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|Title:||Artificial neural network model of hardness, porosity and cavitation erosion wear of APS deposited Al2O3 -13 wt% TiO2 coatings||Authors:||Szala, M.
|Issue Date:||2021||Project:||project Lublin University of Technology—Regional Excellence Initiative, funded by the Polish Ministry of Science and Higher Education (contract No.030/RID/2018/19)||Serial title, monograph or event:||Journal of Physics: Conference Series||Volume:||1736||Issue:||1||Abstract:||The aim of the article is to build-up a simplified model of the effect of atmospheric plasma spraying process parameters on the deposits’ functional properties. The artificial neural networks were employed to elaborate on the model and the Matlab software was used. The model is crucial to study the relationship between process parameters, such as stand-off distance and torch velocity, and the properties of Al2O3-13 wt% TiO2 ceramic coatings. During this study, the coatings morphology, as well as its properties such as Vickers microhardness, porosity, and cavitation erosion resistance were taken into consideration. The cavitation erosion tests were conducted according to the ASTM G32 standard. Moreover, the cavitation erosion wear mechanism was presented. The proposed neural model is essential for establishing the optimisation procedure for the selection of the spray process parameters to obtain the Al2O3-13 wt% TiO2 ceramic coatings with specified functional properties||URI:||http://hdl.handle.net/10316/100818||ISSN:||1742-6588
|Appears in Collections:||I&D CEMMPRE - Artigos em Revistas Internacionais|
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