial Weights and Exposure Time in the Atmosphere, Nigerian Journal of Materials Science and Engineering, 3(1):8-14.
 
[7]  Nwoye, C. I., Idenyi, N. E., Asuke, A. and Ameh, E. M. (2013). Open System Assessment of Corrosion Rate of Aluminum-Manganese Alloy in Sea Water Environment Based on Exposure Time and Alloy Weight Loss. J. Mater. Environ. Sci., 4(6): 943-952.
 
[8]  Nwoye, C. I., Neife, S., Ameh, E. M., Nwobasi, A. and Idenyi, N. E. (2013). Predictability of Al-Mn Alloy Exposure Time Based on Its As-Cast Weight and Corrosion Rate in Sea Water Environment. Journal of Minerals and Materials Characterization and Engineering, 1:307-314.
 
[9]  Nwoye, C. I. (2008). C-NIKBRAN Data Analytical Memory (Software).