Service Life Prediction of Organic Coatings - American Chemical Society

Chapter 24

Application of Failure Models for Predicting Weatherability in Automotive Coatings D. R. Bauer

Downloaded by AUBURN UNIV on December 27, 2017 | http://pubs.acs.org Publication Date: April 15, 1999 | doi: 10.1021/bk-1999-0722.ch024

Ford Motor Company, Research Laboratory MD#3182, P.O. Box 2053, Dearborn, MI 48121

In large part, paint and coatings determine the esthetic appeal of a vehicle. Long-term customer satisfaction with paint is determined by how well the paint protects the body and by how well the paint maintains its overall appearance. The average age of vehicles has been steadily increasing. Customers expect reasonable maintenance of paint appearance for 10 years or longer. Catastrophic changes in appearance (peeling, cracking, etc.) can cause significant loss of satisfaction with the entire vehicle. A critical goal of weatherability testing is to be able to predict the risk of catastrophic paint failure in service. Paint performance is a function of the intrinsic capability of the particular paint system and the environment into which it is placed. It is important to note that the in-service time-to-failure has to be described by a distribution function. To predict this distribution function, it is necessary to determine the distribution functions for both the environment harshness and the paint system capability. This paper describes how to develop paint failure models that are capable of predicting the distribution of in-service failure rates. By using mechanistic failure models, test time can be greatly reduced.

The ability to predict accurately the long-term weatherability performance of paints and coatings is essential for both the coatings industry and for those industries that coat their products. Failure to anticipate in-service failures leads to high warranty costs and dissatisfied customers. Over-engineering a paint system can lead to significantly higher cost that does not provide value to the customer. An "ideal" paint system is one that exceeds customer expectations for long-term performance at niinimal cost. Performance is typically measured in terms of specific engineering metrics (e.g., gloss retention). Customer expectations are much more subjective. Both customer expectation and paint system performance are functions of time. Given the

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© 1999 American Chemical Society Bauer and Martin; Service Life Prediction of Organic Coatings ACS Symposium Series; American Chemical Society: Washington, DC, 1999.


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difficulty of translating customer expectations into engineering metrics, the time-tofailure is usually defined as the time at which a particular performance characteristic drops below a specified value rather than by the time when performance drops below customer expectations. The shapes of the performance functions depend on the nature of the failure mode. For example, gloss loss in basecoat/clearcoats tends to be gradual unless some catastrophic failure occurs. As shown in Figure la, for gradual changes, long-term performance can be readily predicted from short-term measurements. Of course, in-service, there will be a distribution of slopes (determined by environmental load and other material and processing variables) that will lead to a distribution of times-to-failure. Distribution functions are critical to risk assessment since no commercially viable coating system ever fails 100% in-service. The application of distribution functions to interpret weathering data has been discussed (1,2). Other failure modes (delamination and cracking) tend to be abrupt. As is clear from Figure lb, short-term testing is not a good predictor of long-term performance. Again, the performance (time-to-failure) has to be described in terms of distribution functions of environmental load as well as material and process variables. Catastrophic failures are harder to anticipate, require longer exposure times, and often have a bigger impact on customer satisfaction. The long outdoor exposure times necessary to induce failure have caused an increased reliance on accelerated exposures. The main problem with the use of accelerated exposures is that they can change the balance of degradation and stabilization chemistries as well as the physics of failure. The effect can vary from coating to coating leading to acceleration factors that depend on the coating, on the failure mode, as well as on the test (3-6). Recent analytical studies have focused on developing chemical methodologies to better understand the factors that control catastrophic failure (7,8). In essence, this work involves translating those analytical studies into failure models whereby measurements of critical coating attributes and their distributions can be used to anticipate long-term in-service performance. This paper extends the analysis presented in a previous paper (2) hereafter referred to as I. In the Sections that follow, the development and application of different coating failure models are described. Development and Application of Failure Models In order to develop failure models, it is necessary to describe all possible failure modes of a basecoat-clearcoat paint system. Such failure modes that can be related to weathering induced photooxidation include gloss loss, color change, clearcoat cracking, delamination of clearcoat from the basecoat, and delamination of the basecoat or primerfroma photosensitive substrate. In principle, it is necessary to develop specific failure models for each of these failure modes. Before doing that, a general model that can be used to describe any failure mode controlled by photooxidation is presented. Photooxidation Distribution Function: A General Model for Coating Weathering Failures. As noted above, a failure model must include information about both the coating performance and the environmental harshness. In the absence

Bauer and Martin; Service Life Prediction of Organic Coatings ACS Symposium Series; American Chemical Society: Washington, DC, 1999.

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a

Time

b

Time Figure 1. Performance versus time. Changes can be either gradual (a) or abrupt (b). Abrupt changes tend to be more noticeable and more difficult to predictfromshort-term measurements.


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of specific mechanistic information concerning failure modes, it is still possible to account for the variation in environment. The time-to-failure, tf, can be written as the product of a coating constant, C, and an exposure variable, EX:

t = C


f

x

EX

(1)

Following the methodology used in I, EX is defined as the number of years of exposure on a particular vehicle that is equal to one year of exposure of a panel on a test rack in Florida. Thus, the value of C is the exposure time (years) in Florida required to fail the particular coating system. The value of EX is inversely related to the harshness of the exposure. EX will depend on the geographic location, customer parking habits, and on the specific dependence of coating photooxidation on environmental variables such as light intensity, temperature, humidity, etc. Based on mechanistic arguments and empirical data (9-12), it was proposed in I that the rate of coating photooxidation is proportional to accumulated light dose times an Arrenhius dependence on temperature (activation energy = ~7 kcal/mole). Seasonal intensity and mean daily high temperatures were used to calculate the relative rate of photooxidation at different geographical locations in the continental US (13). An arbitrary correction factor for humidity was included to correct for differences between prediction and observation of weathering in Arizona and Florida. The distribution function is determined by counting the number of vehicles that reside in locations with specific harshness. This distribution function (Figure 1 of I) assumes that vehicles are exposed 100% of the time (i.e., as if they were exposure panels). In fact, some vehicles are parked mostly outside while others are mostly parked in garages or parking structures. A "parking" distribution function was estimated and combined with the photooxidation distribution to estimate an "in-service vehicle" distribution function for EX in the continental US. This distribution function is shown in Figure 2. The distribution function is fairly broad. Around 90% of the vehicles accumulate the equivalent of one year exposure in Florida between 1.2-5 years. The distribution in Figure 2 can be recast to answer the following question: What is the % expected failure after 10 years in service for a paint system that fails after a particular number of years in Florida. This relationship is shown in Figure 3. The critical point of Figure 3 is that very long exposure times (~8 years) are required to reduce the in-service failure rate to below 5% after 10 years (a typical average customer expectation for paint life). A plot of % failure with time in service is shown in Figure 4 for a system that fails after 5 years in Florida. The onset of failure is about 6 years rising rapidly to a total failure rate of almost 40% after 10 years in service. Another critical point is that this analysis assumes that all paints of this technology will fail at exactly the same time in Florida, independent of material and process variability. This is highly unlikely. In the absence of a more detailed mechanistic failure models, evaluation of the effects of process and material variability on performance will require the exposure of a wide variety of samples prepared under all possible conditions. This, together with the very long exposure times required to ensure performance



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Years In-Service Exposure Equal to One Year Florida Exposure Figure 2. Cumulative distribution function for photooxidative harshness (see I for derivation). The abscissa is the number of years in service required to equal one year continuous exposure in Florida. The ordinate is the fraction of vehicles. For example, roughly 20% of the vehicles in service receive a exposure equal to a year in Florida in 1.6 years or less.



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0 1

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10 11 12

Time-to-Failure in Florida, Years Figure 3. Percent failure on vehicles at 10 YIS versus time-to-failure in Florida for a given paint system.



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8

9

10

Years-in-Service Figure 4. Percent failure in service versus time in service for a coating system that fails at 5 years in Florida.



385 against catastrophic failure, provides strong motivation to understand specific mechanisms of failure. In the previous paper, I, two different mechanistic failure models were developed based on different hypothetical failure modes. The purpose of that development was to illustrate how failure models might be developed and applied. The failure models were based on two limiting cases for UV light induced delamination. In the first case, it was assumed that light transmitted through a black pigmented coating resulted in degradation and ultimate delamination of a photosensitive substrate. This case was used to illustrate the potentially large sensitivity of failure rates to process variations. In this case, performance was related to film thickness in an exponential fashion. Under some test protocols, field failures would be observed long before samples failed in Florida. In such cases, it is usually necessary to develop materials or processes that avoid this sensitivity. In the second case, it was assumed that basecoat-clearcoat delamination could be caused by a slow loss of ultraviolet light absorber (UVA) that is added to clearcoats. Once the light absorber is lost, UV light induces degradation of the basecoat ultimately leading to delamination. A simple model was derived based on this hypothesis. The model was used to illustrate how rapid measurement of specific parameters could be used to predict long term performance. In the next section, this model is evaluated in more detail. As a result of further experimental work, the model has been modified to more correctly represent the actual failure mode. The predictions of the old and new model are compared for the purposes of discussing the precision required to make successful predictions. Model for Basecoat-Clearcoat Delamination. In the initial stages of the development of basecoat-clearcoat automotive paint systems, it was recognized that it is necessary to protect the basecoat from UV radiation (14). UVAs were added to clearcoats to achieve this protection. Samples where the UVA was deliberately left out exhibited delamination of the clearcoat from the basecoat after an exposure time rangingfrom2 to >4 years in Florida. UVAs were developed with excellent physical retention (no loss due to volatility or extraction). It was assumed that the UVAs were basically permanent in the coating. Over the past several years, the work of Pickett and Moore, Decker and Zahouily, and Gerlock et al. demonstrated that UVAs in fact were slowly consumed in polymer systems (7,15,16). The rate of consumption depended on the UVA , the polymer host and the exposure condition. The exact mechanism(s) of loss are still not completely understood. There appear to be two basic processes: attack on the excited state of the UVA by oxidation products generated in the polymer matrix; and a photolytic process that does not depend on free radicals produced by the polymer matrix (15,17). The quantum efficiency is very low (

Service Life Prediction of Organic Coatings - American Chemical Society

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