Prediction of Mechanical Properties of Plasma Sprayed Thermal Barrier Coatings (TBCs) with Genetic Programming (GP)

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2017 by IJETT Journal
Volume-47 Number-3
Year of Publication : 2017
Authors : Mohammed Yunus, Mohammad S. Alsoufi
  10.14445/22315381/IJETT-V47P223

MLA 

Mohammed Yunus, Mohammad S. Alsoufi "Prediction of Mechanical Properties of Plasma Sprayed Thermal Barrier Coatings (TBCs) with Genetic Programming (GP)", International Journal of Engineering Trends and Technology (IJETT), V47(3),139-145 May 2017. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group

Abstract
The mechanical properties especially hardness and porosity of plasma sprayed thermal barrier coating (TBC) play a major role in deciding their lifetime and performance with respect to input process parameters such as power input of plasma jet, coating thickness, stand-off distance and type of coating. Sources of mechanical properties values are experimental measurements only, and empirical correlations are to be built up (without appropriate fitting techniques), however, these are often too complicated, expensive and time consuming and can lead to erroneous results. Genetic programming (GP) is the most common approach from various evolutionary computation methods using multivariate regression fitting for the modelling of various systems. This study presents a new model for estimating the mechanical properties of TBC using GP. On the basis of a training data set, different genetic models for mechanical properties with great accuracy were obtained during simulated evolution. The newly developed GP-based computational model provides a more accurate prediction of mechanical properties compared to the empirical correlations, and the results can then be utilized to estimate a future set of parameters based on the historical data.

 References

[1] Clarke, D.R., M. Oechsner, and N.P. Padture, Thermalbarrier coatings for more efficient gas-turbine engines. MRS Bulletin, 2012. 37(10): p. 891-898.
[2] Vaßen, R., et al., Testing and evaluation of thermal-barrier coatings. MRS Bulletin, 2012. 37(10): p. 911-916.
[3] Sampath, S., et al., Processing science of advanced thermal-barrier systems. MRS Bulletin, 2012. 37(10): p. 903-910.
[4] Pan, W., et al., Low thermal conductivity oxides. MRS Bulletin, 2012. 37(10): p. 917-922.
[5] Yunus, M. and M.S. Alsoufi, Multi-Objective Optimization of Joint Strength of Dissimilar Aluminum Alloys Formed by Friction Stir Welding Using Taguchi-Grey Relation Analysis. International Journal of Engineering & Technology IJET-IJENS, 2016. 16(04): p. 10-17.
[6] Yunus, M., M.S. Alsoufi, and S.M. Munshi, Taguchi-Grey relation analysis for assessing the optimal set of control factors of thermal barrier coatings for high-temperature applications. Mechanics of Advanced Materials and Modern Processes, 2016. 2(1): p. 4.
[7] Yunus, M. and M.S. Alsoufi, Multi-output optimization of tribological characteristics control factors of thermally sprayed industrial ceramic coatings using hybrid Taguchigrey relation analysis. Friction, 2016. 4(3): p. 208-216.
[8] Li, C.-J. and A. Ohmori, Relationships between the microstructure and properties of thermally sprayed deposits. Journal of Thermal Spray Technology, 2002. 11(3): p. 365-374.
[9] Yunus, M. and M.S. Alsoufi, A Statistical Analysis of Joint Strength of Dissimilar Aluminium Alloys Formed By Friction Stir Welding Using Taguchi Design Approach, Anova For The Optimization Of Process Parameters. IMPACT: International Journal of Research in Engineering & Technology (IMPACT: IJRET), 2015. 3(7): p. 63-70.
[10] Shrestha, S. and A. Sturgeon, Characteristics and electrochemical corrosion behaviour of thermal sprayed aluminium (TSA) coatings prepared by various wire thermal spray processes. TWI Ltd, 2005: p. 4-8.
[11] Miller, R.A., Current status of thermal barrier coatings — An overview. Surface and Coatings Technology, 1987. 30(1): p. 1-11.
[12] Miller, R.A., Thermal barrier coatings for aircraft engines: history and directions. Journal of Thermal Spray Technology, 1997. 6(1): p. 35.
[13] Beardsley, M.B., Thick thermal barrier coatings for diesel engines. Journal of Thermal Spray Technology, 1997. 6(2): p. 181-186.
[14] Soltani, R., T.W. Coyle, and J. Mostaghimi, Creep Behavior of Plasma-Sprayed Zirconia Thermal Barrier Coatings. Journal of the American Ceramic Society, 2007. 90(9): p. 2873-2878.
[15] Eaton, H.E. and R.C. Novak, Sintering studies of plasmasprayed zirconia. Surface and Coatings Technology, 1987. 32(1): p. 227-236.
[16] Brink, R.C., Material Property Evaluation of Thick Thermal Barrier Coating Systems. Journal of Engineering for Gas Turbines and Power, 1989. 111(3): p. 570-577.
[17] Yunus, M., J.F. Rahman, and S. Ferozkhan, Genetic programming approach for the prediction of thermal characteristics of ceramic coatings. IJIERD, 2011. 2(1): p. 69-79.
[18] Yunus, M., J.F. Rahman, and S. Ferozkhan, Evaluation of machinability characteristics of industrial ceramic coatings using genetic programming based approach. International Journal of Mechanical Engineering and Technology (IJMET), 2011. 2(2): p. 126-137.
[19] Koza, J.R., Genetic Programming: On the Programming of Computers by Natural Selection. 1992, Cambridge, MA.: MIT Press. 836.
[20] Koza, J.R., Genetic Programming II (Automatic Discovery of Reusable Programs). 1994, Massachusetts: The MIT Press. 768.
[21] Koza, J.R., et al., Genetic Programming III: Darwinian Invention and Problem Solving. 1999: Morgan Kaufmann. 1154.
[22] Brezocnik, M., M. Kovacic, and M. Ficko, Prediction of surface roughness with genetic programming. Journal of Materials Processing Technology, 2004. 157–158: p. 28-36.
[23] Brezocnik, M. and M. Kovacic, Integrated Genetic Programming and Genetic Algorithm Approach to Predict Surface Roughness. Materials and Manufacturing Processes, 2003. 18(3): p. 475-491.
[24] Cruse, T.A., B.P. Johnsen, and A. Nagy, Mechanical properties testing and results for thermal barrier coatings. Journal of Thermal Spray Technology, 1997. 6(1): p. 57.
[25] E2109-01, A., Standard Test Methods for Determining Area Percentage Porosity in Thermal Sprayed Coatings. 2014, ASTM International: West Conshohocken, PA.
[26] Choi, S.R., D.-M. Zhu, and R.A. Miller, Effect of Sintering on Mechanical and Physical Properties of Plasma-Sprayed Thermal Barrier Coatings. 2004, NASA/TM-2004-212625: USA.
[27] Zhu, D. and R.A. Miller, Sintering and creep behavior of plasma-sprayed zirconia- and hafnia-based thermal barrier coatings. Surface and Coatings Technology, 1998. 108–109: p. 114-120.
[28] Gusel, L. and M. Brezocnik, Modeling of impact toughness of cold formed material by genetic programming. Computational Materials Science, 2006. 37(4): p. 476-482.
[29] Chang, Y.S., K.S. Park, and B.Y. Kim, Nonlinear model for ECG R-R interval variation using genetic programming approach. Future Gener. Comput. Syst., 2005. 21(7): p. 1117-1123.
[30] Brezocnik, M. and L. Gusel, Predicting stress distribution in cold-formed material with genetic programming. The International Journal of Advanced Manufacturing Technology, 2004. 23(7): p. 467-474.

Keywords
Hardness, Porosity, Thermal Barrier Coatings, Plasma Spraying, Genetic Programming.