Modeling the Performance of Protective Coatings in Marine Service J. Peter Ault and James A. Ellor Elzly Technology Company Abstract Despite improvement in organic coating technology, accurate prediction of service life continues to be problematic. Several models have been developed by various authors who describe the failure of protective coatings. Generally these models describe ionic migration to the coating/substrate interface or describe the breakdown of the polymer chains resulting in gloss and color changes. This paper discusses these models relative to various marine coating service environments. Relationships between the typically scientific models and more practical field coating observations will be drawn. Finally, the paper discusses the prospect for simulating or predicting coating performance. Coating Degradation Model Modeling the performance of protective coatings requires an understanding of how a coating provides corrosion protection and how the protection may be compromised. Over the past several years, several research projects have added to our understanding of these mechanisms. J. Ellor et. al. 1 conducted research on polyamide-epoxy systems used by the Navy. Key conclusions include: Corrosion protection of the steel substrate is afforded by the ability of the organic coating to mitigate the permeation of ionic species across the organic layer. Literature discussed within this effort suggested that moisture permeates coatings rapidly. The coating tends to inhibit the passage of oxygen through the coating more so than moisture. Tracking of the resistance (or impedance) of the coating can indicate a minimal DFT needed to control ionic permeability of the coating. For the epoxy coatings studied, this critical thickness seemed to be about 12 mils. Salt contamination under a coating for immersion service will lead to corrosion of the substrate and coating adhesion loss. However in the absence of a film-breach or holiday, the steel corrosion rate will remain low. It also appeared that the salt under the coating lead to the depassivation of the steel, especially in the early stages of the coating exposure. In seawater immersion, salt contamination (via initial doping) in levels as high as 25 µg/cm 2 may contribute to the initial coating disbondment via osmotic pressure but cannot be responsible for the entire growth of large blisters. So while salt may 1 J.A. Ellor, K. Cramer, J. Repp, and R. Parks, Novel Methods for Evaluating Epoxy-Barrier Coatings for Seawater Service, presented at the 1997 PAC-RIM Corrosion Control Conference, Honolulu, Hawaii 1
contribute to the disbondment, other factors aid blister growth on the coating. This might be osmotic pressure generated via corrosion products or mechanical stresses. In freshwater or in condensate areas, the blistering can be more rapid and extensive due to the increased osmotic forces generated. T. Nguyen et. al. 2 summarize several key points in their work describing a model for coating deterioration in a neutral environment: Coating deterioration in areas adjacent to defects is directly related to local electrochemical phenomena. This includes active corrosion at the specific defect site and cathodic delamination of the coating in the surrounding areas. Any electrochemical or physical phenomena impacting process kinetics will affect the coating deterioration rate. The authors also suggest that coating degradation on apparently intact coatings follows similar principles, with the degradation occurring initially at a micro-fault in the applied coating. These micro-faults may be hydrophylic regions that coalesce over time as moisture permeates the coating. The application of the coating at increasing thicknesses seems to reduce the frequency with which such events take place. Following the development of pathways to the substrate, ionic diffusion (permeability) occurs through the coating. The diffusion of ionic species to the steel substrate promotes more rapid local corrosion on the steel substrate. Corrosion then proceeds as is the case for a coating with an initial defect. Anodic processes tend to occur at the central holiday. The authors suggest that the key anodic reactions are: These reactions lead eventually to local hydrolysis reactions, the accumulation of chlorides at the defect, and a lowering of the local ph. The key cathodic reaction is the reduction of oxygen under the coating in the area surrounding the defect. This reaction creates local alkaline conditions that support further delamination of the coating in the area around the defect. This reaction rate is controlled by the diffusion of Na + ions along the steel/coating interface that are 2 T. Nguyen, J. Hubbard, & J. Pommersheim, Unified Model for the Degradation of Organic coatings on Steel in a Neutral Environment, Journal of Coatings Technology, Volume 68, Number 855, pp. 45-56 2
needed for charge balance with the hydroxyl (OH - ) ions generated from the reduction of the oxygen. Stratmann 3 echos many of these same issues in introducing the topic of deterioration at the coating-substrate interface. He summarizes: The polymer is separated from the underlying metallic substrate by a thin but dense oxide layer. Small molecules like water or oxygen molecules will easily penetrate through the coating The situation changes in the presence of defects that penetrate the coating, such as cut edges, scratches, or damage induced by stone impact. Then ions will diffuse along the interface and this will result in the build-up of an electrified interface between the oxide and the polymer. Now electrochemical reactions may happen at the buried interface and it is to be expected that these reactions differ strongly in their kinetics from the reactions at the defect itself, the latter being characterized by conventional corrosion kinetics. Scientific understanding mainly has to deal with the transport phenomena of ions and water along the interface, with the structure and the properties of the electrified interface, and with the kinetics of electron- and ion-transfer reactions at the interface. Key Factors of Degradation Models In a general summary of the various studies on coating degradation models, there appear two factors that are of primary importance: Factor I Effective Barrier Properties The research and models suggest that barrier coatings function as long as they are applied at a sufficient film thickness and that this coating film integrity is maintained. For example, disbonded organic coatings of sufficient film thickness will provide corrosion control until the film is breached. Coatings of insufficient dry film thickness will not serve as adequate barriers to ionic permeability and will not restrict coating breakdown. Coatings will also not function well if they contain holidays. The models also suggest that all coatings, even those of apparent sufficient dry film thickness may have weak areas within the coatings that may over time coalesce and serve as break-down sites. Such film properties appear to be impacted by the number of coats used to achieve a particular thickness as well as any factors that may impact the proper cure of the coating material. Coating film properties can also be affected by deformation (e.g., blistering) of the coating. The literature shows that such deformation increases ionic permeability in the deformed area. 3 M. Stratmann, Corrosion Stability of Polymer-Coated Metals-New Concepts Based on Fundamental Understanding, Corrosion, Vol. 61, No. 12, December 2005. 3
Factor II Electrochemical Reaction Rates at Coating Defects Failure propagation at a coating defect is related to the kinetics controlling the corrosion reactions at the substrate. Anodic reactions occurring at the defect site are counterbalanced by cathodic reactions occurring underneath the coating. The rate of these reactions is primarily controlled by diffusion and charge balance migration underneath the coating. Surface salts under the coating will have an impact on the electrochemical reaction rates. The impact of surface salts is at least three-fold: To promote rapid moisture penetration through a coating via osmotic principles and thus lead to the local disbondment of the coating. To increase the ionic conductivity of the moisture pathways under a coating. This factor aids in the mass transfer/charge balance relations needed to enhance the coating failure rate. To create local disbondment of the coating which may lead to the formation of blisters on the coating surface. The effects of soluble salt contamination are more severe in conjunction with an inadequate DFT or in the presence of a large number of coating holidays. As discussed above, the blisters can impact the local permeability of the coating; they may also create sites with a higher propensity for mechanical damage creating film holidays. Other items such as non-soluble surface contaminants ( dust ) or lack of surface profile can act to interfere with achieving or maintaining an adequate coating wet-adhesion. This can increase the dimensions and extent of moisture pathways under the coating and foster film undercutting. Development of an Analytical Simulation Model Service Life vs. Ranking It is common practice to evaluate coatings by rating them versus pictorial standards. This process provides a numerical rating for the coating condition at a given point in time. An average rating value may be useful in making generalizations concerning relative coating performance at the time of inspection. However from an engineering standpoint, describing the lifetime afforded by the alternative materials is of more interest. Furthermore, experience tells us that a coating does not all fail at the same time. Depending on the situation, the minimum or maximum time to failure may be of more interest than the average service life. Lifetime analysis of engineering data is typically modeled using exponential, Weibull, or gamma distributions, to name but a few of the common models. Data are gathered concerning the time to failure for specific trials. Failure can be defined using various measured parameters. In medical analysis of a drug treatment, failure may be defined as 4
death of the patient. In our corrosion analysis, failure may be defined by a critical parameter of interest, such as cumulative corrosion rust-through, blistering, cutback or deviation in color/gloss. To conduct service life analyses, data must be collected such that it allows observation of the time-to-failure rather than the relative condition at a point-intime. Through statistical data analysis, one can model the expected performance of a larger population as compared to the test sample size. Obviously, a larger sample size will provide better correlation to a larger-world observation. To demonstrate the concept, we will use data from the literature. 4,5 Consider for example two coating systems: one is an organic zinc rich primer with an epoxy mid coat and urethane topcoat and the other is red lead. Both coatings are applied over carbon steel with an SSPC SP-3 surface preparation and are exposed in a corrosive environment. Coatings are periodically inspected and the panels are rated for rust through by ASTM D 610. Failure is defined as the time to reach a rating of 7 or about 0.5% rust-through. Observations are made for as long as 10 years. Following the data collection, the data are plotted as illustrated below: 12 10 Years to Failure 8 6 4 OZ/E/Uretane Red Lead 2 0 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Cum. Probability The x-axis is labeled cumulative probability. Each plotting position is defined by ordering the lifetime data in increasing value. The ordinate coordinate assigned to each observed lifetime is given by [i/(n+1)], where i = data number and n= total number of data. For the zinc based system there are 6 data points, so each successive lifetime is plotted at (1/(6+1), 4 Environmentally Acceptable Materials for the Corrosion Protection of Steel Bridges, Federal Highway Administration Report FHWA RD 96-058, January 1997. 5 Performance of Alternate Coatings in the Environment (PACE), Federal Highway Report FHWA-RD-89-127, June 1989. 5
(2/(6+1), (3/(6+1), etc. There are 94 red lead panels, so these data are plotted at (1/(94+1)), (2/(94+1)), (3/(94+1)), etc. Interestingly, the mode of each data set (0.5 cumulative probability) is fairly similar. One would be expected say that the average life of the red lead system is just over 4 years and just under 5 years for the organic zinc based system. Thus one might conclude there is little difference in material performance. However, the plot also provides considerably more information. If one asks what percentage of the population will have failed at 6 years, about 70% of the zinc rich system area would be expected to have exceeding 0.5% rust-through versus over 90% for the red lead system. The data also suggest that performance at times less than 4 years may be fairly similar. Thus the distribution plot provides substantially more information about these systems that a simple average of the lifetime data. For many structures where coating breakdown results initially in aesthetic concerns vs. immediate structural concerns, there is little point to pay for increased coating performance unless one is seeking the highest levels of aesthetic value. An ASTM D 610 rating of "7" ostensibly corresponds to a breakdown of 0.3% of the coating. Is one really going to repaint an entire significant structure that has 99.7% of the paint remaining? Conversely, for structures where structural corrosion in the absence of a coating is a critical issue, small differences in the propensity for initial coating breakdown can have a substantial difference in impact to the structure. For the purpose of discussion, consider seawater ballast tanks or chemical tank linings and suppose that a coating applied performs (mathematically) similar to the red lead coatings discussed above. If we ran the exposure test for a 2 year period, we would find that only 10 of 94 sample panels had "failed." This might be considered good performance on average, but our concern is not with the average data. Our concern is that nominally 10% of the population has failed. Each of these areas will become an initial site for corrosion attack. Given a high enough corrosion rate, one may expect a corrosion penetration (i.e., leak) in a tank with a coating in very good condition. A statistical approach to modeling coating lifetime provides a basis for predicting early-life and late-life failure extremes; items which are often of most interest to the designer. In our modeling, we could use the different failure propensities or failure distributions of the data to show the net result on a prospective structure. If the corrosion rate of the underlying structure in the environment of interest is known, we can also predict the cumulative corrosion damage in this area. Coating Deterioration Rate Most often in inspection processes, data are obtained at different time intervals. Yet while these data are tabulated, there is usually little analysis of the time-rate of coating deterioration. The average value from the last observations is typically used to compare the different coating systems. 6
Of particular concern are situations where the deterioration rate of alternative coating systems is not the same at the final inspection. The "better" coating system (assuming that neither has yet "failed") may be that system which is showing the slowest rate of continued decay. The following depicts the failure rate, as rust through, of an epoxy-mastic/urethane coating applied over SP-2 or SP-3 surface preparations and exposed within a marine environment. 6 Based on the general rust through observed at 36 months, the ASTM D 610 ratings would be 6.2 for the six SP-3 areas and 6.5 for the six SP-2 areas. This appears as a small difference. (Often the pseudo-logarithmic nature of the D 610 rating system is forgotten.) % Rust Thru 1.2 1 0.8 0.6 0.4 0.2 0 y = 0.0046e 0.1507x R 2 = 0.8786 SP-2 SP-3 0 20 40 y = 0.002e 0.1479x Months R 2 = 0.727 But looking at the regression data for the observed data, one can see that the future trends predicted for the two levels of surface preparation are very different indeed. The SP-2 panel data suggests that its ongoing rate of deterioration is substantially more than that of the SP-3 panels. This sort of additional analysis is lost in most ranking schemes, despite the fact that deterioration vs. time data are collected. The regression data can also help forecast the predicted lifetime for alternative coating systems that have not yet exhibited failure. This lifetime data can be used in conjunction with the statistical service life models discussed above to derive a probable failure distribution for coatings applied to a real life structure. Thus we may be able to complete lifetime models based on shorter exposure periods. Visual Data Representation 6 Environmentally Acceptable Materials for the Corrosion Protection of Steel Bridges, Federal Highway Administration Report FHWA RD 96-058, January 1997. 7
Proper treatment of the data allows for the development of a simulated visual model for the failure distributions predicted. Often recoat decisions are based on aesthetic considerations as opposed to engineering considerations. And most often the structure under consideration is not at a uniform ASTM D 610 rating, an often used "failure" rating. Much like computer-based visual aids are currently available to help an owner select structure color on aesthetic concerns, we believe that representations of the probable corrosion breakdown (or any other form of coating deterioration) imposed on a structure would aid an owner in making an informed decision on coating selection. The following shows a simplistic visualization of the application of the red-lead breakdown distribution vs. time applied to a "box structure." Based on the lifetime model shown in the above-data, the cumulative coating failure is displayed on a simulated box structure. It is a simplification of complex phenomena into a visual model that may assist in assessing the significance of different life distributions for a coating applied to a real life structure. For those charged with making coating decisions, it can aid in translating rating data or breakdown data into a "physical" reality. We believe this visualization process will aid the owner in interpreting numerical breakdown values, which are abstractions of the real structure. Obviously in a real model, the visual display would reflect the structure of interest (i.e., a bridge, ballast tank, etc.). 8
Summary This paper has presented various related ides regarding the effective modeling of coating failure. To further the concepts presented herein, it is necessary to utilize testing methods which provide data better suited to service life modeling. Some key concepts to embrace when developing data for service life prediction include: Applying paint exactly as the manufacturer or specification tells you to do will, indeed, make the paint last a very long time. This does not always correlate with real life experience because not all of the paint is applied as instructed. Variances from ideal application are the weak link with respect to attaining target service life. We must quantitatively understand the effects of these variances as well as their likelihood to effectively predict service life. Once we understand the effects of and likelihood of certain defects, we can estimate the percentage of the coating that will fail as a function of time. Like most lifetime analysis, we expect a distribution such as a Weibull distribution. With the proper models, we can develop visual tools that help people "visualize" the growth coating deterioration on a structure (such as a tank). This will help technical and non-technical people better understand the consequences of various maintenance strategies. 9