Open Access

ARTICLE

Artificial Neural Networks for Optimizing Alumina Al₂O₃ Particle and Droplet Behavior in 12kK Ar-H₂ Atmospheric Plasma Spraying

Ridha Djebali^1,*, Bernard Pateyron², Mokhtar Ferhi¹, Mohamed Ouerhani³, Karim Khemiri¹, Montassar Najari¹, M. Ammar Abbassi⁴, Chohdi Amri⁵, Ridha Ennetta⁶, Zied Driss⁷

1 UR22ES12: Modeling, Optimization and Augmented Engineering, ISLAIB, University of Jendouba, Beja, 9000, Tunisia
2 IRCER, UMR CNRS 7315, University of Limoges, 12 rue Atlantis, Limoges, 87068, France
3 ICAM School of Engineering, Nantes Campus, 35 Av. Champ de Manoeuvres, Carquefou, 44470, France
4 Research Lab, Technology Energy and Innovative Materials, Faculty of Sciences, University of Gafsa, Gafsa, 2112, Tunisia
5 Department of Physics, College of Science, United Arab Emirates University, Al Ain, 15551, United Arab Emirates
6 Lab. Mechanical Modeling, Energy and Materials (M2EM), ENIG, University of Gabes, Gabes, 6029, Tunisia
7 Laboratory of Electro-Mechanic Systems, ENIS, University of Sfax, Sfax, 3038, Tunisia

* Corresponding Author: Ridha Djebali. Email:

Frontiers in Heat and Mass Transfer 2025, 23(2), 441-461. https://doi.org/10.32604/fhmt.2025.063375

Received 13 January 2025; Accepted 18 March 2025; Issue published 25 April 2025

Abstract

This paper investigates the application of Direct Current Atmospheric Plasma Spraying (DC-APS) as a versatile thermal spray technique for the application of coatings with tailored properties to various substrates. The process uses a high-speed, high-temperature plasma jet to melt and propel the feedstock powder particles, making it particularly useful for improving the performance and durability of components in renewable energy systems such as solar cells, wind turbines, and fuel cells. The integration of nanostructured alumina (Al₂O₃) thin films into multilayer coatings is considered a promising advancement that improves mechanical strength, thermal stability, and environmental resistance. The study highlights the importance of understanding injection parameters and their impact on coating properties and uses simulation tools such as the Jets & Poudres (JP) code for in-depth analysis. Furthermore, the paper discusses the implementation of Artificial Neural Networks (ANN) to optimize the coating process by predicting flight characteristics and improving operating conditions. The results show that ANN models are effective in achieving highly accurate prediction values, highlighting the potential of AI in improving thermal spray technology.

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