Stochastic Modeling of Multiphase Materials Based on Digital Image Data
- Multiphase materials combine properties of several materials, which makes them interesting for high-performing components. This thesis considers a certain set of multiphase materials, namely silicon-carbide (SiC) particle-reinforced aluminium (Al) metal matrix composites and their modelling based on stochastic geometry models. Stochastic modelling can be used for the generation of virtual material samples: Once we have fitted a model to the material statistics, we can obtain independent three-dimensional “samples” of the material under investigation without the need of any actual imaging. Additionally, by changing the model parameters, we can easily simulate a new material composition. The materials under investigation have a rather complicated microstructure, as the system of SiC particles has many degrees of freedom: Size, shape, orientation and spatial distribution. Based on FIB-SEM images, that yield three-dimensional image data, we extract the SiC particle structure using methods of image analysis. Then we model the SiC particles by anisotropically rescaled cells of a random Laguerre tessellation that was fitted to the shapes of isotropically rescaled particles. We fit a log-normal distribution for the volume distribution of the SiC particles. Additionally, we propose models for the Al grain structure and the Aluminium-Copper (\({Al}_2{Cu}\)) precipitations occurring on the grain boundaries and on SiC-Al phase boundaries. Finally, we show how we can estimate the parameters of the volume-distribution based on two-dimensional SEM images. This estimation is applied to two samples with different mean SiC particle diameters and to a random section through the model. The stereological estimations are within acceptable agreement with the parameters estimated from three-dimensional image data as well as with the parameters of the model.
Author: | Katharina Losch |
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URN: | urn:nbn:de:hbz:386-kluedo-53241 |
Advisor: | Claudia Redenbach |
Document Type: | Doctoral Thesis |
Language of publication: | English |
Date of Publication (online): | 2018/07/06 |
Year of first Publication: | 2018 |
Publishing Institution: | Technische Universität Kaiserslautern |
Granting Institution: | Technische Universität Kaiserslautern |
Acceptance Date of the Thesis: | 2017/07/17 |
Date of the Publication (Server): | 2018/07/06 |
Page Number: | X, 107 |
Faculties / Organisational entities: | Kaiserslautern - Fachbereich Mathematik |
DDC-Cassification: | 5 Naturwissenschaften und Mathematik / 510 Mathematik |
MSC-Classification (mathematics): | 62-XX STATISTICS |
Licence (German): | Creative Commons 4.0 - Namensnennung, nicht kommerziell, keine Bearbeitung (CC BY-NC-ND 4.0) |