Process Parameters Optimization of Plasma Spraying Nanostructured Coating Based on Particle Swarm Optimization Algorithm

Abstract:

Process parameters of plasma spraying nanostructured Al₂O₃-13%TiO₂ (mass fraction) coating were optimized based on particle swarm optimization (PSO) algorithm. BP neural network was applied to compute fitness of PSO algorithm. A BP neural network model was built. Process parameters of coating were optimized based on PSO algorithm. The results shown that maximal bonding strength was 33.08MPa. Process parameters of plasma spraying nanostructured Al₂O₃-13%TiO₂ (mass fraction) coating were obtained. The results were superior to design of orthogonal optimization. It provided definite reference for selecting the best process parameters of plasma spraying nanostructured Al₂O₃-13%TiO₂ (mass fraction) coating.

• Periodical: Applied Mechanics and Materials (Volume 665)
• Pages: 68-71
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.665.68
• Online since: October 2014
• Authors: Bin Yang
• Keywords: BP Neural Network (BPNN), Nanostructured Coating, Particle Swarm Optimization Algorithm, Plasma Spraying, Process Parameters Optimization

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