Materials Map

Discover the materials research landscape. Find experts, partners, networks.

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The Materials Map is an open tool for improving networking and interdisciplinary exchange within materials research. It enables cross-database search for cooperation and network partners and discovering of the research landscape.

The dashboard provides detailed information about the selected scientist, e.g. publications. The dashboard can be filtered and shows the relationship to co-authors in different diagrams. In addition, a link is provided to find contact information.

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The Materials Map is still under development. In its current state, it is only based on one single data source and, thus, incomplete and contains duplicates. We are working on incorporating new open data sources like ORCID to improve the quality and the timeliness of our data. We will update Materials Map as soon as possible and kindly ask for your patience.

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Naji, M.
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Belnoue, Jonathan P.-H.

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University of Bristol

in Cooperation with on an Cooperation-Score of 37%

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Publications (35/35 displayed)

  • 2024An accurate forming model for capturing the nonlinear material behaviour of multilayered binder-stabilised fabrics and predicting fibre wrinkling7citations
  • 2024That’s how the preform crumples: Wrinkle creation during forming of thick binder-stabilised stacks of non-crimp fabrics6citations
  • 2024Virtual data-driven optimisation for zero defect composites manufacture6citations
  • 2024Parametric study on the effect of material properties, tool geometry, and tolerances on preform quality in wind turbine blade manufacturing4citations
  • 2024Process models: A cornerstone to composites 4.05citations
  • 2024But how can I optimise my high-dimensional problem with only very little data? – A composite manufacturing application6citations
  • 2023A comprehensive modelling framework for defect prediction in automated fibre placement of compositescitations
  • 2023Thickness Control of Autoclave-Molded Composite Laminates5citations
  • 2022Intelligent Composites Forming - Simulations For Faster, Higher Quality Manufacturecitations
  • 2022A MODELLING FRAMEWORK FOR THE EVOLUTION OF PREPREG TACK UNDER PROCESSING CONDITIONScitations
  • 2022Understanding tack behaviour during prepreg-based composites’ processing19citations
  • 2021On the physical relevance of power law-based equations to describe the compaction behaviour of resin infused fibrous materials15citations
  • 2021Consolidation-driven wrinkling in carbon/epoxy woven fabric prepregs29citations
  • 2021Compaction behaviour of continuous fibre-reinforced thermoplastic composites under rapid processing conditions14citations
  • 2021Modelling compaction behavior of toughened prepreg during automated fibre placementcitations
  • 2021Lab-based in-situ micro-CT observation of gaps in prepreg laminates during consolidation and cure20citations
  • 2021Hypo-viscoelastic modelling of in-plane shear in UD thermoset prepregs20citations
  • 2020Predicting consolidation-induced wrinkles and their effects on composites structural performance16citations
  • 2020Experimental characterisation of the in-plane shear behaviour of UD thermoset prepregs under processing conditions36citations
  • 2020A rapid multi-scale design tool for the prediction of wrinkle defect formation in composite components27citations
  • 2019Modelling of the in-plane shear behavior of uncured thermoset prepregcitations
  • 2019A numerical study of variability in the manufacturing process of thick composite parts28citations
  • 2019Machine-driven experimentation for solving challenging consolidation problemscitations
  • 2019Mitigating forming defects by local modification of dry preforms27citations
  • 2018Modelling process induced deformations in 0/90 non-crimp fabrics at the meso-scale30citations
  • 2018Experimental Characterisation of In-plane Shear Behaviour of Uncured Thermoset Prepregscitations
  • 2018Multi-scale modelling of non-uniform consolidation of uncured toughened unidirectional prepregs1citations
  • 2016Predicting wrinkle formation in components manufactured from toughened UD prepregcitations
  • 2016Understanding and prediction of fibre waviness defect generationcitations
  • 2016Cohesive/Adhesive failure interaction in ductile adhesive joints Part I29citations
  • 2016An experimental investigation of the consolidation behaviour of uncured prepregs under processing conditions54citations
  • 2015The compaction behaviour of un-cured prepregscitations
  • 2012A numerical model for thick composite-metallic adhesive jointscitations
  • 2011Adaptive calibration of a nonlocal coupled damage plasticity model for aluminium alloy AA6082 T0citations
  • 2007Modeling crack initiation and propagation in nickel base superalloyscitations

Places of action

Chart of shared publication
Lindgaard, Esben
3 / 21 shared
Bak, Brian L. V.
3 / 3 shared
Hallett, Stephen R.
32 / 270 shared
Thompson, Adam J.
8 / 13 shared
Broberg, Peter H.
3 / 3 shared
Krogh, Christian
1 / 19 shared
Chen, Siyuan
3 / 3 shared
Tretiak, Iryna
1 / 8 shared
Wang, Yi
8 / 27 shared
Dodwell, Tim J.
1 / 1 shared
Ivanov, Dmitry S.
17 / 31 shared
Mahapatra, Sarthak
3 / 5 shared
Gongadze, Ekaterina
1 / 4 shared
Dighton, Chris
1 / 1 shared
Nash, Gregory
1 / 1 shared
Moss, Martin
1 / 1 shared
Hemingway, Brett
1 / 1 shared
Dodwell, Timothy
1 / 5 shared
Valverde, Mario A.
2 / 3 shared
Sun, Xiaochuan
1 / 1 shared
Onoufriou, Maria
1 / 1 shared
Mehrabadi, Armin Rashidi
1 / 1 shared
Milani, Abbas S.
1 / 4 shared
Rashidi, Armin
1 / 3 shared
Milani, Abbas
1 / 2 shared
Kawashita, Luiz F.
1 / 24 shared
Kupfer, Robert
1 / 60 shared
Gude, Mike
1 / 775 shared
Kratz, James
8 / 46 shared
Galvez-Hernandez, Pedro
1 / 1 shared
Potter, Kevin
2 / 41 shared
Pickard, Laura Rhian
1 / 10 shared
Varkonyi, Balazs
1 / 1 shared
Chea, Ming Kai
1 / 1 shared
Jones, I. A.
1 / 6 shared
Long, A. C.
1 / 9 shared
Matveev, M. Y.
1 / 2 shared
Ivanov, D. S.
1 / 6 shared
Nixon-Pearson, Oliver J.
6 / 12 shared
Nixon-Pearson, O. J.
1 / 4 shared
Hallett, S. R.
1 / 11 shared
Belnoue, J. P.-H.
2 / 4 shared
Georgilas, I.
1 / 1 shared
Koptelov, Anatoly
1 / 3 shared
Turk, Mark A.
1 / 1 shared
Vermes, Bruno
1 / 1 shared
Said, Bassam El
1 / 7 shared
Kim, Byung Chul
1 / 20 shared
Advani, S. G.
1 / 8 shared
Binetruy, C.
1 / 13 shared
Syerko, E.
1 / 3 shared
Comas-Cardona, S.
1 / 5 shared
Leygue, A.
1 / 3 shared
Sorba, G.
1 / 2 shared
Mesogitis, Tassos
2 / 4 shared
Partridge, Ivana K.
1 / 25 shared
Potter, K. D.
1 / 7 shared
Korsunsky, A. M.
1 / 18 shared
Walsh, Michael J.
1 / 1 shared
Dini, Daniele
1 / 7 shared
Prakash, Leo D. G.
1 / 1 shared
Korsunsky, Alexander M.
1 / 32 shared
Song, Xu
1 / 2 shared
Chart of publication period
2024
2023
2022
2021
2020
2019
2018
2016
2015
2012
2011
2007

Co-Authors (by relevance)

  • Lindgaard, Esben
  • Bak, Brian L. V.
  • Hallett, Stephen R.
  • Thompson, Adam J.
  • Broberg, Peter H.
  • Krogh, Christian
  • Chen, Siyuan
  • Tretiak, Iryna
  • Wang, Yi
  • Dodwell, Tim J.
  • Ivanov, Dmitry S.
  • Mahapatra, Sarthak
  • Gongadze, Ekaterina
  • Dighton, Chris
  • Nash, Gregory
  • Moss, Martin
  • Hemingway, Brett
  • Dodwell, Timothy
  • Valverde, Mario A.
  • Sun, Xiaochuan
  • Onoufriou, Maria
  • Mehrabadi, Armin Rashidi
  • Milani, Abbas S.
  • Rashidi, Armin
  • Milani, Abbas
  • Kawashita, Luiz F.
  • Kupfer, Robert
  • Gude, Mike
  • Kratz, James
  • Galvez-Hernandez, Pedro
  • Potter, Kevin
  • Pickard, Laura Rhian
  • Varkonyi, Balazs
  • Chea, Ming Kai
  • Jones, I. A.
  • Long, A. C.
  • Matveev, M. Y.
  • Ivanov, D. S.
  • Nixon-Pearson, Oliver J.
  • Nixon-Pearson, O. J.
  • Hallett, S. R.
  • Belnoue, J. P.-H.
  • Georgilas, I.
  • Koptelov, Anatoly
  • Turk, Mark A.
  • Vermes, Bruno
  • Said, Bassam El
  • Kim, Byung Chul
  • Advani, S. G.
  • Binetruy, C.
  • Syerko, E.
  • Comas-Cardona, S.
  • Leygue, A.
  • Sorba, G.
  • Mesogitis, Tassos
  • Partridge, Ivana K.
  • Potter, K. D.
  • Korsunsky, A. M.
  • Walsh, Michael J.
  • Dini, Daniele
  • Prakash, Leo D. G.
  • Korsunsky, Alexander M.
  • Song, Xu
OrganizationsLocationPeople

document

Adaptive calibration of a nonlocal coupled damage plasticity model for aluminium alloy AA6082 T0

  • Korsunsky, A. M.
  • Belnoue, Jonathan P.-H.
Abstract

<p>Continuum Damage Mechanics (CDM) accounts for material degradation (softening and ultimately failure) by modifying the load-bearing properties of the material (stiffness and strength) through a special state variable referred to as damage. Damage is typically represented by a scalar or a higher dimension object (such as vector or tensor) with values between zero for virgin material and unity for the material that lost all its bearing capacity. Considered in this way, damage becomes an additional field quantity that needs to be considered along with strain and stress, and can be computed either incrementally, or as a certain function of a suitable physical parameter such as inelastic strain. The advantage of enriching the formulation of a continuum deformation problem with a damage parameter is that it allows considering the material post-critical behaviour, i.e. its response under deformations exceeding those when the maximum load-bearing capacity is reached. Typically, this post-critical behaviour is associated with strain localisation, initiation, growth and interaction of discontinuities, and final fracture. Within the CDM framework, cracks are represented by diffuse regions of material damaged so that it lost all its strength in at least one direction. Computationally, modelling the post-critical (softening) behaviour of material represents a challenge in terms of the numerical stability of algorithms. Nonlocal description of damage appears to offer a rational route towards stable modelling. Nonlocal averaging of the plastic strain for the evaluation of damage also renders CDM models independent of the mesh size and orientation, and helps overcome numerical instabilities. The formulation that emerges can be referred to as coupled nonlocal damage-plasticity modelling [1, 2]. An important challenge remains, however, in developing this general approach into a flexible and material-specific modelling tool. This concerns the need to calibrate a large number of material parameters that emerge in this formulation. In order to address this challenge, recently we developed an approach for the calibration of CDM models of ductile materials that we propose to refer to as adaptive calibration. The calibration of the damage function is accomplished by matching the model prediction to the experimental data obtained from a single tensile test with multiple gauge length extensometry [3] used to capture strain localisation and size effects. We describe the application and validation of this approach to the damage function parameter calibration for the aluminium alloy AA 6082 T0. Excellent agreement with experimental measurements is obtained.</p>

Topics
  • impedance spectroscopy
  • polymer
  • aluminium
  • crack
  • strength
  • aluminium alloy
  • plasticity