OUR RESEARCH

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In this project, the goal is to use bio-composites for metallic components produced via Wire Arc Additive Manufacturing (WAAM). Humidity has a great impact on the properties of bio-composite materials. Moisture is absorbed by both constituents i.e., bio-polymeric matrices and also natural reinforcements. So, it is of paramount importance to develop computational models which can describe accurately the moisture diffusion process in these materials considering their heterogenous hierarchical microstructure.  We use both classical physics and machine learning techniques to develop highly accurate and computationally efficient multi-scale models. 

 Collaborator: Bernhard Mehlig and Giovanni Volpe (University of Gothenburg)  

 A wide variety of microstructural parameters, such as fibre volume fraction and fibre orientation distribution (among others) play an important role in the mechanical performance of short fibre reinforced (bio-)composites. Thus, different homogenization schemes have been extensively investigated in different micromechanical models. However, due to different challenges such as expensive calculations and difficult generation of microstructural samples, it is needed to develop more sophisticated methods. In this project, we are using micro-mechanical simulations and artificial neural networks for developing data-driven models for complex behaviour of these materials. 

 Composite laminates, composed of layers of woven laminas, have advantages compared to laminates made from unidirectional laminas, specifically in design and manufacturing. These materials are obtaining an increasing number of applications in structural components, due to superior mechanical performance compared to unidirectional laminates and improved delamination resistance. However, modelling woven composites is very challenging due to (i) existing two heterogenous subscales, namely mesoscale and microscale, (ii) the interlaced nature of yarns, which results in developed complex stress states. In this project, we are developing deep-learning-enhanced multi-scale models for woven composites using mean-field and high-fidelity full-field simulations and artificial neural networks. 

Collaborator: Martin Fagerström (Chalmers University of Technology)

Short fibre reinforced composites have high specific properties compared to pure matrices. Also, fabrication processes of these materials are efficient both time wise and cost-wise. As a result, an increasing trend in observed is usage of these materials in different industries. A large number of microstructural properties such as fibre volume fraction, fibre orientation distribution, fibre geometrical aspects etc. affect the macroscopic behaviour of these materials. Hence, to have an accurate structure-property relationship, it is crucial to take these parameters into account in the modelling process.

Collaborators: Martin Fagerström, Fredrik Larsson, and Magnus Ekh (Chalmers University of Technology)