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Non Skid Coating Formulation Utilizing a Design of Experiments (DOE) Approach

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. Overview. Repair and Replacement

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Non Skid Coating Formulation Utilizing a Design of Experiments (DOE) Approach

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  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. Overview Repair and Replacement • Repair is time-, material-, and labor-intensive. • Repair costs Range from $13- $25 /ft2 • CV 63 (November 2000) • 116,000 ft2 • Labor: $22.50 / ft2 • Material: $ 2.80 / ft2 • CVN 72 (April 2004) • 70,000 ft2 • Cost: $1.4 Million ($20 / ft2 ) • 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.

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

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

  9. 3-Factor (full factorial) Linear Factor 2 Linear Design • 2 levels for each factor • 2n trials • For 3 factors n = 3 • 23 trials required Factor 1 Factor 3

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

  11. Non Skid Polymer Formulation Components & Levels • Components (Levels) • Polyamine Curing Agent #1 (Stoich) • Polyamine Curing Agent #2 (Stoich) • Modifier #1 (0% – 30% by weight of base resin) • Modifier #2 (0% – 30% by weight of base resin) • Modifier #3 (0% – 30% by weight of base resin) • Adhesion Promoter #1 (0% – 0.5% by weight of Resin) • Adhesion Promoter #2 (0% – 0.5% by weight of Resin) • 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) • Polyamine Curing Agent #1 (Fraction of total curing agent used: 0 - 1) • Polyamine Curing Agent #2 (Stoich, based on amount of PCA1) • Modifier #1 (0% – 30% by weigh of Resin) • Modifier #2 (0% – 30% by weight of Resin) • Modifier #3 (0% – 30% by weight of Resin) • Adhesion Promoter #1 (0% – 0.5% by weight of Resin) • Adhesion Promoter #2 (0% – 0.5% by weight of Resin) • Base Resin (100 grams)

  14. Curing Run MOD 1 MOD 2 MOD 3 AP 1 AP 2 Agent 1 1 0.00 0.0 15 0 0.5 0 2 1.00 0.0 30 0 0 0.5 3 1.00 0.0 0 0 0.5 0 4 1.00 0.0 30 0 0.25 0 5 0.00 0.0 0 30 0 0.5 6 0.50 0.0 0 0 0 0.5 7 0.00 0.0 30 0 0.5 0.5 8 1.00 30.0 0 0 0 0 9 0.00 30.0 0 0 0.5 0.25 10 0.00 0.0 0 0 0 0 11 1.00 0.0 0 30 0 0.25 12 0.00 0.0 15 15 0 0 13 0.25 3.8 3.75 18.75 0.25 0.125 14 0.00 0.0 0 30 0.5 0 15 0.00 0.0 0 0 0 0 16 1.00 0.0 15 0 0 0 17 1.00 15.0 0 15 0.5 0 18 0.00 15.0 15 0 0.5 0 19 1.00 15.0 0 0 0.5 0 20 0.00 0.0 15 15 0 0 21 0.00 30.0 0 0 0 0 22 0.00 0.0 30 0 0 0.5 23 1.00 30.0 0 0 0.5 0.5 24 0.00 0.0 0 30 0.5 0.5 25 1.00 0.0 0 15 0 0.5 26 0.50 10.0 10 0 0.25 0.25 27 1.00 0.0 0 0 0.5 0 28 1.00 0.0 0 0 0.25 0.5 29 0.00 0.0 0 0 0.5 0.5 30 1.00 0.0 30 0 0.5 0.25 31 1.00 15.0 0 15 0 0.5 32 0.00 0.0 0 0 0.5 0.5 33 0.50 0.0 0 30 0 0 34 1.00 0.0 0 15 0 0.5 35 0.00 30.0 0 0 0 0.5 36 1.00 0.0 0 30 0.5 0 37 0.00 0.0 30 0 0 0 38 1.00 0.0 0 30 0.5 0.5 D-Optimal Design: 38 Total Trials

  15. Conversions Trial #1

  16. Curing Curing Total MOD 1 MOD 2 MOD 3 AP 1 AP 2 Resin Run Agent 1 Agent 2 Mixture (grams) (grams) (grams) (grams) (grams) (grams) (grams) (grams) (grams) 1 0.00 52.83 0.00 15.00 0.00 0.50 0.00 100.00 168.33 2 66.19 0.00 0.00 30.00 0.00 0.00 0.50 100.00 196.69 3 86.62 0.00 0.00 0.00 0.00 0.50 0.00 100.00 187.12 4 66.37 0.00 0.00 30.00 0.00 0.25 0.00 100.00 196.62 5 0.00 67.43 0.00 0.00 30.00 0.00 0.50 100.00 197.93 6 43.13 29.75 0.00 0.00 0.00 0.00 0.50 100.00 173.38 7 0.00 45.91 0.00 30.00 0.00 0.50 0.50 100.00 176.91 8 65.63 0.00 30.00 0.00 0.00 0.00 0.00 100.00 195.63 9 0.00 45.53 30.00 0.00 0.00 0.50 0.25 100.00 176.28 10 0.00 59.50 0.00 0.00 0.00 0.00 0.00 100.00 159.50 11 97.75 0.00 0.00 0.00 30.00 0.00 0.25 100.00 228.00 12 0.00 56.55 0.00 15.00 15.00 0.00 0.00 100.00 186.55 13 22.13 45.81 3.75 3.75 18.75 0.25 0.13 100.00 194.57 14 0.00 67.69 0.00 0.00 30.00 0.50 0.00 100.00 198.19 15 0.00 59.50 0.00 0.00 0.00 0.00 0.00 100.00 159.50 16 76.22 0.00 0.00 15.00 0.00 0.00 0.00 100.00 191.22 17 82.06 0.00 15.00 0.00 15.00 0.50 0.00 100.00 212.56 18 0.00 45.72 15.00 15.00 0.00 0.50 0.00 100.00 176.22 19 76.31 0.00 15.00 0.00 0.00 0.50 0.00 100.00 191.81 20 0.00 56.55 0.00 15.00 15.00 0.00 0.00 100.00 186.55 21 0.00 45.28 30.00 0.00 0.00 0.00 0.00 100.00 175.28 22 0.00 45.66 0.00 30.00 0.00 0.00 0.50 100.00 176.16 23 66.00 0.00 30.00 0.00 0.00 0.50 0.50 100.00 197.00 24 0.00 67.69 0.00 0.00 30.00 0.50 0.50 100.00 198.69 25 92.00 0.00 0.00 0.00 15.00 0.00 0.50 100.00 207.50 26 36.44 25.14 10.00 10.00 0.00 0.25 0.25 100.00 182.07 27 86.62 0.00 0.00 0.00 0.00 0.50 0.00 100.00 187.12 28 86.43 0.00 0.00 0.00 0.00 0.25 0.50 100.00 187.18 29 0.00 59.75 0.00 0.00 0.00 0.50 0.50 100.00 160.75 30 66.56 0.00 0.00 30.00 0.00 0.50 0.25 100.00 197.31 31 81.69 0.00 15.00 0.00 15.00 0.00 0.50 100.00 212.19 32 0.00 59.75 0.00 0.00 0.00 0.50 0.50 100.00 160.75 33 48.88 33.72 0.00 0.00 30.00 0.00 0.00 100.00 212.59 34 92.00 0.00 0.00 0.00 15.00 0.00 0.50 100.00 207.50 35 0.00 45.28 30.00 0.00 0.00 0.00 0.50 100.00 175.78 36 98.12 0.00 0.00 0.00 30.00 0.50 0.00 100.00 228.62 37 0.00 45.66 0.00 30.00 0.00 0.00 0.00 100.00 175.66 38 98.12 0.00 0.00 0.00 30.00 0.50 0.50 100.00 229.12 Experimental Design

  17. ANOVA TableLow-Energy Blunt Impact Response:LE BluntTransform: Natural logConstant:1.99Sum ofMeanFSourceSquaresDFSquareValueProb > F Model 90.25 4 22.56 13.24 < 0.0001 significant A25.75125.7515.110.0004B5.0515.052.960.0943D1.2511.250.740.3972BD10.13110.135.940.0201 Residual 57.93 34 1.70Lack of Fit44.96281.610.740.7297not significantPure Error12.9762.16Cor Total 148.18 38The 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 TableHigh-Energy Blunt Impact Response: HE BluntTransform:Inverse sqrtConstant:2.24Sum ofMeanFSourceSquaresDFSquareValueProb > F Model 1.56 8 0.19 4.52 0.0011 significant A0.2810.286.520.0160B0.1210.122.870.1007C0.09010.0902.090.1588D0.1910.194.390.0448E0.1710.173.920.0570F0.03710.0370.860.3606BD0.3010.306.870.0136CF0.4310.4310.090.0034 Residual 1.29 30 0.043Lack of Fit0.91240.0380.590.8333not significantPure Error0.3860.064 Cor Total 2.85 38The 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. ANOVA TableHigh-Energy Sharp Impact Response: HE SharpTransform: Square rootConstant:7.84Sum ofMeanFSourceSquaresDFSquareValueProb > F Model 1342.46 8 167.81 24.41 < 0.0001 significant A1004.8211004.82146.19< 0.0001B71.07171.0710.340.0031D19.44119.442.830.1030E15.29115.292.230.1462F0.2210.220.0320.8590E250.30150.307.320.0111AD85.92185.9212.500.0013EF174.461174.4625.38< 0.0001 Residual 206.20 30 6.87Lack of Fit171.12247.131.220.4346not significantPure Error35.0865.85 Cor Total 1548.66 38The 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.

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

  23. DESIGN-EXPERT Plot X = AP 1 Y = AP 2 Actual Factors 103 Curing Agent 2 = 0.00 Modifier 1 = 23.11 78 Modifier 2 = 0.00 Modifier 3 = 0.00 52 26 HE Sharp 0 0.50 0.50 0.38 0.38 0.25 0.25 AP 2 0.13 0.13 AP 1 0.00 0.00 Tail-Hook Impact ResistanceHigh-Energy Sharp

  24. OptimizationHigh-Energy Sharp ConstraintsLowerUpperLowerUpperNameGoalLimitLimitWeightWeightImportanceA2 is in range 01113ATU is in range 030113OP is in range 030113GTS is in range 030113GTMS is in range 00.5113OS is in range 00.5113HE Sharpminimize 0784115 Solutions IDA2ATUOP*GTSGTMSOSHE SharpDesirability 1 0.00 20.25 0.00 0.86 0.50 0.00 3 0.983 2 0.00 11.65 9.18 9.17 0.50 0.00 6 0.9633 0.00 23.17 3.98 2.65 0.00 0.50 9 0.9464 0.00 17.54 2.80 3.37 0.00 0.50 16 0.9185 0.00 17.26 2.74 0.00 0.00 0.50 20 0.9036 0.00 20.96 0.00 0.12 0.50 0.22 22 0.8967 0.02 13.35 0.00 16.65 0.00 0.36 29 0.8728 0.00 0.88 9.67 19.03 0.00 0.50 30 0.8699 0.00 2.65 0.00 7.15 0.50 0.15 36 0.84810 0.00 10.02 0.00 0.00 0.49 0.30 56 0.794

  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

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