Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

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

Version 1 : Received: 3 November 2020 / Approved: 4 November 2020 / Online: 4 November 2020 (16:01:50 CET)

How to cite: Szala, M.; Łatka, L.; Awtoniuk, M.; Winnicki, M.; Michalak, M. Neural Modelling of APS Thermal Spray Process Parameters for Optimising the Hardness, Porosity and Cavitation Erosion Resistance of Al2O3 -13% TiO2 Coatings. Preprints 2020, 2020110200 (doi: 10.20944/preprints202011.0200.v1). Szala, M.; Łatka, L.; Awtoniuk, M.; Winnicki, M.; Michalak, M. Neural Modelling of APS Thermal Spray Process Parameters for Optimising the Hardness, Porosity and Cavitation Erosion Resistance of Al2O3 -13% TiO2 Coatings. Preprints 2020, 2020110200 (doi: 10.20944/preprints202011.0200.v1).

Abstract

The study aims to elaborate a neural model and algorithm for optimising hardness and porosity of coatings and thus ensure that they have superior cavitation erosion resistance. Al2O3-13wt.%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 optimise both properties and cavitation erosion resistance of the coatings. Results were analysed 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 – maximising the hardness and cavitation erosion resistance of Al2O3-13%TiO2 coatings and, at the same time, minimizing their porosity.

Subject Areas

artificial neural network; APS; cavitation erosion; ceramic coatings; multi-objectives optimization; wear; hardness; microstructure; alumina – titania; Al2O3-13%TiO2.

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