Neural Modelling of APS Thermal Spray Process Parameters for Optimizing the Hardness, Porosity and Cavitation Erosion Resistance of Al2O3-13 wt% TiO2 Coatings

The study aims to elaborate a neural model and algorithm for optimizing hardness and porosity of coatings and thus ensure that they have superior cavitation erosion resistance. Al2O3-13 wt% TiO2 ceramic coatings were deposited onto 316L stainless steel by atmospheric plasma spray (ASP). The coatings...

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Bibliographic Details
Published in:Processes 2020-12, Vol.8 (12), p.1544
Main Authors: Szala, Mirosław, Łatka, Leszek, Awtoniuk, Michał, Winnicki, Marcin, Michalak, Monika
Format: Article
Language:English
Subjects:
Algorithms
Aluminum oxide
Artificial intelligence
Artificial neural networks
Atmospheric models
Austenitic stainless steels
Cavitation
Cavitation erosion
Cavitation resistance
Ceramic coatings
Ceramic glazes
Coatings
Design of experiments
Erosion resistance
Genetic algorithms
Global optimization
Investigations
Mathematical models
Microhardness
Multiple criterion
Neural networks
Optimization
Plasma
Porosity
Process parameters
Protective coatings
Stainless steel
Titanium dioxide
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Summary:The study aims to elaborate a neural model and algorithm for optimizing hardness and porosity of coatings and thus ensure that they have superior cavitation erosion resistance. Al2O3-13 wt% TiO2 ceramic coatings were deposited onto 316L stainless steel by atmospheric plasma spray (ASP). The coatings were prepared with different values of two spray process parameters: the stand-off distance and torch velocity. Microstructure, porosity and microhardness of the coatings were examined. Cavitation erosion tests were conducted in compliance with the ASTM G32 standard. Artificial neural networks (ANN) were employed to elaborate the model, and the multi-objectives genetic algorithm (MOGA) was used to optimize both properties and cavitation erosion resistance of the coatings. Results were analyzed with MATLAB software by Neural Network Toolbox and Global Optimization Toolbox. The fusion of artificial intelligence methods (ANN + MOGA) is essential for future selection of thermal spray process parameters, especially for the design of ceramic coatings with specified functional properties. Selection of these parameters is a multicriteria decision problem. The proposed method made it possible to find a Pareto front, i.e., trade-offs between several conflicting objectives—maximizing the hardness and cavitation erosion resistance of Al2O3-13 wt% TiO2 coatings and, at the same time, minimizing their porosity.
ISSN:2227-9717
2227-9717
DOI:10.3390/pr8121544