2013 Virtual Department of Defense Corrosion Conference September 16-17, 2013 NACE International

Visualization of the spread of Corrosion Damage

Joe H. Payer1, Carl Hotz2, Cameron Gatian2, Paul Young3, Anil Patnaik2 1Corrosion and Reliability Engineering Program

2Department of Civil Engineering 3Dept. Chemical and Biomolecular Engineering

The University of Akron Akron, OH44325

JPayer@uakron.edu

ABSTRACT

Better linkage is needed between corrosion knowledge and design, manufacture and operation of corrodible equipment and structures. The benefit is to make the corrosion knowledge accessible to engineers and decision makers for more effective corrosion management and mitigation programs. Visualization of the corrosion damage evolution is an effective tool to enhance design, materials selection, life prediction and performance assessment for corrosion mitigation and control. The objective here is to demonstrate pictorial representations of corrosion rates and damage evolution. The visualizations are based on corrosion data that are available in tabular, graphical, analytical or photographic form, and they represent this data in a visual-time-sequence format. The intent is to incorporate visualizations in design tools and software packages for analysis of corrosion mitigation. Visual feedback is presented on the effects of materials selection and environmental exposure conditions. So, the extent and spread of corrosion damage increases with increasing severity of the environment and decreases for more corrosion resistant materials systems. Several visualizations are presented that are relevant to military vehicles. Keywords: Corrosion, Visualization, Life Prediction, Performance Assessment, Damage Evolution

INTRODUCTION

This work addresses the need for more effective transfer of corrosion knowledge to decision makers in design, manufacture and operation of corrodible equipment and structures. Great strides are being made in development of models of corrosion degradation processes and computational tools for the implementation these models. The goal is to embed this knowledge in tool sets for design, materials selection and asset management, so that effective corrosion mitigation based on corrosion knowledge is accessible and decisions made on a sound technical basis. Visualizations of corrosion damage evolution provide feedback on the results of materials selection and environmental exposure conditions.

The objective here was to develop visualizations for the spread of corrosion. The visualizations

demonstrate the expected rate of corrosion spread to better inform the user for design and materials selection; for comparison of corrosion mitigation alternatives; and for education and training. Input for the visualizations can be corrosion data such as weight loss, penetration rate, creep from scribes on painted panels from laboratory or field exposures. In addition, information on corrosion damage can be used from vehicle test tracks, fleet studies and in-service studies. The use of both quantitative and photographic data is demonstrated.

This work was in collaboration with the development of the Accelerated Corrosion Expert System (ACES) [1,2]. ACES is a Simulation and Modeling System that will predict the initiation and growth of corrosion over time on Wheeled Vehicles, Aircraft, Ships and other Assets. The general concept is that 3-D CAD/CAE geometry, material, coating and auxiliary data, maintenance and operational profiles and environmental data are all provided as input to a combined physics-based, statistical and heuristically reasoning engine running on a Graphical Processing Unit (GPU) parallel processing system. The result is a time dependent simulation of the geometry deterioration due to coating breakdown and corrosion over time.

Animations of the spread of corrosion were developed to represent high-risk areas that are prone to corrosion. For example, high-risk areas on a vehicle are shown in Figure 1. To visualize corrosion spread from these areas, corrosion rate data were used to develop time sequences for the spread of corrosion. Auto CAD was utilized in the construction of the individual frames that make up the animation, and the animations of the spread of corrosion were created using the Graphics Interchange Format (GIF), a bitmap image format.

FIGURE 1-“High risk” areas prone to corrosion identified with ACES. (Source: T. Savell, GCAS, Inc.)

The project structure is shown below: I. Develop damage relationships for metal loss and creep as function of time

(a) Linear weight relationship based on a DoD vehicle coupon study

(b) Bilogarithmic weight loss relationship based on atmospheric corrosion exposures (c) Creep from scribe rate based on panels in atmospheric exposure

These baseline rates are scaled for mild, moderate and severe conditions. II. Develop sequences of the damage progression applying relationships above

(a) Time sequences of spread of damage from a boundary (lap joint or other feature), from a scribe in coated systems and from local coating breakdown and corrosion (b) Create animations using the Graphics Interchange Format (GIF)

III. Develop sequences of damage progression from photos of atmospheric tests, vehicle tests and fleet exams

(a) Series of photos demonstrating the damage evolution (b) Renditions derived from photos

The methodology for development of the animations is described, and then several cases that were treated and results are summarized. The cases include:

• Spread (area corroded) of Corrosion from Weight Loss Data • Area Growth from Penetration Rate Data • Localized Coatings Breakdown and Corrosion • Visualization using Photos of Scribes on Painted Panels

For each of these, screen shots are presented to illustrate the animation.

Development of Time Sequences using AutoCAD

The steps to develop a time sequence are demonstrated in Figure 2. Step (a), a reference frame is drawn with a scribe with the polyline command. This creates the baseline condition for the subsequent spread of corrosion. Step (b), copy the frame for desired number of years and number them. Step (c), with the polyline command draw lines with the same length as the radius of the corroded area for the corresponding year extending from the initial shape. Step (d), close the area and delete the guidelines. Step (e), after the area is closed the leading edges can be made “fuzzy”. Drawing a polyline does this.

(2a)

(2b)

(2c)

(2d)

(2e)

FIGURE 2-Steps (a) – (e) to develop a time sequence

A “rust” color/texture was given to the corrosion areas to give a more realistic look. A picture of

rust was imported into AutoCAD, shaped to the area, trimmed to the desired shape. Afterward, each time sequence was implemented into a GIF animation program. Once the GIF was created, the visualization with a “fuzzy” edge boundary was able to play through smoothly through all time sequences. A snapshot of one of the time sequences in the animation is shown in Figure 3.

FIGURE 3- A snapshot of length of corrosion spread vs. time from weight loss data

Spread (area corroded) of Corrosion from Weight Loss Data

It is common to express corrosion rates over time as mass loss data from coupon exposures in accelerated tests, atmospheric exposure sites and vehicle tests [3]. Several sources and representative data are presented in a study to develop a vehicle micro-environment index [3]. The mass loss data were used to calculate spread of corrosion by converting mass loss to a volume of metal loss. A characteristic shape for the volume of metal loss is defined as in Figure 4, and from this shape, a depth to width ratio is determined. For the shape of corrosion damage in Figure 4, a depth to width ratio of 1:10 is determined, and the depth to spread ratio from the center line is 1:5.

FIGURE 4- Evolution of corrosion with depth to width ratio of 1:10.

Figure 5 presents an example of the length of spread over time. A baseline case is shown along

with a more severe (20% greater) corrosion rate and a less severe (20% lesser corrosion rate. The former would represent a more severe environment, and the latter a less severe environment.

FIGURE 5- Evolution of corrosion – Length of spread vs. Time Graph for baseline, more severe and

less severe environmental conditions. The calculated length vs. time data were used to draw sequence steps from 1 year, 2 year, 4 year, 8 year and finally 20 years of exposure time. A sequence for corrosion spread from a linear boundary is shown below. The time period is 20 years based on corrosion data. An animation is created, and this represents a baseline condition. The spread of corrosion would be more rapid (speed up the animation) for more corrosive conditions or less corrosion resistant metal/coating systems.

FIGURE 6- sequence for corrosion spread from a linear boundary

The corrosion spread can be shown to grow from any initiation boundary. Figure 7 presents snapshots from a linear boundary and a spot location.

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FIGURE 7- (a) a frame from the spread of corrosion from a linear boundary

(b) a frame from areal growth from a point

Area Growth from Penetration Rate Data

The spread of corrosion damage can also be calculated from the corrosion penetration rate data. A case is presented here with penetration of corrosion following a linear bilogarithmic law [4]. This relationship is shown below, where p is penetration rate and t is time. The coefficients A and B are determined to fit the corrosion exposure data.

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The penetration of corrosion in micrometers was presented for steel in a marine environment [5]. For the coefficients, the value of A is 2.441 and the value for B is 1.49. The length of corrosion spread was calculated using the same corrosion shape factor as above, i.e. 1: 5 for penetration to width ratio. The results for spread over 20 years based on the bilogarithmic penetration are shown in Figure 8.

FIGURE 8-Our calculated length of spread of corrosion vs. time based a bilogarithmic penetration rate

for penetration for steel in a marine environment [5].

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Localized Coatings Breakdown and Corrosion

A GIF animation was created for localized coating breakdown at points and subsequent spread of corrosion from these sites. The corrosion propagates from isolated points on the painted surface, and the initiation sites increase with time. The spread from each point follows the growth relationship for spread as in the prior scenarios (see Figure 7). With the growth of the corroded areas, some spots meet and combine to create larger damaged areas. Figure 9 shows localized coating breakdown and corrosion on a military truck door. This pattern was the basis for the coating/corrosion animation, and snapshots at 4, 8 and 12 years are shown in Figure 10.

FIGURE 9- Localized Coatings Corrosion on Military Truck Door [photo courtesy of John Repp, Elzly Corp.]

FIGURE 10- Snapshots from the animation of coating breakdown and spread of corrosion damage

Visualization using Photos—Scribes on Painted Panels

A GIF animation was completed for the spread of corrosion from painted scribes. Figures for this visual came from the report “Corrosion Performance of Common Materials of Manufacture for Military Vehicles and Weapon Systems in the Hawaiian Islands” [6]. Two photos of a steel panel, one with exposure after 3 years and one with no exposure were taken from the report and used to create

the GIF animation. A group of intermediate photos was created to represent the full exposure period. Using Photoshop CS5 and starting with the photo with 3 years exposure, multiple photos were created by erasing some of the rust and matching the color of the clean panel to the non-corroded area. Technical judgment was used to create the time steps of damage in the sequence prior to the final worst case. Snap shots of early and later stages of corrosion from a scribe in a painted panel are shown in Figure 11.

FIGURE 11- Snapshots from animation of damage evolution from scribes in painted panels.

Visualization of Severity of Damage Evolution

From the baseline animation, the severity of damage evolution can be represented by the speed of the animation. The spread of corrosion would be more rapid (speed up the animation) for more corrosive conditions or less corrosion resistant metal/coating systems. The spread of corrosion would be slower (slow down the animation) for less corrosive conditions of more corrosion resistant metal/coating systems. For a given location on a vehicle, the environmental corrosivity and the material corrosion resistance are identified, and these determine the speed at which the animation is run. Snapshots of side-by-side animations of mild, moderate and severe cases are shown in Figure 12.

FIGURE 12- Snapshots from an animation for mild, moderate and severe corrosion damage evolution.

SUMMARY There is a great need to make corrosion knowledge more accessible to those carrying out design,

materials selection and asset management. The benefit is to make the corrosion knowledge accessible to engineers and decision makers for more effective corrosion management and mitigation programs. Visualization of the corrosion damage evolution is an effective tool to enhance design, materials selection, life prediction and performance assessment for corrosion mitigation and control. The objective here was to demonstrate pictorial representations of corrosion rates and damage evolution. The visualizations are based on corrosion data that are available in tabular, graphical, analytical or photographic form, and they represent this data in a visual-time-sequence format. The intent is to incorporate visualizations in design tools and software packages for analysis of corrosion mitigation. Visual feedback is presented on the effects of materials selection and environmental exposure conditions. So, the extent and spread of corrosion damage increases with increasing severity of the environment and decreases for more corrosion resistant materials systems. Several visualizations are presented that are relevant to military vehicles.

Visualizations were developed for the spread of corrosion. The visualizations demonstrate the expected rate of corrosion spread to better inform the user for design and materials selection; for comparison of corrosion mitigation alternatives; and for education and training. The methodology for development of the animations is described, and then several cases that were treated and results were summarized. The cases include:

• Spread (area corroded) of Corrosion from Weight Loss Data • Area Growth from Penetration Rate Data • Localized Coatings Breakdown and Corrosion • Visualization using Photos of Scribes on Painted Panels

Visual feedback is presented on the effects of materials selection and environmental exposure

conditions. So, the extent and spread of corrosion damage increases with increasing severity of the environment and decreases for more corrosion resistant materials systems. Several visualizations are presented that are relevant to military vehicles.

ACKNOWLEDGMENTS

This work is associated with the National Corrosion Center (NCERCAMP) at The University of Akron and the DoD Technical Corrosion Collaboration (TCC) supported by the U.S. Department of Defense Office of Corrosion Policy and Oversight. Students are affiliated with the Corrosion Squad, a multidisciplinary student organization at The University of Akron. The TTC research is administered by the US Air Force Academy under agreement number FA7000-10-2-0013.

C. Thomas Savell, GCAS Incorporated and Scott W Porter, US ARMY RDECOM-TARDEC Materials, Corrosion and Environmental Engineering are gratefully acknowledged for their support, guidance and direction of the work.

REFERENCES

1. Savell, C.T., Handsy, I.C., Ault, P., Baboian, R., Thompson, L., Hathaway, R.M., Lamb, D.A., “Accelerated Corrosion Expert Simulator (ACES)”, DoD Corrosion Conference – 2009, Washington, DC, August 10-14, 2009, NACE

2. C. Thomas Savell, Scott Woodson, Maurizio Borsotto, J. Peter Ault, John Repp, Robert Baboian, Carl Handsy, Dan Nymberg, Scott Porter; “ACES: Accelerated Corrosion Expert Simulator”, Paper 20948 DoD Corrosion Conference-2011, La Quinta, CA, August 3, 2011, NACE

3. John Repp, J. Peter Ault, Carl Handsy, and C. Thomas Savell, “Development of a Vehicle Micro Environment Corrosivity Index”, Paper 20428, DoD Corrosion Conference-2011, La Quinta, CA, August 3, 2011, NACE

4. M. Pourbaix: "The Linear Bilogarithmic Law for Atmospheric Corrosion," Atmospheric Corrosion, W. H.Ailor (Ed.), Wiley, New York, 1982.

5. S. W. Dean, "Analyses of Four Years of Exposure Data from the USA Contribution to the ISO CORRAG Program," Atmospheric Corrosion, ASTM STP 1239, W. W. Kirk and Herbert H. Lawson, (Eds.), American Society for Testing and Materials, Philadelphia, PA, 1995

6. J. Repp, J.P. Ault, I.C. Handsy, “Corrosion Performance of Common Materials of Manufacture for Military Vehicles and Weapon Systems in the Hawaiian Islands”, 2007 Tri-Service Corrosion Conference (DoD Corrosion Conference), Dec 2007, Denver CO