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


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. published by seventh sense research group

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.


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