Vitrimers have attracted significant attention in recent years as promising alternatives to epoxies and other conventional thermosets. Their dynamic covalent bond-exchange reactions enable healing of cracks and delaminations, as well as reprocessability, while preserving the underlying crosslinked network. These features are particularly appealing for aerospace materials, where epoxy matrix composites dominate. While numerous recent studies investigate delamination healing in vitrimer-based composites, far fewer studies have examined their healing performance in architected materials with intricate geometries, such as cellular solids. These engineered structures can exhibit mechanical properties unattainable in natural materials, yet their low relative density makes them susceptible to crack initiation and propagation. Integrating vitrimers into such architectures therefore offers a promising route toward damage-tolerant lightweight systems.

This work investigates the healing performance of an adipic acid vitrimer integrated into two distinct 2D cellular architectures. Specimens were manufactured via waterjet cutting and subjected to cyclic loading to observe crack initiation and propagation throughout the lattice. Subsequent healing cycles were performed using a mold and heat press system to apply the pressure required for crack-face closure and bond-exchange activation. The more compliant architecture accumulated greater damage and was consequently more challenging to heal, whereas the stiffer design recovered its stiffness more readily. Overall, the results demonstrate the feasibility of incorporating vitrimers and other self-healing polymers into foams and metamaterials while highlighting the process-design considerations and limitations that must be addressed to achieve reliable mechanical recovery. ... Read more

Architected truss metamaterials are ultra-light materials built from repeating networks of small, interconnected beams. By tailoring this internal geometry, they can achieve combinations of stiffness and energy absorption that are difficult to obtain with conventional materials or foams, particularly at very low density.

Previous research has shown that their mechanical performance depends not only on the beams themselves, but also on parameters such as material choice, manufacturing method, lattice architecture, and the shape of the joints where beams meet. This thesis extends existing joint-resolved modeling approaches beyond the linear elastic range into the nonlinear regime, where large deformations and buckling become important. To do so, it develops a hybrid finite element framework in which joints are represented by detailed 3D solid elements and struts by efficient beam elements, combining improved physical realism with manageable computational cost. The thesis presents a nonlinear solver, validation metrics, an accuracy and computational efficiency study, and outlines future developments toward monolithic and data-driven modeling approaches. ... Read more

Classical mechanochemical kinetics do not explain the persistent and history-dependent fluorescence patterns observed in spiropyran (SP) to merocyanine (MC) transitions within elastomers. This thesis addresses this gap by developing a dual-imaging methodology that integrates Digital Image Correlation (DIC) with fluorescence imaging to establish a reproducible correlation between local strain fields and mechanophore activation. The approach employs synchronized acquisition under alternating green and near-infrared illumination, enabling co-registered speckle and fluorescence images at identical mechanical states. A custom-built hardware and software was implemented, combining automated stage control, LED modulation, and image capture within a unified interface. This framework ensures temporal and spatial alignment, while allowing full-field strain analysis and fluorescence quantification across identical deformation steps. The developed methodology demonstrates that fluorescence activation systematically coincides with regions of strain localization. While low-strain sensitivity is constrained by illumination artifacts, the integrated imaging provides robust quantitative correlations at higher strains. Beyond the immediate results, the framework establishes a transferable platform for testing mechanophores as molecular stress sensors, with applications in diagnostics, materials design, and the broader study of mechanochemistry in soft matter. ... Read more

Phase separation plays a crucial role in the development of microstructures in multiphase materials, influencing their functional properties. This thesis addresses the inverse problem of inferring the free-energy function directly from microstructures developed by diffusion-induced phase separation. The Cahn–Hilliard equation is employed to generate a dataset of two-dimensional phase-field simulations, with the free-energy function parameterized using 5 B-spline control points. A diverse set of microstructural descriptors is implemented, including statistical descriptor (two-point correlation function), topological descriptors (Betti numbers), geometric metrics (edge length, curvature, pore size), and black-box features extracted from a pre-trained ResNet50V2. Neural network models are trained to map these descriptors to the underlying free-energy parameters. Quantitative evaluation shows that the combined descriptor model achieved accurate predictions, while SHAP analysis confirms the physical relevance of the learned feature–to-parameter relationships. Qualitative comparisons of predicted free-energy functions and re-constructed microstructures demonstrates that the re-constructed morphologies closely reproduce the characteristic patterns of the target morphologies. This study demonstrates the feasibility of descriptor-based machine learning frameworks for free-energy inference, providing a foundation for future applications in data-driven materials design and the analysis of experimental microscopic images. ... Read more

Accurate constitutive modeling of hyperelastic materials remains a challenging task due to their inherently nonlinear and complex stress–strain behavior. Traditional phenomenological models often fall short in capturing this complexity, particularly in modern engineering materials with rich mechanical responses. In recent decades, data-driven modeling approaches have emerged as promising alternatives, offering flexibility in learning material behavior directly from data. Multi-Layer Perceptrons (MLPs), in particular, have become widely adopted due to their universal approximation capabilities. Despite their benefits, MLP-based approaches face significant limitations. Their "blackbox" nature limits interpretability and restricts insights into underlying material mechanics. Furthermore, although MLPs with fixed activation functions can approximate hyperelastic behavior in theory, their limited smoothness, such as in the case of ReLU, can restrict accurate representation of derivatives essential for modeling material responses. These shortcomings highlight the need for alternative frameworks that can represent material behavior more accurately and transparently. An emerging alternative is the Kolmogorov-Arnold Network (KAN), which offers improved interpretability and greater flexibility due to its architecture. By leveraging the Kolmogorov-Arnold representation theorem, KANs decompose complex functions into simpler, easy-to-understand components. WhileKANs have shown promise in various applications, including material modeling, their use in hyperelasticity remains limited due to challenges in ensuring physically consistent predictions. Current KAN-based frameworks cannot guarantee physically valid hyperelastic modeling. To address these challenges, this work introduces a novel Input-Convex Kolmogorov-Arnold Network (ICKAN) architecture tailored for hyperelastic constitutive modeling. The ICKAN model employs spline-based, learnable activation functions to capture material nonlinearities and explicitly incorporates convexity and monotonicity constraints to ensure adherence to physical principles. Validation using benchmark datasets demonstrates that ICKAN accurately predicts hyperelastic stress–strain behavior across a range of loading conditions. By enhancing interpretability and ensuring physically consistent predictions, the proposed ICKAN framework provides a robust and transparent solution, underscoring the broader potential of KANs in data-driven constitutive modeling. ... Read more

Adhesives have played a vital role throughout human history. Studying their composition and production methods offers insight into past technologies and helps reconstruct historical practices. This study focuses on the materials science analysis of Betula sp. (birch) bark tar, a widely used adhesive in prehistory times. By examining its molecular composition and production techniques, this research seeks to replicate ancient manufacturing methods using experimentally produced samples.
In this study, Gas Chromatography-Mass Spectrometry (GC/MS) was employed to analyze the chemical composition of the adhesives. To classify different production methods, machine learning techniques—including Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and K-Nearest Neighbors (KNN)—were applied. The results indicate that LDA successfully differentiates between production techniques, suggesting its potential for identifying variations in tar preparation. However, since this study is based on experimentally produced samples, its application to archaeological specimens requires further validation. ... Read more

Understanding how microstructural architecture governs macroscopic mechanical behavior is central to multiscale materials design, yet existing microstructure-informed workflows either rely on extensive experimental studies or costly microstructure-homogenization simulations. This thesis develops a unified, data-driven constitutive modeling framework that directly maps continuous two-phase microstructure to linear and nonlinear effective responses using statistical descriptors, bypassing reconstruction and generalizing across diverse morphologies. We first construct Micro3D, a statistically diverse synthetic dataset of binary microstructures using Gaussian random fields and multiple morphology generators, from which two-point statistics are extracted and compressed to serve as compact, physics-meaningful inputs. For the linear regime, a two-branch multilayer perceptron (MLP) is constructed with embedded symmetry and positive-definiteness constraints, using a Cholesky-based representation to predict the effective tensor. For the nonlinear regime, a hybrid framework combining a three-branch architecture, a hypernetwork, and an input-convex neural network (ICNN) is developed to capture complex material behaviors. Both models demonstrate strong generalization to unseen microstructures, with the nonlinear model accurately reproducing responses under previously unseen loading paths. Together, these components provide a practical route to microstructure-informed surrogate models that are interpretable, extensible, and suitable for downstream simulation. ... Read more

Properties of highly ordered crystalline materials, like strength and ductility, are dependent on the preferred orientations of the grains within the material, that is, the texture. When a material is processed and microstructural transformations occur, the texture of the material changes drastically. These texture evolutions are presently simulated using Crystal Plasticity Finite Element Methods (CPFEM). While these simulations are precise, they are computationally expensive and slow.

A surrogate for such simulation methods that can benefit from a data-driven approach could be deep learning through artificial neural networks. The aim of this study is to leverage a suitable deep learning neural network model and assess its ability to capture the complexity of texture. As such, Normalizing Flows (NF), a generative deep learning model, is employed to learn textures by capturing the multi-modal distribution of discrete sets of orientation matrices that represent those textures. The performance of the model is assessed for two types of data, matrices that belong to a fixed texture (unconditional modelling) and matrices paired with simple conditions (conditional modelling) that produce textures based on the conditions. The training data for the neural network is synthetically generated in a microstructure modelling software. The model is trained on this synthetic data and subsequently used to generate samples on the distribution it has learned. These samples and ground truth data are plotted in pole figures to compare the distribution of the generated data and the ground truth data, respectively, to visualize the model’s generative capability.

It is found that the model closely approximates the distribution of a given texture, capturing the structure of the distribution, albeit having a reduced density in the modes of the distribution. The conditional model is also capable of generating the approximate texture relevant to the condition given to it during evaluation. These findings indicate that the model is capable of learning texture, and further improvements in the model’s architecture could make the model highly robust. ... Read more

Deep learning has revolutionized scientific and engineering applications by enabling fast, data-driven predictions and optimizations. In materials science, however, its impact is limited by complex structure–property relationships, sparse high-quality data, and the need to respect fundamental physical laws. Overcoming these challenges calls for machine learning frameworks that learn effectively from limited data while producing results that are physically meaningful, interpretable, and actionable.
This thesis develops physics-guided machine learning approaches to accelerate the design, modeling, and interpretation of materials across scales and material classes. It introduces neural network architectures for metamaterial design that learn directly from data while remaining physically consistent. These models retrieve structural designs that achieve specified target properties instantly, enabling rapid exploration of the structure–property landscape and supporting scenarios with multiple design goals more efficiently than traditional optimization methods. For material modeling, this work addresses the limitation that stress fields are not directly accessible in experiments. The proposed frameworks instead learn constitutive laws from measurable quantities such as displacements and forces, while preserving essential physical principles such as thermodynamic consistency. This work further demonstrates how data-driven approaches reveal previously unknown structure–property relationships, such as the role of dangling atomic masses in the thermal transport of crystalline nanoporous materials. Finally, it introduces a chemistry-constrained generative framework that proposes synthesizable, diverse, and novel molecular structures using limited data, while providing interpretable representations of the molecular generation.
Together, these contributions establish that physics-guided machine learning can complement and extend traditional materials science by delivering reliable, interpretable, and generalizable solutions to longstanding challenges in design and modeling. ... Read more

When designing mechanical components, they commonly undergo multiple modeling phases for stress determination using analytical or numerical methods like the Finite Element Method (FEM). This is followed by experimental validation performed via stress mapping to identify and account for possible mechanical failure within the design phase. Among the experimental stress mapping techniques, mechanosensing is gaining rapidly increasing attention by research and industry. Mechanosensing is a chemistry-based technique that utilizes molecules called mechanophores. When mechanophores are embedded in transparent polymers, they act as mechanical probes sensing stress/strain throughout the polymer by emitting fluorescence under deformation. While studies have shown the capabilities of mechanophores as stress/strain probes qualitatively, it is currently not known how the mechanophore activation is correlated with the stress/strain-based quantities from a solid mechanics perspective. This study addresses this problem from a phenomenological viewpoint to fill the research area gap. In this work, using a uniaxial tensile testor, experiments are conducted on a Polydimethylsiloxane (PDMS) with a mechanophore spiropyran embedded homogeneously in the bulk of the polymer. The fluorescence data captured in the tests is correlated with the numerically obtained continuum mechanics stress/strain quantities. These correlations will be useful in giving directions towards research in the fundamental understanding of the mechanics of mechanophores, thereby bridging the gap between chemistry and mechanics of mechanosensors. This will pave the way towards optical-only in-situ measurements of stress-strain behavior. ... Read more

Torpedo ladle cars play a role in transporting hot metal within steel plants, and optimizing their operation is crucial for reducing energy consumption and CO2 emissions.This study investigates the feasibility of developing a digital twin for simulating the thermal management of torpedo ladle cars at Tata Steel by combining reduced-order models with machine learning techniques. Four distinct digital twin models were developed and evaluated: Random forest combined with singular value decomposition (SVD) (RF-SVDmodel), neural network combined with SVD (NN-SVDmodel), neural network without SVD (NN-model), and recurrent neural network without SVD (RNN-model). These models generalization abilities were evaluated using a learning curve or K-fold cross validation. The models accuracy was
tested using validation datasets of a full cycle prediction of the torpedo ladle car process. The RMSE and R2 of the validation prediction of each model were obtained and compared. Results show that RF-SVDmodel, NN-SVDmodel and RNN-model exhibit some challenges such as potential overfitting and inconsistency in performance. The NN-model is the most promising option with robust performance, high generalization abilities, and competitive accuracy to the AnsysTwinbuilder. ... Read more

A novel surrogate model to approximate microscopic behaviour and accelerate concurrent multiscale finite element simulations is proposed. The study serves as a proof of concept, focusing exclusively on 2D, geometric non-linear lattice materials. Despite numerous successful implementations of surrogate modelling techniques in literature, challenges remain, mainly with the black-box nature of most of these models, suffering from lack of interpretability. To tackle these issues, this study reintroduces physics into the model through the use of beam theory in so-called Beam Neural Networks. These networks are tested against a benchmark feed-forward neural network in both interpolation and extrapolation. Although the findings do not satisfy the requirements for practical application, they do indicate that the introduction of beam theory to the model has improved the model's extrapolation ability, suggesting that the proposal has improved robustness and interpretability of the model. Given further optimization, there is promise of Beam Neural Networks to become an useful tool to accelerate concurrent multiscale modelling in the future. ... Read more

Piezoelectric metamaterials, recognized for their remarkable electromechanical coupling, have garnered significant attention for their ability to generate artificial nonzero piezoelectric coefficients beyond those of natural ceramics. Despite being a relatively nascent field, research in piezoelectric metamaterials is expanding rapidly, driven by their potential applications in dynamic environments, including vibration-dominated systems. This study advances understanding in this field by focusing on the wave dispersion characteristics of 2D piezoelectric truss metamaterials.

Using a multiphysics finite element framework based on Euler-Bernoulli beam theory, we develop a robust model to capture 3D deformation and apply the Wave Finite Element Method with Bloch boundary conditions to analyze dispersion relations. Mode identification is performed to investigate the effects of piezoelectricity on both in-plane and out-of-plane wave modes. Two distinct lattice configurations—rectangular with varying stretch ratios and hexagonal with different internal angles—are systematically analyzed to elucidate their wave propagation behaviour.

The findings reveal that piezoelectricity significantly impacts in-plane modes by reducing group velocity and modifying bandgap structures. Conversely, out-of-plane modes remain unaffected. Notably, piezoelectricity strongly suppresses energy flow in the direction of poling, enabling directional wave control and facilitating wave beaming phenomena. These results highlight the potential of piezoelectric metamaterials in achieving tunable wave propagation and directional energy flow.

To the best of our knowledge, this study marks the first comprehensive investigation into wave dispersion in piezoelectric truss metamaterials. The developed framework, rigorously validated against established results for purely mechanical metamaterials, demonstrates robustness and reliability. By advancing the theoretical modeling of piezoelectric truss lattices, this work bridges the gap between foundational research and transformative applications, including energy harvesting, wave control, and sensing technologies. These findings establish a solid platform for future exploration of 3D piezoelectric truss lattices and the strategic use of dynamic stimuli to engineer tunable bandgap materials.
... Read more

Recent advancements in additive manufacturing have led to significant progress in the field of metamaterials, wherein the introduction of microscopic features affects the material properties on a macroscale. Common examples of these materials are truss-based and plate-based structures. However, these lattices bear inherent susceptibility to stress concentration points, which undermine their overall performance. Incorporating smooth surfaces in the material design can be a promising strategy for solving that issue. One noteworthy class of smooth metamaterials originates from the topologies formed in the process of spinodal decomposition. For small fluctuations, these structures can be described mathematically using Gaussian random fields (GRF) stemming from the superposition of standing waves. Recent research work has led to the development of a subtype of these structures, termed spinodoids. Spinodoids are anisotropic structures formed through a biased sampling of wave direction vectors encompassing the underlying GRF.
The development of spinodoid topologies has allowed for extensive design exploration and property-structure investigations, important in the context of many potential future applications, such as bone biomaterials design. Until now, the majority of studies performed on spinodoid metamaterials were limited to the elastic regime. However, many materials, especially biological tissues such as bone, rarely exhibit purely elastic behaviour. Thus, exploring other material regimes is of great interest. This work attempts a finite element analysis of three different types of spinodoid structures: lamellar, cubic, and columnar. These structures' properties are based on a generalized Maxwell model for cortical bone. The study explores how tuning the design parameters influences their mechanical behaviour in a viscoelastic regime. ... Read more

Additive manufacturing (AM) is considered an environmentally friendly manufacturing process that builds solid 3D structures layer-by-layer from a computer-aided design (CAD), resulting in reduced waste compared to conventional subtractive manufacturing. However, a significant challenge in AM is the extensive use of plastics, which lack a standardized recycling process. Insufficient adhesion between printed layers contributes to waste generated with AM. Therefore, it is important to prioritize sustainability as a design parameter for future AM materials. Dynamic Covalent Networks (DCNs) offer a potential solution by combining the desirable characteristics of thermosets with the recyclability of thermoplastics. DCNs undergo bond rearrangement reactions influenced by external stimuli such as heat or light, leading to changes in their topology. Bond exchange reactions can occur through two mechanisms: dissociative, involving separate steps of bond breaking and forming, or associative, involving simultaneous bond breaking and forming. Recently, a phosphate triester-based DCN was developed, obtaining a neighboring β-hydroxyl group which could perform transesterification exchange reactions within the network via the formation of a cyclic phosphate triester intermediate in a dissociative manner. Based on this network rearrangement, we synthesized a network by reacting phosphoric acid with a diglycidyl ether to perform ring-opening reactions, creating a pendent β-hydroxyl functional group that assists in neighboring group transesterification. The reversibility of the bond exchange reaction within the network was investigated using a heating and cooling cycle with variable temperature (VT) ³¹P solid-state NMR (SSNMR). Furthermore, the network was reprocessable using compression molding at 130 °C. Fast relaxation times of 5 seconds at 200 °C were observed. Additionally, frequency sweep and dynamic mechanical temperature analysis (DMTA) experiments showed profiles that were expected for a dissociative bond exchange mechanism. However, an increase in the storage modulus at 150 °C was observed, indicating a curing process of the network. Subsequently, similar experiments were performed on the cured network, in which a reduction of the dynamic properties of the network was noted, with a stress relaxation time of 114 seconds at 200 °C. DMTA and frequency sweep experiments confirmed its increased storage modulus as well. Nevertheless, the uncured network was reprocessable via extrusion at 200 °C. However, it required at least 50 minutes for the network to obtain a viscous flow behavior suitable for optimal extrusion. ... Read more

Cardiovascular diseases continue to be the primary cause of death worldwide, where the buildup of plaque within arterial walls, known as atherosclerosis, is a major contributor to various acute cardiovascular events. Determining the material properties and the resulting stress distributions is crucial in the risk assessment of atherosclerotic plaques, as stress is considered an indicator of plaque vulnerability. Material models can be found with stress-strain pairs, but experimentally determining stress tensors is challenging. To address this limitation, we use a recently developed technique called EUCLID (Efficient Unsupervised Constitutive Law Identification and Discovery) for material characterisation of a two-dimensional multicomponent atherosclerotic plaque, based solely on displacement and force data. A finite element model was developed to simulate the mechanical behaviour of the plaque using the neo-Hookean hyperelastic model, and noisy data was introduced into the model by applying Gaussian noise on the displacements. An L-BFGS gradient descent optimiser was used to minimise the objective function, which is the residual error between predicted internal forces and true external forces. Results showed that at the expected noise level in clinical imaging modalities, no physically relevant stress distributions were obtained, where the plaque’s heterogeneity was observed to affect the accuracy. Clinical imaging was further emulated by systematically removing data to determine the effect of missing data on the model. No significant deterioration of the accuracy of obtained parameters was seen until using 10% of the total data, indicating good robustness to missing data. While the study has limitations, the proposed approach could have implications for the future diagnosis and treatment of atherosclerosis. Future research could explore alternative optimisation algorithms or techniques to improve the model’s accuracy under these conditions. ... Read more

Hall spars, a leading innovator in the composite mast-building industry with a long history of successful projects, provided a challenge which is the inspiration of this thesis. This thesis aims to contribute to the challenge: Joining techniques for the internal bonding of carbon fibre-reinforced polymer components into carbon fibre-reinforced polymer structures. ”Internal bonding” can also be called ”bonding.

Currently, the epoxy adhesive film is used for these types of joints. Adhesive co-bonding raises challenges due to the limited access to the mast. Challenges concerning surface preparation, low mechanical strength, reliability and labour intensity. Other techniques will be reviewed for the internally bonding limited access hollow tubes. The aim is that no new tooling is required, and the currently used materials and process parameters can be used. The thermoset resin used is an epoxy carbon fibre prepreg.

According to the literature study, potential joining techniques for this application are joining with partially cured thermosets and joining with fusion welding of thermoset composites. Differential scanning calorimetry measurements are performed to analyse the thermoset resin. Material characterisation is required to apply the joining methods with the currently used material and process parameters of Hall Spars. Process parameters for the curing model, and glass transition temperature model are derived. Using this material model allows creating the insight that partial cured joining with the thermoset resin of Hall Spars is not feasible. The structural integrity of the joined parts can not be guaranteed in the curing cycle.

Polyetherimide is used as thermoplastic material to apply the fusion welding technique on the thermoset. This thermoplastic film is co-cured with the thermoset and used as a coupling layer to join the two thermoset parts. Performing an interphase analysis with the scanning electron microscope indicates interphase formation at the PEI/epoxy interface. The higher the isothermal curing temperature, the thicker this interphase. Gelation causes the interphase formation to slow down. The interphase formation at the interface is not been observed as expected based on the literature review. This required a test if the interphase formation would have occurred at a smaller scale than the scanning electron microscopy could observe. This can be achieved by performing single-lap shear experiments. These joints are processed according to Hall Spars process parameters. Joints created with the fusion coupling technique with the PEI film show promising results. However, due to high void content, a lower ultimate lap shear strength is observed. Therefore it is advised to investigate a curing cycle with a second dwell phase. This changes the viscosity profile of the resin. Therefore allows the trapped air at the PEI/epoxy interface to leave the joint. This could potentially lead to more reliable joints. ... Read more

Ceramic matrix composites (CMCs) are advanced materials that consist of a ceramic matrix reinforced with a high-strength, high-stiffness material, such as carbon fibers. They offer excellent thermal and chemical stability while exhibiting low weight and exceptional mechanical properties. A novel CMC material is the C/C-SiC produced with 2/2 twill weave fabric. It consists of a carbon fiber-reinforced carbon (C) and silicon carbide (SiC) matrix. In this study, a macroscopic non-linear constitutive model accounting for the damage-induced plasticity is proposed for the 2/2 twill weave C/C-SiC composite.

In the context of this thesis, a computational model is developed, based on the framework of continuum damage mechanics and general plasticity theory. A potential function inspired by the Tsai-Wu criterion combined with a damage model is used to predict the strain and damage evolution. An exponential damage evolution law is introduced while the coupling of different damage modes is also considered. Moreover, an experimental investigation on the macroscopic mechanical behavior and damage mechanisms of C/C-SiC under in-plane onand off-axis loading conditions is performed. Specimens with 0\, 30\ and 45\ on- and off-axis angles were manufactured and tested under monotonic and cyclic tensile and compression loads. Furthermore, the microstructure of the pristine material and the fracture surfaces of the tested specimens are studied through scanning electron microscopy (SEM). A Bayesian optimization algorithm is finally used to optimize simultaneously the different material parameters based on the experimental test data.

The predicted stress-strain curves are in good agreement with the experimental curves, especially in the case of monotonic tensile loading. Both damage initiation and evolution are predicted accurately by the chosen laws and coupling functions. Moreover, the combination of the Tsai-Wu criterion with a damage evolution law is proven to predict the ultimate strength well. Fiber pull-out is observed in tension, while interlaminar and translaminar cracks in compression.
This study thus provides an accurate constitutive model, a complete mechanical characterization of the in-plane behavior and a better understanding of the fracture mechanisms of C/C-SiC. ... Read more