| 1. |
- Alzweighi, Mossab, et al.
(författare)
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Anisotropic damage behavior in fiber-based materials : Modeling and experimental validation
- 2023
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Ingår i: Journal of the mechanics and physics of solids. - : Elsevier BV. - 0022-5096 .- 1873-4782. ; 181
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Tidskriftsartikel (refereegranskat)abstract
- This study presents a thermodynamically consistent continuum damage model for fiber-based materials that combines elastoplasticity and damage mechanisms to simulate the nonlinear mechanical behavior under in-plane loading. The anisotropic plastic response is characterized by a non-quadratic yield surface composed of six sub-surfaces, providing flexibility in defining plastic properties and accuracy in reproducing material response. The damage response is modeled based on detailed uniaxial monotonic and cyclic tension-loaded experiments conducted on specimens extracted from a paper sheet in various directions. To account for anisotropic damage, we propose a criterion consisting of three sub-surfaces representing tension damage in the in-plane material principal directions and shear direction, where the damage onset is determined through cyclic loading tests. The damage evolution employs a normalized fracture energy concept based on experimental observation, which accommodates an arbitrary uniaxial loading direction. To obtain a mesh-independent numerical solution, the model is regularized using the implicit gradient enhancement by utilizing the linear heat equation solver available in commercial finite-element software. The study provides insights into the damage behavior of fiber-based materials, which can exhibit a range of failure modes from brittle-like to ductile, and establishes relationships between different length measurements.
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| 2. |
- Alzweighi, Mossab, et al.
(författare)
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Evaluation of Hoffman and Xia plasticity models against bi-axial tension experiments of planar fiber network materials
- 2022
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Ingår i: International Journal of Solids and Structures. - : Elsevier BV. - 0020-7683 .- 1879-2146. ; 238
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Tidskriftsartikel (refereegranskat)abstract
- The anisotropic properties and pressure sensitivity are intrinsic features of the constitutive response of fiber network materials. Although advanced models have been developed to simulate the complex response of fibrous materials, the lack of comparative studies may lead to a dubiety regarding the selection of a suitable method. In this study, the pressure-sensitive Hoffman yield criterion and the Xia model are implemented for the plane stress case to simulate the mechanical response under a bi-axial loading state. The performance of both models is experimentally assessed by comparison to bi-axial tests on cruciform-shaped specimens loaded in different directions with respect to the material principal directions. The comparison with the experimentally measured forces shows the ability of the Hoffman model as well as the Xia model with shape parameter k≤2 to adequately predict the material response. However, this study demonstrates that the Xia model consistently presents a stiffer bi-axial response when k≥3 compared to the Hoffman model. This result highlights the importance of calibrating the shape parameter k for the Xia model using a bi-axial test, which can be a cumbersome task. Also, for the same tension-compression response, the Hill criterion as a special case of the Hoffman model presents a good ability to simulate the mechanical response of the material for bi-axial conditions. Furthermore, in terms of stability criteria, the Xia model is unconditionally convex while the convexity of the Hoffman model is a function of the orthotropic plastic matrix. This study not only assesses the prediction capabilities of the two models, but also gives an insight into the selection of an appropriate constitutive model for material characterization and simulation of fibrous materials. The UMAT implementations of both models which are not available in commercial software and the calibration tool of the Xia model are shared with open-source along with this work.
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| 3. |
- Alzweighi, Mossab
(författare)
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Modelling Fiber Network Materials:Micromechanics, Constitutive Behaviour and AI
- 2023
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- This thesis focuses on understanding the mechanical behavior of fiber-based materials by utilizing various modeling approaches. Particular emphasis is placed on their structural variability, anisotropic properties, and damage behavior. Furthermore, the study explores moisture diffusion phenomena within these materials, leveraging machine learning techniques. The research employs a blend of multiscale modeling, experimental investigation, machine learning, and continuum modeling to enhance the predictive capabilities for modelling fiber-based materials.In Paper I, the work investigates the impact of stochastic variations in the structural properties of thin fiber networks on their mechanical performance. A multiscale approach that includes modeling, numerical simulation, and experimental measurements is proposed to assess this relationship. The research also considers the influence of drying conditions during production on fiber properties. The study finds that spatial variability in density has a significant impact on local strain fields, while fiber orientation angle with respect to drying restraints is a key influencer of the mechanical response. In Paper II, the research delves into the investigation of anisotropic properties and pressure sensitivity of fiber network materials. It draws a comparison between the Hoffman yield criterion and the Xia model, which are widely utilized for simulating the mechanical response in fiber-based materials. The study performs a detailed analysis of these models under bi-axial loading conditions, assessing their numerical stability and calibration flexibility. Further supporting the research community, the paper provides open-source access to the user material implementations of both models and introduces a calibration tool specifically for the Xia model, thereby promoting ease of usage and facilitating further research in this domain. In Paper III a novel thermodynamically consistent continuum damage model for fiber-based materials is introduced. Through the integration of elastoplasticity and damage mechanisms, the model employs non-quadratic surfaces comprised of multi sub-surfaces, augmented with an enhanced gradient damage approach. The model’s capability is demonstrated by predicting the nonlinear mechanical behavior under in-plane loading. This study provides valuable insights into the damage behavior of fiber-based materials, showcasing a range of failure modes from brittle-like to ductile. In Paper IV, the study examines moisture penetration in fiber-based materials and the resultant out-of-plane deformation, known as curl deformation, using a combination of traditional experiments, machine learning techniques, and continuum modeling. The paper compares the effectiveness of two machine learning models, a Feedforward Neural Network (FNN) and a Recurrent Neural Network (RNN), in predicting the gradient of the moisture profile history. The study finds that the RNN model, which accounts for temporal dependencies, provides superior accuracy. The predicted gradient moisture profile enables simulating the curl response, offering a deeper understanding of the relationship between moisture penetration and paper curling.
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| 4. |
- Alzweighi, Mossab, et al.
(författare)
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Predicting moisture penetration dynamics in paper with machine learning approach
- 2024
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Ingår i: International Journal of Solids and Structures. - : Elsevier BV. - 0020-7683 .- 1879-2146. ; 288, s. 112602-
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Tidskriftsartikel (refereegranskat)abstract
- In this work, we predicted the gradient of the deformational moisture dynamics in a sized commercial paper by observing the curl deformation in response to the one-sided water application. The deformational moisture is a part of the applied liquid which ends up in the fibers causing swelling and subsequent mechanical response of the entire fiber network structure. The adapted approach combines traditional experimental procedures, advanced machine learning techniques and continuum modeling to provide insights into the complex phenomenon relevant to ink-jet digital printing in which the sized and coated paper is often used, meaning that not all the applied moisture will reach the fibers. Key material properties including elasticity, plastic parameters, viscoelasticity, creep, moisture dependent behavior, along with hygroexpansion coefficients are identified through extensive testing, providing vital data for subsequent simulation using a continuum model. Two machine learning models, a Feedforward Neural Network (FNN) and a Recurrent Neural Network (RNN), are probed in this study. Both models are trained using exclusively numerically generated moisture profile histories, showcasing the value of such data in contexts where experimental data acquisition is challenging. These two models are subsequently utilized to predict moisture profile history based on curl experimental measurements, with the RNN demonstrating superior accuracy due to its ability to account for temporal dependencies. The predicted moisture profiles are used as inputs for the continuum model to simulate the associated curl response comparing it to the experiment representing “never seen” data. The result of comparison shows highly predictive capability of the RNN. This study melds traditional experimental methods and innovative machine learning techniques, providing a robust technique for predicting moisture gradient dynamics that can be used for both optimizing the ink solution and paper structure to achieve desirable printing quality with lowest curl propensities during printing.
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| 5. |
- Alzweighi, Mossab, et al.
(författare)
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The influence of structural variations on the constitutive response and strain variations in thin fibrous materials
- 2021
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Ingår i: Acta Materialia. - : Elsevier BV. - 1359-6454 .- 1873-2453. ; 203
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Tidskriftsartikel (refereegranskat)abstract
- The stochastic variations in the structural properties of thin fiber networks govern to a great extent their mechanical performance. To assess the influence of local structural variability on the local strain and mechanical response of the network, we propose a multiscale approach combining modeling, numerical simulation and experimental measurements. Based on micro-mechanical fiber network simulations, a continuum model describing the response at the mesoscale level is first developed. Experimentally measured spatial fields of thickness, density, fiber orientation and anisotropy are thereafter used as input to a macroscale finite-element model. The latter is used to simulate the impact of spatial variability of each of the studied structural properties. In addition, this work brings novelty by including the influence of the drying condition during the production process on the fiber properties. The proposed approach is experimentally validated by comparison to measured strain fields and uniaxial responses. The results suggest that the spatial variability in density presents the highest impact on the local strain field followed by thickness and fiber orientation. Meanwhile, for the mechanical response, the fiber orientation angle with respect to the drying restraints is the key influencer and its contribution to the anisotropy of the mechanical properties is greater than the contribution of the fiber anisotropy developed during the fiber sheet-making.
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