OUR RESEARCH
Classical and data-driven modelling of moisture diffusion in bio-composite coatings
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.
Micromechanics-based deep-learning modelling of short fiber composites
A wide variety of microstructural parameters, such as fiber volume fraction and fiber orientation distribution (among others) play an important role in the mechanical performance of short fiber reinforced 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 behavior of these materials.
Multi-scale artificial neural networks for modelling woven composites
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.
Collaborators: Martin Fagerström
Micromechanical and multi-scale modelling of short fiber composites
Short fiber 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 fiber volume fraction, fiber orientation distribution, fiber geometrical aspects etc. affect the macroscopic behavior 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, Magnus Ekh (Chalmers University of Technology)