Corrosion Resistance Characterization of Coating Systems Used to Protect Aluminum Alloys Using

195 Pages·2010·2.22 MB·English

by  Graciela I Bosisio

Corrosion Resistance Characterization of Coating Systems Used to
Protect Aluminum Alloys Using Electrochemical Impedance
Spectroscopy and Artificial Neural Networks

A Dissertation
Presented in Partial Fulfillment of the Requirements for
the Degree Doctor of Philosophy in the
Graduate School of The Ohio State University

By
Federico Gambina
Graduate Program in Materials 
Science and Engineering
*****
The Ohio State University
2010

Dissertation Examination Committee
Professor Rudy Buchheit
Professor Gerald Frankel
Professor Morris Patricia

ABSTRACT
In this study, the corrosion protection provided by of a number of chromate and
chromate-free coatings systems was characterized in detail. High-solids SrCrO -
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pigmented epoxy primers applied to 2024 and 7075 substrates were subject to salt spray
exposure testing for 30 days. Samples were removed periodically and an electrochemical
impedance measurement (EIS) was made. Although none of the coatings tested showed
visual evidence of corrosion, the total impedance of the samples decreased by as much as
two orders of magnitude. An analysis of capacitance showed that the primer coatings
rapidly took up water from the exposure environment, but the coating-metal remained
passive despite the fact that it was wet. These results support the idea that chromate
coatings protect by creating a chromate-rich electrolyte within the coating that is
passivating to the underlying metal substrate. They also suggest that indications of metal
substrate passivity found in the low-frequency capacitive reactance of the impedance
spectra are a better indicator of corrosion protection than the total impedance.
The low-frequency capacitive reactance from EIS measurements is also good at
assessing the protectiveness of chromate-free coatings systems. Fifteen different coatings
systems comprising high-solids, chromate-free primers and chromate-free conversion
coatings were applied to 2024 and 7075 substrates. These coatings were subject to salt
spray exposure and EIS measurements. All coatings were inferior to coating systems
containing chromate, but changes in the capacitive reactance measured in EIS was shown
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to anticipate visual indications of coating failure. A predictive model based on neural
networks was trained to recognize the pattern in the capacitive reactance in impedance
spectra measured after 48 hours of exposure and make an estimate of remaining coating
life. A sensitivity analysis was performed to prune the impedance inputs. As a result of
this analysis, a very simple but highly predictive model was constructed that used low-
frequency phase angle information extracted directly from EIS measurements to predict
time to failure in salt spray up to 30 days of exposure.
The exposure and EIS characterization of the chromate-free coatings systems
enabled a ranking of the coatings systems in terms of corrosion protection provided.
Coating systems were ranked according to several different methods described in the
literature. Among the coatings evaluated, Deft 02GN084, a high solids, solvent-borne and
Pr-containing primer coating showed best protection when used in conjunction with a
number of different conversion coatings and surface pretreatments. Several different
trivalent chromium conversion coatings and pretreatment were used. This general type of
conversion coating appeared to provide better corrosion protection than other
pretreatments whose functions were primarily surface cleaning or adhesion promotion.

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Dedicated to Emily.
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ACKNOWLEDGMENTS
I would like to thank my advisor, Dr. Rudy Buchheit, who accepted me for my
final undergraduate project, encouraged me to apply for graduate school and patiently
guided me throughout the process to ultimately complete my dissertation. I also would
like to thank Dr. Jerry Frankel and Dr. Mariano Iannuzzi, whose interactions between my
undergraduate school and the Fontana Corrosion Center allowed me to meet my advisor.
Further, I would like to thank Dr. Ricardo Carranza who pointed me in the direction of
corrosion studies while finishing my undergraduate studies.
I also wish to acknowledge those who have sponsored portions of my work,
namely, the Science and Technology Program of the Office of the Chief Scientist (OCS),
Office of Civilian Radioactive Waste Management (OCRWM), U.S. Department of
Energy (DOE) and the support of the Strategic Environmental Research and
Development Program (SERDP).
I would like to thank Dr. Girdhari Kumar and Dr. Mary Katherine Cavanaugh 
for their assistance, particularly with ANN and fuzzy curves, also Abraham Palomec-
Garfias for his assistance with sample preparation, exposure and EIS data acquisition and 
Nasser Lopez for his support with computer data handling.
Finally, I wish to thank and acknowledge the past and present members of the
FCC group who contributed to, and assisted me in finishing, this work (Dr. Belinda
Hurley Dr. Nick Birbilis, Dr. Kevin Ralston, Mariano Kappes, Leonel Chiacchiarelli,
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Brendy Ricon-Troconis, Omar Lopez-Garrity), and also the support of my aunt, Victoria
Gullickson, and family for their patience and assistance with this work.

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VITA
2004………………………………………....B.S. (Materials Science and Engineering), 
The Ohio State University, Columbus, OH 
2007………………………………………....M.S. 
The Ohio State University, Columbus, OH 
2007 ‐ Present……………………………….Ph.D. 
The Ohio State University, Columbus, OH 

FIELDS OF STUDY

Major Fields: Materials Science and Engineering 
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TABLE OF CONTENTS 
ABSTRACT .............................................................................................................................ii
ACKNOWLEDGMENTS.............................................................................................................................v
VITA ...........................................................................................................................vii
CHAPTER 1: INTRODUCTION..............................................................................................1
CHAPTER 2: LITERATURE REVIEW..................................................................................5
2.1 Introduction...................................................................................................................5
2.2 Coating system definition...............................................................................................7
2.2.1 Chromate compounds.....................................................................................................8
2.2.2 Alternatives to chromate.................................................................................................9
2.3 EIS technique...............................................................................................................14
2.3.1 Validation of EIS data..................................................................................................15
2.3.2 Equivalent Circuit Modeling........................................................................................17
2.3.3 Coating characterization by EIS...................................................................................19
2.4 Artificial neural networks............................................................................................25
2.4.1 ANN architecture..........................................................................................................26
2.4.2 Training and validation.................................................................................................28
2.4.3 ANN models as a prediction tools................................................................................29
2.5 Unresolved issues.........................................................................................................31
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CHAPTER 3: CHARACTERIZATION OF CR-CONTAINING COATINGS BY
ELECTROCHEMICAL IMPEDANCE SPECTROSCOPY..................................................................52
3.1 Introduction.................................................................................................................52
3.2 Experimental procedures.............................................................................................56
3.2.1 Alloys and coating chemistry.......................................................................................56
3.2.2 EIS measurements........................................................................................................57
3.2.3 Water uptake.................................................................................................................59
3.3 Results..........................................................................................................................60
3.3.1 EIS................................................................................................................................60
3.3.2 Water uptake.................................................................................................................63
3.4 Discussion....................................................................................................................64
3.4.1 Influence of the conversion coating in the corrosion mechanism.................................66
3.5 Conclusions..................................................................................................................67
CHAPTER 4: EIS STUDY TO PREDICT LONG-TERM PERFORMANCE OF CR-
FREE COATINGS ON ALUMINUM ALLOYS USING EIS AND ANN.............................................90
4.1 Introduction.................................................................................................................90
4.2 Experimental................................................................................................................94
4.2.1 Coating system description...........................................................................................94
4.2.2 Time to failure variable (TTF)......................................................................................96
4.2.3 EIS................................................................................................................................98
4.2.4 Data pre-processing......................................................................................................99
4.2.5 Artificial neural networks.............................................................................................99
4.2.6 Sensitivity analysis.....................................................................................................101
4.3 Results........................................................................................................................102
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