1. Non Skid Coating Formulation Utilizing a Design of Experiments (DOE) Approach TRFA Annual Meeting, Fort Lauderdale FL 14 November 2005 Charles S. Tricou Applied Research Laboratory The Pennsylvania State University

2. Repair and Replacement Repair is time-, material-, and labor- intensive. Repair costs Range from $13- $25 /ft 2 –CV 63 (November 2000) »116,000 ft 2 »Labor: $22.50 / ft 2 »Material:$ 2.80 / ft 2 –CVN 72 (April 2004) »70,000 ft 2 »Cost: $1.4 Million ($20 / ft 2 ) Durability Approximately 80% of CVN flight deck nonskid coatings are replaced following each deployment. Extending the durability and functionality of nonskid coatings to last through 2 full deployments will save the Navy ~ $5M per year. Nonskid coatings in arrested landing areas are removed and replaced 2 or 3 times per deployment cycle. Flight deck coatings have degraded during deployment to an extent necessitating repair. Repairs at foreign ports are very expensive and result in temporary loss of platform availability. Overview

3. Approach Develop a High-Performance Epoxy / Urethane Polymer A design of experiments (DOE) approach was used to optimize coating performance in corrosion resistance, chemical resistance, impact resistance, and long-term coefficient of friction (COF) retention. This approach offers the potential of achieving maximum performance from an organic-based nonskid coating. After qualification, such a system may be used as a drop-in replacement for current epoxy-based systems.

4. Performance Measurements (Outputs) Coating performance measurements  Adhesion  Corrosion (QUV, Salt Fog, Immersion, etc.) Service-specific durability tests Erosion / Wear Impact Resistance Chemical Resistance

5. Performance Measurements (Outputs) Coating performance measurements  Adhesion  Corrosion (QUV, Salt Fog, Immersion, etc.) Service-specific durability tests Erosion / Wear Impact Resistance Chemical Resistance

6. DOE Approach – What is it? Design of Experiments (DOE) is a scientific approach to experimentation. A good DOE will yield the following benefits:  Aid in the selection and isolation of the important variables to be studied  Minimize the number of experiments that must be carried out to yield meaningful results  Maximize the amount of information that can be extracted from the experiments  Minimize the cost of product development and process control

7. DOE – How it Works 2-Factor (full factorial) Linear Linear Design  2 levels for each factor  2 n trials  For 2 factors n = 2 →4 trials required Factor 1 Factor 2 Provides information about interactions between factors (variables)

8. 2-Factor (full factorial) Quadratic Factor 1 Factor 2 Non-Linear Design  3 levels for each factor  3 n trials  For 2 factors (n = 2) →9 trials required

9. 3-Factor (full factorial) Linear Factor 1 Factor 2 Linear Design  2 levels for each factor  2 n trials  For 3 factors n = 3 → 2 3 trials required Factor 3

10. Mixture Designs Constraints  C1 + C2 + C3 = Fixed % Component 1 Component 3Component 2 Binary Blend

11. Non Skid Polymer Formulation Components & Levels Components (Levels) A.Polyamine Curing Agent #1 (Stoich) B.Polyamine Curing Agent #2 (Stoich) C.Modifier #1 (0% – 30% by weight of base resin) D.Modifier #2 (0% – 30% by weight of base resin) E.Modifier #3 (0% – 30% by weight of base resin) F.Adhesion Promoter #1 (0% – 0.5% by weight of Resin) G.Adhesion Promoter #2 (0% – 0.5% by weight of Resin) H.Base Resin (100 grams) Constraints: Total Modifier cannot exceed 30% 0 ≤ C + D + E ≤ 30 grams

12. Design Strategy In total, there are 8 potential components that may be used in the coating formulation. However, the amount of base resin used in each trial is held constant at 100 grams. Since the amount of resin does not vary, the base resin may be eliminated as a variable, reducing the number of variables to 7. To maintain stoichiometry, the amount of one curing agent used will depend upon the amount of the other curing agent used. By expressing the amount of one of the curing agents as a fraction of the total curing agent used, the other curing agent is eliminated as a variable, reducing the total number of variables from 7 to 6.

13. Design Strategy A quadratic D-Optimal design was chosen for this experiment. The D-Optimal design provides substantial information with a a minimum number of trials. Components (Levels) A.Polyamine Curing Agent #1 (Fraction of total curing agent used: 0 - 1) B.Polyamine Curing Agent #2 (Stoich, based on amount of PCA1) C.Modifier #1 (0% – 30% by weigh of Resin) D.Modifier #2 (0% – 30% by weight of Resin) E.Modifier #3 (0% – 30% by weight of Resin) F.Adhesion Promoter #1 (0% – 0.5% by weight of Resin) G.Adhesion Promoter #2 (0% – 0.5% by weight of Resin) H.Base Resin (100 grams)

14. D-Optimal Design: 38 Total Trials Run Curing Agent 1 MOD 1MOD 2MOD 3AP 1AP 2 10.00 0.01500.50 21.00 0.030000.5 31.00 0.0000.50 41.00 0.03000.250 50.00 0.003000.5 60.50 0.00000.5 70.00 0.03000.5 81.00 30.00000 90.00 30.0000.50.25 100.00 0.00000 111.00 0.003000.25 120.00 0.015 00 130.25 3.83.7518.750.250.125 140.00 0.00300.50 150.00 0.00000 161.00 0.015000 171.00 15.00150.50 180.00 15.01500.50 191.00 15.0000.50 200.00 0.015 00 210.00 30.00000 220.00 0.030000.5 231.00 30.0000.5 240.00 0.00300.5 251.00 0.001500.5 260.50 10.01000.25 271.00 0.0000.50 281.00 0.0000.250.5 290.00 0.0000.5 301.00 0.03000.50.25 311.00 15.001500.5 320.00 0.0000.5 330.50 0.003000 341.00 0.001500.5 350.00 30.00000.5 361.00 0.00300.50 370.00 0.030000 381.00 0.00300.5

15. Conversions Trial #1

16. Experimental Design Run Curing Agent 1 (grams) Curing Agent 2 (grams) MOD 1 (grams) MOD 2 (grams) MOD 3 (grams) AP 1 (grams) AP 2 (grams) Resin (grams) Total Mixture (grams) 10.0052.830.0015.000.000.500.00100.00168.33 266.190.00 30.000.00 0.50100.00196.69 386.620.00 0.500.00100.00187.12 466.370.00 30.000.000.250.00100.00196.62 50.0067.430.00 30.000.000.50100.00197.93 643.1329.750.00 0.50100.00173.38 70.0045.910.0030.000.000.50 100.00176.91 865.630.0030.000.00 100.00195.63 90.0045.5330.000.00 0.500.25100.00176.28 100.0059.500.00 100.00159.50 1197.750.00 30.000.000.25100.00228.00 120.0056.550.0015.00 0.00 100.00186.55 1322.1345.813.75 18.750.250.13100.00194.57 140.0067.690.00 30.000.500.00100.00198.19 150.0059.500.00 100.00159.50 1676.220.00 15.000.00 100.00191.22 1782.060.0015.000.0015.000.500.00100.00212.56 180.0045.7215.00 0.000.500.00100.00176.22 1976.310.0015.000.00 0.500.00100.00191.81 200.0056.550.0015.00 0.00 100.00186.55 210.0045.2830.000.00 100.00175.28 220.0045.660.0030.000.00 0.50100.00176.16 2366.000.0030.000.00 0.50 100.00197.00 240.0067.690.00 30.000.50 100.00198.69 2592.000.00 15.000.000.50100.00207.50 2636.4425.1410.00 0.000.25 100.00182.07 2786.620.00 0.500.00100.00187.12 2886.430.00 0.250.50100.00187.18 290.0059.750.00 0.50 100.00160.75 3066.560.00 30.000.000.500.25100.00197.31 3181.690.0015.000.0015.000.000.50100.00212.19 320.0059.750.00 0.50 100.00160.75 3348.8833.720.00 30.000.00 100.00212.59 3492.000.00 15.000.000.50100.00207.50 350.0045.2830.000.00 0.50100.00175.78 3698.120.00 30.000.500.00100.00228.62 370.0045.660.0030.000.00 100.00175.66 3898.120.00 30.000.50 100.00229.12

17. ANOVA Table Low-Energy Blunt Impact Response:LE BluntTransform: Natural logConstant:1.99 Sum ofMeanF SourceSquaresDFSquareValueProb > F Model90.25422.5613.24< 0.0001significant A25.75125.7515.110.0004 B5.0515.052.960.0943 D1.2511.250.740.3972 BD10.13110.135.940.0201 Residual 57.93341.70 Lack of Fit 44.96281.610.740.7297not significant Pure Error 12.9762.16 Cor Total 148.1838 The Model F-value of 13.24 implies the model is significant. There is only a 0.01% chance that a "Model F-Value" this large could occur due to noise.

18. Low-Energy Blunt Impact

19. ANOVA Table High-Energy Blunt Impact Response: HE BluntTransform:Inverse sqrtConstant:2.24 Sum ofMeanF SourceSquaresDFSquareValueProb > F Model1.5680.194.520.0011significant A0.2810.286.520.0160 B0.1210.122.870.1007 C0.09010.0902.090.1588 D0.1910.194.390.0448 E0.1710.173.920.0570 F0.03710.0370.860.3606 BD0.3010.306.870.0136 CF0.4310.4310.090.0034 Residual1.29300.043 Lack of Fit0.91240.0380.590.8333not significant Pure Error0.3860.064 Cor Total2.8538 The Model F-value of 4.52 implies the model is significant. There is only a 0.11% chance that a "Model F-Value" this large could occur due to noise.

20. High-Energy Blunt Impact

21. Response: HE Sharp Transform: Square rootConstant:7.84 Sum ofMeanF SourceSquaresDFSquareValueProb > F Model1342.468167.8124.41< 0.0001significant A1004.8211004.82146.19< 0.0001 B71.07171.0710.340.0031 D19.44119.442.830.1030 E15.29115.292.230.1462 F0.2210.220.0320.8590 E 2 50.30150.307.320.0111 AD85.92185.9212.500.0013 EF174.461174.4625.38< 0.0001 Residual206.20306.87 Lack of Fit171.12247.131.220.4346not significant Pure Error35.0865.85 Cor Total1548.6638 The Model F-value of 24.41 implies the model is significant. There is only a 0.01% chance that a "Model F-Value" this large could occur due to noise. ANOVA Table High-Energy Sharp Impact

22. Tail-Hook Impact Resistance High-Energy Sharp DESIGN-EXPERT Plot X = Polyamine Curing Agent 2 Y = Modifier 3 Actual Factors B: Modifier 1 = 0.00 C: Modifier 2 = 0.00 E: A.P. 1 = 0.50 F: A.P. 2 = 0.00 6 112 217 323 429 HE Sharp 0.00 0.25 0.50 0.75 1.00 0.00 7.50 15.00 22.50 30.00 Polyamine Curing Agent 2 Mod 3

23. Tail-Hook Impact Resistance High-Energy Sharp

24. Constraints LowerUpperLowerUpper NameGoalLimitLimitWeightWeightImportance A2 is in range 01113 ATU is in range 030113 OP is in range 030113 GTS is in range 030113 GTMS is in range 00.5113 OS is in range 00.5113 HE Sharpminimize 0784115 Solutions IDA2ATUOP*GTSGTMSOSHE SharpDesirability 10.0020.250.000.860.500.0030.983 20.0011.659.189.170.500.0060.963 30.0023.173.982.650.000.5090.946 40.0017.542.803.370.000.50160.918 50.0017.262.740.000.000.50200.903 60.0020.960.000.120.500.22220.896 70.0213.350.0016.650.000.36290.872 80.000.889.6719.030.000.50300.869 90.002.650.007.150.500.15360.848 100.0010.020.000.000.490.30560.794 Optimization High-Energy Sharp

25. Results Based on the results of this study, two candidate formulations have been identified which provide improved performance in blunt and sharp impact resistance. These formulations are unique blends which did not appear in the original DOE.

26. Team Participants Applied Research Laboratory Epoxy Chemicals, Inc. Pratt & Whitney Automation St. Gobain Mineral Abrasives