EPJ Web of Conferences
Volume 6, 2010ICEM 14 – 14th International Conference on Experimental Mechanics
|Number of page(s)||2|
|Section||Biomaterials, Bio-compatible Materials and Biomechanics|
|Published online||10 June 2010|
Modeling of structural effects in biomedical elements after titanium oxidation in fluidized bed
Czestochowa University of Technology, 1 Faculty of Materials
Processing Technology and Applied Physics, Materials Engineering
Institute, Biomaterials and Surface Layer Research
Institute, Av. Armii
Krajowej 19, 42-200
1 e-mail: email@example.com
Oxidation is one of the most employed methods to improve titanium and its alloys properties especially due to medical application. This process like most of the thermochemical treatment processes substantially influences on the characteristic of surface layers and the same on its mechanical and useful properties. Oxide coatings produced during titanium oxidation were examined due to their composition identification. Titanium was oxidized in fluidized bed in temperature range between 500÷700°C. Microstructures of titanium with a visible oxide coating on its surface after thermochemical treatment and changes of grain size in core of titanium samples are described. Moreover Xray phase analysis of obtained oxides coatings was made as well as microhardness measurements of titanium surface layers after oxidation process. Finally, the surfaces of titanium after oxidation in fluidized bed were measured by Auger electron spectroscopy. All research results are used to built numerical model of oxidation process in fluidized. Titanium oxidation process in fluidized bed is very complicated, because changes of parameters are non linear characteristics. This fact and lack of mathematical algorithms describing this process makes modeling properties of titanium elements by traditional numerical methods difficult or even impossible. In this case it is possible to try using artificial neural network. Using neural networks for modeling oxidizing in fluidized bed is caused by several nets' features: non linear character, ability to generalize the results of calculations for data out of training set, no need for mathematical algorithms describing influence changes input parameters on modeling materials properties.
Key words: Titanium / Oxidation / Fluidized bed / Neural network / Biomaterials
© Owned by the authors, published by EDP Sciences, 2010
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