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normalized, but otherwise not transformed through modeling of any type. Among the various models tested, an ANN trained with only phase angle information collected after 24 hours of exposure, related well enough to time to failure in salt spray that the relationship allowed prediction of salt spray performance from the short-term EIS measurement. In this work, which follows the approach introduced by Kumar, an attempt is made to predict salt spray performance of commercially available organic coating systems after 720 hours exposure by using an ANN trained with EIS data collected at less than 48 hours. In doing so, an empirical but predictive short-term characterization is developed. In this work, direct inputs from EIS spectra were selected based on a sensitivity analysis, which allowed an estimation of which parts of the EIS spectrum were most significant in forecasting time to failure in the exposure test. ANNs are massively parallel mathematical processing algorithms that emulate aspects of neural processing in the human brain. ANNs excel in recognizing patterns in complex data sets without knowing governing relationships among them beforehand [11]. A feed-forward fully-connected architecture is a commonly used ANN architecture. This architecture consists of an input layer, a hidden layer and an output layer [12]. Each layer is formed by one or more neurons. Each neuron is mathematically connected to all other neurons in the adjacent layers. The network prediction is based upon a feed-forward process in which data is fed in the input layer and thru mathematical operations in the forward direction and an outcome is obtained. The cognitive power of ANN is based on the network architecture (number of layers and neurons in each layer), the training and validation routines, and the type of input variables [13]. Although neural networks have the power to discriminate between noisy input variables and significant ones, assessing the significance between input variables prior to using them in an ANN leads to simpler and more able models. It also allows an understanding of which input variables are most significantly related to the output variables [14, 15]. Sung studied how the accuracy of an ANN changed when the network was trained with data input pruned from a larger data set. Three different pruning methods were explored: sensitivity analysis, change of the means square error, and the method of fuzzy curves [16], with the latter approach being most efficient while retaining acceptable model performance. The method of fuzzy curves [14, 17, 18] for evaluating the significance of input variables to an ANN model prediction is an adaptation derived from fuzzy set theory [19]. Fuzzy set theory, in contrast to a classic crisp set theory, is based on the idea of graded set membership, which can be represented by a mathematical membership function [20]. This mathematical function transforms the input variables into values between zero and one (fuzzy set) instead of only zero and one (crisp theory). This fuzzy set is used to determine the relative significance among input variables. In this work, a range of ANN models were constructed and evaluated for relating EIS data to data from exposure testing. Sensitivity analyses based on the method of fuzzy curves were carried out to pre-prune and simplify models. Pruned ANN models have been trained, validated and assessed for their ability to predict long term exposure testing. 5.6.2.2 Experimental 278
5.6.2.2.1 Coating system description. Thirty commercially available coatings were applied to AA7075-T6 and AA2024-T3 sheet stock samples. Figure 6.14 illustrates the range of coatings tested in this study. All samples were prepared at the Patuxent River Naval Air Station. Each coating system consisted of a conversion coating and a primer. Each primer and conversion coating combination was prepared with and without a clear polyurethane topcoat. Table 6.3 shows coating thickness by sample. Thickness measurements were performed five times for each coating using a coating thickness gauge Elcometer Model 456 with an associated error of less than 4 μm. Five (18cm x 4cm) panels for each coating system were shear cut to form 14 (5cm x 4cm approximately) nominally identical samples, resulting in a total of 840 total tested samples. Each sample was exposed to salt spray for characterization. 2 AA7075 AND AA2024 X 5 ALODINE 5700 (NCP) T5900 (TCP) BOEGEL 8500 PREKOTE (Cleaner) METALLAST (EPA) X 3 DEFT 02GN084 (Pr epoxy) Hentzen 16708TEP (Ti and Silicates) SICOPOXY 577-630 X 2 60 POLYURETHANE (Akso gloss White Eclipse) NONE Figure 6.14. Diagram showing all 60 coating system used. Two metal substrates: AA7075-T6 and AA2024-T3. Five conversion coatings (Alodine, TCP, Boegel, PreKote and Metllast), three primers (Deft, Hentzen, and Sicopoxy) and two finishing procedures, with and without a polyurethane top coat. Five (5) commercial surface treatments were used: Alodine 5700, T5900, Boegel 8500, PreKote, and Metallast EPA. Alodine 5700 is a very dilute form of Alodine 5200 (90% is water), which is a fluorozirconate conversion coating with a Ti base. T5900-TCP (tri-valent chromium conversion coating process) is a dilute solution where 1%wt of the total composition is Cr-III sulfate basic and potassium hexafluorozirconate. Metallast-EPA is a trivalent chromium fluorozirconate conversion coating with a proprietary organic corrosion-inhibiting additive. PreKote is a non-chromate alkali soap cleaner with silane adhesion promoter and inorganic inhibitor. Boegel 8500 is a dilute aqueous mixture of zirconium alkoxide (zirconia alkoxide stabilized with acidic acid) and a silane coupling agent. The processing used to apply each of the 279
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FINAL REPORT Scientific Understandi
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This report was prepared under cont
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List of Acronyms AES Atomic Emissio
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List of Figures Figure 1.1. Figure
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Figure 1.21. Figure 1.22. Figure 1.
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Figure 3.11. Cathodic polarization
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Figure 4.12. Average Roughness of R
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Figure 5.4. Figures 5.5. Apparatus
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Figure 5.36. Figure 5.37. R paramet
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Figure 6.23. Two commercial samples
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Figure 7.1. a) instrumental configu
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List of Tables Table 1.1. Table 1.2
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Table 6.4. Processing parameters fo
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Keywords 1. Al alloys 2. Non-chroma
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1. Executive Summary This report su
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the role of electrophoresis in inhi
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occurring at low pHs and release at
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were obtained by a post treatment w
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4. G.O. Ilevbare, J.R. Scully, J. Y
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3. Objective The primary objective
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5. Results and Accomplishments In t
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powder, slurried with ultrapure wat
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TCP-coated AA2024-T3. A Kratos AXIS
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X-ray Photoelectron Spectroscopy (X
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Potential (V vs. Ag/AgCl) -0.2 -0.3
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Signal Intensity (counts/s) assessi
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Al (including both Al 0 and Al 3+ c
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TEM or during sample preparation, s
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Potential (V vs. Ag/AgCl) and uncoa
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5.1.4.8 Effects of Aging on the Fil
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Intensity (counts/s) Concentration
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|Z| 0.01Hz (ohm cm 2 ) |Z| 0.01Hz (
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The artificial scratch cell was als
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Raman intensity, a.u. 4000 880 3500
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Figure 1.24. Raman spectra for the
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peak at 520 cm -1 and an intense Cr
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Figure 1.28. Plots of the intensity
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Figure 1.30. (A) Video micrograph o
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The Zr and O signals arise from the
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Figure 1.35. (A) Corrosion potentia
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Table 1.1. Corrosion current (i cor
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additional pitting. The TCP coating
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Figure 1.41. Cr(VI) peak intensity
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Work is ongoing to understand how t
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9. L. Li, D-Y. Kim and G. M. Swain,
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carefully rinsed with DI water, air
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decrease to a net anodic current in
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Potential (mV vs. SCE) Potential (m
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i (A/cm 2 ) Immediately after silic
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Potential (mV vs. SCE) Rp (ohm.cm 2
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2.4.4 In situ AFM Scratching Result
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Figure 2.31. In situ AFM scratching
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(2.3) Figure 2.35. Specie diagram f
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concentration and the silicon to ca
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analysis coupled with XPS revealed
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5.2.4.4 In Situ AFM Scratching Befo
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esults in the formation of silicate
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26. P. Schmutz and G.S. Frankel, J.
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Pr 3+ , Y 3+ showed [12] an order o
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0.1M NaCl solution with varying con
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8.7 through pH 12.6 due to the lowe
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(b) (c) 99
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immersed in Ce 3+ remained bright w
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S phase and dissolution of Al matri
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Corrosion rate, i corr (A/cm 2 ) 6x
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I L (microA) 600 500 400 300 NC (-8
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than that of chromate[35]. Historic
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coupon was immersed with the polish
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5.3.2.4 Results 5.3.2.4.1 Speciatio
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(c) Figure 3.8. Speciation diagram
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E vs. SCE (V) -0.70 -0.75 -0.80 -0.
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E vs. SCE (V) 1000 rpm E vs. SCE (V
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Chemical quantification showed a me
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(a) (b) Figure 3.15. (a) (top) SEM
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E vs. SCE (V) E vs. SCE (V) E vs. S
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-Z" (ohm) -Z" (ohm) -Z" (ohm) -Z" (
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(Figure 3.8b). However, no effect o
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CPS CPS CPS x 2 14 10 12 10 8 6 4 2
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CPS CPS 100 95 90 85 80 75 70 x 10
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I k L 0. 2 1 2 AnFC B D 2 3 O 1
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i -800 (A/cm 2 ) 10 -4 NaCl NaCl+5
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Corrosion rate ( 1/R p ) ohm -1 cm
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particles compared to zinc-free sol
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and Zn bentonite compounds have red
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2) Deoxidizing for 3 minutes in an
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Scintag (now ThermoARL) Pad-V TM an
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Table 3.5. Characterization of bent
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Zn 2+ cation release (meq/100g) Pr
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(a) (b) (c) Figure 3.33. (a) An exp
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|Z| (ohm) Phase angle (degrees) 5.3
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|Z| at 0.01 Hz (ohm.cm 2 ) Pore Res
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|Z| at 0.01 Hz (ohm.cm 2 ) |Z| at 0
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pigmented coatings exhibited partia
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5.3.3.6 Conclusions 1. Exchange ben
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39. M. Mahdavian and M. M. Attar, P
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100. F.Liebau, Structural Chemistry
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of the coated sample, which is tigh
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Lubricant Blue (Struers) was used f
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good grip on the dolly, the equipme
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Pressure (kPa) r (mm) Pressure x Ra
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Pressure x Radius (kN/m) plateau bu
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Pressure x Radius (kN/m) Pxr (KN/m)
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Average Roughness (m) In contrast,
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Adhesion Strength (J/m 2 ) PVB PVB
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Pressure (kPa) Constant Infusion up
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(a) (b) (c) (d) Figure 4.19. Coatin
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Min. Potential (V vs SHE) Min. Pote
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Pressure (kPa) exposure in hot wate
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Engineering Stress (MPa) slope of t
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Adhesion Strength (J/m 2 ) ASTM D33
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Pressure (kPa) Pressure (kPa) Radiu
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Adhesion Strength (J/m 2 ) Pressure
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Table 4.7. Adhesion Strength values
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Non-Cl./Deox. No CC Cleaned/Deox. N
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5.4.5 Conclusions and Implications
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5.5 Task 5: Inhibitor Activation an
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solution for equilibration at contr
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Table 5.2. Techniques applied to sp
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The capacitance was determined by E
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combination with zinc molybdate, a
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Concentration, mmol / L Final pH te
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Alkaline affects praseodymium solub
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MoO 4 2- ) inhibition to the system
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Zinc molybdate is reported to be si
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The presence of a salt that has no
Page 261 and 262: This approach yields two figures of
Page 263 and 264: Inhibitor mapping of the Hentzen 16
Page 265 and 266: The molybdate loss in the strontium
Page 267 and 268: The capacitance dC is given by 0 (
Page 269 and 270: 2 C( r, t) C( r )[1 r 2 exp( D nt
Page 271 and 272: -thea (degree) logIZI (Ohm-Cm 2 ) P
Page 273 and 274: Capacitance (F) respectively. The c
Page 275 and 276: Capacitance (F) Weight gain (g) For
Page 277 and 278: Normalized Capacitance Change Norma
Page 279 and 280: Potential V (Ag/AgCl) Corrosion rat
Page 281 and 282: 5.5.4.7 Nanopore structure characte
Page 283 and 284: Normaalized Transport Rate (g/m^2_d
Page 285 and 286: S-Parameter S-Parameter Penetration
Page 287 and 288: S-Parameter S-Parameter Penetration
Page 289 and 290: R-parameter R-parameter Penetration
Page 291 and 292: 5. T. M. Letcher, “Thermodynamics
Page 293 and 294: pigment of CaSiO 3 with initial pH
Page 295 and 296: allowed a comparison of the differe
Page 297 and 298: The solution resistance (R sol ) va
Page 299 and 300: Theta [degrees] theta |Z| [ohm] 10
Page 301 and 302: C [F/cm2] Rcorr [ohm*cm2] 7075-AHN
Page 303 and 304: C [F/cm 2 ] Rcorr [ohm*cm 2 ] Rcorr
Page 305 and 306: Visual TTF [hours] Table 6.2. Param
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Page 309 and 310: Prediction of the TTF obtained by A
Page 311: 5.6.2 Electrochemical Impedance Spe
Page 315 and 316: Table 6.4. Processing parameters fo
Page 317 and 318: population of total TTF vectors th
Page 319 and 320: components of the impedance and the
Page 321 and 322: Figure 6.17. EIS spectra for sample
Page 323 and 324: Figure 6.18. Kaplan-Meier survival
Page 325 and 326: Maximum c i range Maximum ci range
Page 327 and 328: The type and number of input neuron
Page 329 and 330: Predicted TTF The BFM method, intro
Page 331 and 332: The most significant variables to i
Page 333 and 334: 5.6.3 Characterization of Pigment D
Page 335 and 336: of sputtering gold and carbon paint
Page 337 and 338: Figure 6.26. Cross-sectional view o
Page 339 and 340: Figure 6.28. EDX maps of primer cro
Page 341 and 342: Figure 6.31. EDX maps of primer cro
Page 343 and 344: In Figure 6.34, Ca and Si existed a
Page 345 and 346: Table 6.8. Average percentage resid
Page 347 and 348: strictly limited due to the barrier
Page 349 and 350: Figure 6.37. Coating systems in det
Page 351 and 352: Each corrosion volume of scribed ar
Page 353 and 354: Corrosion Area (mm 2 ) 1200 1000 80
Page 355 and 356: Corrosion Area (mm 2 ) Corrosion Ra
Page 357 and 358: Corrosion Rate (mm/yr) Corrosion Vo
Page 359 and 360: Corrosion Area (mm 2 ) corrosion ra
Page 361 and 362: corrosion rate (mm/yr) Corrosion Vo
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Corrosion Volume (mm/yr) Corrosion
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ate, PPG CA7233 coated samples had
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Appendix 6B Table 6B.1. Corrosion v
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Table 6B.3. Corrosion rate of sampl
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Table 6B.5. Corrosion area of sampl
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5.7. Task 7: Characterization of Lo
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at 65C. After using DI water to rin
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Transmission Mode Conduct EIS measu
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Cdl(F/cm 2 ) Rp(.cm 2 ) relatively
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pit bottom area (cm 2 ) pit bottom
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2days 2days 1mm 1mm 7days 7days Pit
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(a) (b) Si Si O Al Na Ca K Si Al-Cu
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Pit 1 and pit 2 as shown in Figure
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In these experiments, Deft primers
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For the case of as-deposited thin f
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3. L. Balazs, Physica E, 54, 1183-1
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and subsequently re-oxidized in two
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not represent true adhesion strengt
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• The pit morphology in 0.5 M NaC
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Appendix 2. List of Scientific/Tech
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“The Secret Life of Chromate Free
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