Mechanics and Physics of Materials
Short description
This group is primarily focused on mechanics and physics of micro-structurally heterogeneous materials. Micro-mechanical and coupled multi-scale models are developed to predict complex behavior of multi-phase materials, with an emphasis on (bio-)composites. To increase the predictive capability and to enhance the computational efficiency of the models, machine learning techniques, particularly artificial neural networks, are used. As an additional line of research activities, this group also works, in collaboration with other research groups, on bio-mechanics problems. Physics-based models are employed together with machine learning techniques to develop computationally enhanced bio-mechanical models.
News
Open PhD position
MSCA-DN project (DurAMat), “Deep-learning-enhanced multiscale modelling of moisture diffusion in bio-composites as coating for WAAM metal”. Application: https://duramat-project.eu/duramat/
PhD half-time seminar
Ehsan Ghane, “Multi-scale deep-learning for elastic and elasto-plastic behavior of woven composites”, June 16, 2023 (PJ Salen, Physics department) at 10:00.
Master thesis defense
Hon Lam Cheung, “Micromechanics-based artificial neural networks and transfer learning for modeling short fiber reinforced composites in automotive applications”, June 7, 2023 (von Bahr, Soliden, Physics department) at 14:00.
European grant application approved
April 2023: MSCA-DN application got approved. 10 PhD student positions will be defined within a consortium of European institutes and industries. 1 PhD student will join this group.
Special issue in the journal “Polymers”
Topic: Mechanics of 3D-Printed Polymers and Polymer Composites.
Guest editors: Mohsen Mirkhalaf and Mohammad Heidari-Rarani.
Deadline for manuscript submissions: August 10, 2022.
Master thesis presentation
Mehrdad Saaedi, “Predicting cancer tumor position in a liver using finite element analysis and artificial neural networks”, July 16, 2021 (online) at 10:00.
Master thesis defense
Johan Friemann, “Predicting the elasto-plastic response of short fiber composites using deep neural networks trained on micro-mechanical simulations”, February 11, 2021 (online) at 10:00.
Master thesis defense
Noah Mentges, “Micro-mechanical modelling of the effects of fibre length distributions on short fibre reinforced composites using orientation averaging”, December 16, 2020 (online) at 14:00.
Projects
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.
Related Publications
J. Friemann, B. Dashtbozorg, M. Fagerström, S.M. Mirkhalaf, (2023). A micromechanics-based recurrent neural networks model for path-dependent cyclic deformation of short fiber composites. International Journal of Numerical Methods in Engineering, 124:2292-2314.
N. Mentges, B. Dashtbozorg, S.M. Mirkhalaf, (2021). A micromechanics-based artificial neural networks model for elastic properties of short fiber composites. Composites Part B: Engineering, 108736.
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.
Related Publications
E. Ghane, M. Fagerström, S.M. Mirkhalaf, (2023). A multiscale deep learning model for elastic properties of woven composites. International Journal of Solids and Structures 282, 112452.
Collaborations
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)
Related Publications
N. Mentges, H. Celik, C. Hopmann, M. Fagerström, S.M. Mirkhalaf, (2023). Micromechanical modelling of short fibre composites considering fibre length distributions. Composites Part B: Engineering 264, 110868.
B. Castricum, M. Fagerström, M. Ekh, F. Larsson, S.M. Mirkhalaf, (2022). A computationally efficient coupled multi-scale model for short fiber reinforced composites. Composites Part A: Applied Science and Manufacturing 163, 107233.
S.M. Mirkhalaf, T.J.H. van Beurden, M. Ekh, F. Larsson, M. Fagerström, (2022). An FE-based orientation averaging model for elasto-plastic behavior of short fiber composites. International Journal of Mechanical Sciences 219, 107097.
S.M. Mirkhalaf, E.H. Eggels, T.J.H. van Beurden, F. Larsson, M. Fagerström, (2020). A finite element based orientation averaging method for predicting elastic properties of short fiber reinforced composites. Composites, Part B: Engineering 202, 108388.
Atherosclerosis is a medical condition which involves hardening and/or thickening of arteries' walls. To predict and analyze development of atherosclerosis, multi-physics models have been. However, one of the main challenges of these models is the associated computational cost. In this project, we are using artificial neural networks (ANNs), to enhance the computational efficiency of these models.
Collaborators
Meisam Soleimani (Leibniz Universität Hannover), Behdad Dashtbozorg (Netherlands Cancer Institute), and Mohammad Mirkhalaf (Queensland University of Technology)
Related Publication
M. Soleimani, B. Dashtbozorg, M. Mirkhalaf, S.M. Mirkhalaf, (2023). A multiphysics-based artificial neural networks model for atherosclerosis Heliyon 9, e17902.
Real-time predictions of the deformation behavior of an organ during surgery is a big challenge, in particular when large deformations are involved. In this project, we are using physics-based models (based on continuum mechanics and using finite element method) and artificial neural networks to develop a tool for predicting the position of a cancer tumor after deformation of a liver.
Collaborator
Behdad Dashtbozorg (Netherlands Cancer Institute)
Helmets are crucial safety equipment which prevent a bike rider from getting seriously injured in the event of an accident. Conventional manufacturing processes have been successfully used to fabricate helmets. But recently, 3D printing has gained a considerable interest for that purpose. This is due to (i) Its capability for printing very complex geometries, (ii) The possibility of customized printing in a timely and cost-wise manner. In this project, we are designing a bike helmet, manufacturing the helmet via 3D printing, and testing its response under impact loads.
Collaborator
Mohammad Heidari-Rarani (University of Isfahan)
For designing architecture materials, there are theoretically no limit to potential designs since unlimited interface and building block shapes can be used. This is absolutely interesting, since it provides the opportunity to design different architectures for different purposes. It is however required to understand the effect of these arbitrary shaped interfaces and building blocks on the performance of a hierarchically architectured material. In this project, we are using finite element analysis and artificial neural networks to develop computationally efficient numerical algorithms for analyzing the effect of arbitrary interfaces and building block shapes on the performance of architectured materials.
Collaborator
Mohammad Mirkhalaf (Queensland University of Technology)