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Article

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

1
Department of Materials Engineering, Faculty of Mechanical Engineering, Lublin University of Technology, Nadbystrzycka 36D, 20-618 Lublin, Poland
2
Faculty of Mechanical Engineering, Wrocław University of Science and Technology, 5 Łukasiewicza Street, 50-371 Wrocław, Poland
3
Institute of Mechanical Engineering, Warsaw University of Life Sciences, Nowoursynowska 164, 02-787 Warsaw, Poland
*
Authors to whom correspondence should be addressed.
Processes 2020, 8(12), 1544; https://doi.org/10.3390/pr8121544
Received: 3 November 2020 / Revised: 22 November 2020 / Accepted: 24 November 2020 / Published: 26 November 2020
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. View Full-Text
Keywords: artificial neural network; APS; cavitation erosion; ceramic coatings; multi-objective optimization; wear; hardness; microstructure; alumina–titania; Al2O3-13 wt% TiO2 artificial neural network; APS; cavitation erosion; ceramic coatings; multi-objective optimization; wear; hardness; microstructure; alumina–titania; Al2O3-13 wt% TiO2
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MDPI and ACS Style

Szala, M.; Łatka, L.; Awtoniuk, M.; Winnicki, M.; Michalak, M. Neural Modelling of APS Thermal Spray Process Parameters for Optimizing the Hardness, Porosity and Cavitation Erosion Resistance of Al2O3-13 wt% TiO2 Coatings. Processes 2020, 8, 1544. https://doi.org/10.3390/pr8121544

AMA Style

Szala M, Łatka L, Awtoniuk M, Winnicki M, Michalak M. Neural Modelling of APS Thermal Spray Process Parameters for Optimizing the Hardness, Porosity and Cavitation Erosion Resistance of Al2O3-13 wt% TiO2 Coatings. Processes. 2020; 8(12):1544. https://doi.org/10.3390/pr8121544

Chicago/Turabian Style

Szala, Mirosław; Łatka, Leszek; Awtoniuk, Michał; Winnicki, Marcin; Michalak, Monika. 2020. "Neural Modelling of APS Thermal Spray Process Parameters for Optimizing the Hardness, Porosity and Cavitation Erosion Resistance of Al2O3-13 wt% TiO2 Coatings" Processes 8, no. 12: 1544. https://doi.org/10.3390/pr8121544

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