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Naji, M. |
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Motta, Antonella |
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Aletan, Dirar |
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Mohamed, Tarek |
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Ertürk, Emre |
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Taccardi, Nicola |
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Kononenko, Denys |
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Petrov, R. H. | Madrid |
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Alshaaer, Mazen | Brussels |
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Bih, L. |
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Casati, R. |
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Muller, Hermance |
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Kočí, Jan | Prague |
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Šuljagić, Marija |
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Kalteremidou, Kalliopi-Artemi | Brussels |
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Azam, Siraj |
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Ospanova, Alyiya |
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Blanpain, Bart |
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Ali, M. A. |
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Popa, V. |
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Rančić, M. |
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Ollier, Nadège |
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Azevedo, Nuno Monteiro |
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Landes, Michael |
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Rignanese, Gian-Marco |
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Belnoue, Jonathan P.-H.
University of Bristol
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (35/35 displayed)
- 2024An accurate forming model for capturing the nonlinear material behaviour of multilayered binder-stabilised fabrics and predicting fibre wrinklingcitations
- 2024That’s how the preform crumples: Wrinkle creation during forming of thick binder-stabilised stacks of non-crimp fabricscitations
- 2024Virtual data-driven optimisation for zero defect composites manufacturecitations
- 2024Parametric study on the effect of material properties, tool geometry, and tolerances on preform quality in wind turbine blade manufacturingcitations
- 2024Process models: A cornerstone to composites 4.0citations
- 2024But how can I optimise my high-dimensional problem with only very little data? – A composite manufacturing applicationcitations
- 2023A comprehensive modelling framework for defect prediction in automated fibre placement of composites
- 2023Thickness Control of Autoclave-Molded Composite Laminatescitations
- 2022Intelligent Composites Forming - Simulations For Faster, Higher Quality Manufacture
- 2022A MODELLING FRAMEWORK FOR THE EVOLUTION OF PREPREG TACK UNDER PROCESSING CONDITIONS
- 2022Understanding tack behaviour during prepreg-based composites’ processingcitations
- 2021On the physical relevance of power law-based equations to describe the compaction behaviour of resin infused fibrous materialscitations
- 2021Consolidation-driven wrinkling in carbon/epoxy woven fabric prepregscitations
- 2021Compaction behaviour of continuous fibre-reinforced thermoplastic composites under rapid processing conditionscitations
- 2021Modelling compaction behavior of toughened prepreg during automated fibre placement
- 2021Lab-based in-situ micro-CT observation of gaps in prepreg laminates during consolidation and curecitations
- 2021Hypo-viscoelastic modelling of in-plane shear in UD thermoset prepregscitations
- 2020Predicting consolidation-induced wrinkles and their effects on composites structural performancecitations
- 2020Experimental characterisation of the in-plane shear behaviour of UD thermoset prepregs under processing conditionscitations
- 2020A rapid multi-scale design tool for the prediction of wrinkle defect formation in composite componentscitations
- 2019Modelling of the in-plane shear behavior of uncured thermoset prepreg
- 2019A numerical study of variability in the manufacturing process of thick composite partscitations
- 2019Machine-driven experimentation for solving challenging consolidation problems
- 2019Mitigating forming defects by local modification of dry preformscitations
- 2018Modelling process induced deformations in 0/90 non-crimp fabrics at the meso-scalecitations
- 2018Experimental Characterisation of In-plane Shear Behaviour of Uncured Thermoset Prepregs
- 2018Multi-scale modelling of non-uniform consolidation of uncured toughened unidirectional prepregscitations
- 2016Predicting wrinkle formation in components manufactured from toughened UD prepreg
- 2016Understanding and prediction of fibre waviness defect generation
- 2016Cohesive/Adhesive failure interaction in ductile adhesive joints Part Icitations
- 2016An experimental investigation of the consolidation behaviour of uncured prepregs under processing conditionscitations
- 2015The compaction behaviour of un-cured prepregs
- 2012A numerical model for thick composite-metallic adhesive joints
- 2011Adaptive calibration of a nonlocal coupled damage plasticity model for aluminium alloy AA6082 T0
- 2007Modeling crack initiation and propagation in nickel base superalloys
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document
Adaptive calibration of a nonlocal coupled damage plasticity model for aluminium alloy AA6082 T0
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>