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.

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.

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.

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.

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.

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 as opposed to conventional subtractive manufacturing. However, a significant challenge in AM is the extensive use of plastics, which lack a standardized recycling process. The 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 and the recyclability of thermoplastics. DCNs undergo bond rearrangement reactions influenced by external stimuli like 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 phos- phoric acid with a diglycidyl ether to perform ring-opening reactions which creates a pendent 𝛽-hydroxyl functional group assisted in neighboring group transesterification. The reversibility of the bond exchange reaction within the network was investigated with a heating and cooling cycle with Variable temperature (VT) 31P solid-state NMR (SSNMR). Furthermore, the network was reprocessable with compression molding at 130 ◦C. Fast re- laxation times of 5 seconds at 200 ◦C were observed. Additionally, frequency sweep and dynamic mechanical temperature analysis (DMTA) experiments showed profiles which were expected for a dissociative bond exchange mechanism. However, an increase of 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 min for the network to obtain a viscous flow behavior for optimal extrusion.

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.

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.

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.

Bainite steels are in high demand in many application areas owing to their outstanding mechanical properties, mainly due to the presence of a combination of fine bainite plates and retained austenite. Understanding the complicated mechanism of bainite transformation is crucial to creating an optimum design. Researchers have addressed this challenge via computational modelling, where the transformation start temperatures of bainite (Bs) and martensite (Ms) are key indexes when designing highperformance steels and their heat treatments.

This thesis aims to explore the potential that data collection has to create a model, using experimental results of the bainite transformation process. A list of metallurgical accepted claims is elaborated to assess the quality of the data. Principal component analysis and clustering techniques are used to identify patterns, most important features and main relationships in the dataset. The results confirm the exponential carbon dependence that bainite and martensite transformation temperatures have, therefore requiring nonlinear models to predict them.

Following, regression models and machine learning algorithms based solely on the chemical composition are used to predict Bs and Ms. Train-test split series and cross-validation are used to evaluate the prediction and consistency of each model. The results show that the ensemble learning algorithms outperform the regression techniques. Random forest and gradient boosting decision tree provide excellent Ms prediction on the validation set with R2 values of 0.92 and 0.93. The smaller dataset size adds up to the complexity of bainite transformation, resulting in worse prediction models of Bs, where the random forest and gradient boosting decision tree R2 values are 0.68 and 0.67 respectively. Even though the models are showing signs of learning, the impact that outliers have demonstrates that the data is not good by itself to create a predictive model. The incorporation of microstructural and process parameters would provide significant advances to the models for designing bainitic steels.

Metamaterials derive their properties from microstructure rather than from bulk material properties. This opens property spaces that are difficult, or impossible, to access with traditional methods. However, exploring this vast design space remains challenging because classical techniques can be computationally inefficient. Recent years has seen many successful applications of data-driven methods to this problem. Data-driven models can be used to bypass expensive Finite Element (FE) simulations and experiments by exploiting large datasets. This requires the parameterization of microstructures such that they can be mapped to properties. The forward problem, in which properties of are determined from design parameters, is typically well-posed, which allows straightforward application of machine learning methods. The inverse problem, in which design parameters are identified to match specific properties, is ill-posed because multiple sets of design parameters can produce similar properties. The Voronoi growth method induced by star-shape metrics provides a way to explore a large geometrical design space using simple parameterization. It is used to generate 2D unit cells of void and material pixels with non-trivial topologies. The growth process enforces connected geometry while also allowing for smooth transition between different designs. Using homogenization techniques, we generate a large dataset of design parameters and stiffness properties. Machine learning techniques are first used to model the forward problem. By combining the trained forward model with the inverse model, the inverse problem is rendered well-posed. Both the forward and (deterministic) inverse model show excellent agreement between target and predicted values. The method is generalized beyond the design space by targeting properties of non-growth based structures. We investigate methods to create a stochastic inverse model, that can produce multiple designs to match target properties. Verification is done by comparing tensile tests of 3D printed samples to FE simulations.