Functionally Graded Materials (FGMs) with Predictable and Controlled Gradient Profiles: Computational Modelling and Realisation

Giorgio Mattei, Annalisa Tirella, Arti Ahluwalia

    Research output: Contribution to journalArticlepeer-review


    Biological function is intricately linked with structure. Many bio- logical structures are characterised by functional spatially distributed gradients in which each layer has one or more specific functions to perform. Reproducing such structures is challenging, and usually an experimental trial-and-error approach is used. In this paper we investigate how the gravitational sedimentation of discrete solid particles (secondary phase) within a primary fluid phase with a time-varying dynamic viscosity can be used for the realisation of stable and reproducible con- tinuous functionally graded materials (FGMs). Computational models were used to simulate the distribution of a particle phase in a fluid domain. Firstly a model of particle sedimentation was implemented in order to predict the particle gradient profiles. Then the fluid domain was modelled as phase with time dependent viscos- ity. Experiments were then used to validate the computational results. The models show that selected composition gradients can be tailored by controlling fluid and particle properties. Using this method the gradient of a custom two-phase system can be designed and tailored in a simple fashion. Moreover this approach can also be employed for the fabrication of porous structures, using a porogen as settling particle. The method is particularly useful in tissue engineering applications, to first predict and then control biomaterial gradients without the use of complicated rapid prototyping or computer aided manufacturing systems.
    Original languageEnglish
    JournalComputer Modeling in Engineering & Sciences
    Issue number6
    Publication statusPublished - 2012


    Dive into the research topics of 'Functionally Graded Materials (FGMs) with Predictable and Controlled Gradient Profiles: Computational Modelling and Realisation'. Together they form a unique fingerprint.

    Cite this