LAPSE:2021.0546
Published Article
LAPSE:2021.0546
Neural Modelling of APS Thermal Spray Process Parameters for Optimizing the Hardness, Porosity and Cavitation Erosion Resistance of Al2O3-13 wt% TiO2 Coatings
June 21, 2021
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.
Keywords
Al2O3-13 wt% TiO2, alumina–titania, APS, artificial neural network, cavitation erosion, ceramic coatings, hardness, microstructure, multi-objective optimization, wear
Suggested Citation
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. (2021). LAPSE:2021.0546
Author Affiliations
Szala M: Department of Materials Engineering, Faculty of Mechanical Engineering, Lublin University of Technology, Nadbystrzycka 36D, 20-618 Lublin, Poland [ORCID]
Łatka L: Faculty of Mechanical Engineering, Wrocław University of Science and Technology, 5 Łukasiewicza Street, 50-371 Wrocław, Poland [ORCID]
Awtoniuk M: Institute of Mechanical Engineering, Warsaw University of Life Sciences, Nowoursynowska 164, 02-787 Warsaw, Poland [ORCID]
Winnicki M: Faculty of Mechanical Engineering, Wrocław University of Science and Technology, 5 Łukasiewicza Street, 50-371 Wrocław, Poland [ORCID]
Michalak M: Faculty of Mechanical Engineering, Wrocław University of Science and Technology, 5 Łukasiewicza Street, 50-371 Wrocław, Poland [ORCID]
Journal Name
Processes
Volume
8
Issue
12
Article Number
E1544
Year
2020
Publication Date
2020-11-26
Published Version
ISSN
2227-9717
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Original Submission
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PII: pr8121544, Publication Type: Journal Article
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LAPSE:2021.0546
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doi:10.3390/pr8121544
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Jun 21, 2021
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Jun 21, 2021
 
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Jun 21, 2021
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Original Submitter
Calvin Tsay
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