Evaluating fracture toughness in composite structures is challenging when material properties are unknown and residual stresses are present, such as aged or repurposed wind turbine blades. This thesis validates stiffness- and curvature-based analytical J-integral formulations for double cantilever beam (DCB) specimens under these conditions. The analytical methods compute the J-integral along the external boundaries, and their accuracy was assessed by comparison with local J-integral values evaluated around the crack tip in finite element models. A surface-based bilinear cohesive zone was implemented in three finite element models: a single-material baseline, a multilayer configuration, and a multilayer model incorporating residual stresses.

The analytical methods showed strong agreement with local J-integral results across a range of load cases, with errors typically below 2% of the fracture toughness. Increased stiffness in the multilayer model lengthened the cohesive zone and improved accuracy by aligning more closely with the analytical assumptions. In the residual stress model, the auxiliary problem, free of residual stresses, accurately reproduced the real problem’s crack plane opening profiles for variations in temperature, stiffness, thermal expansion coefficient, and mechanical loading. Validating the analytical expressions for the auxiliary case--combined with opening profile consistency and the assumption of a potential-function-based J-integral--validated their applicability to the real problem.

These results confirm that the analytical J-integral formulations can accurately estimate the energy release rate when material properties are unknown and residual stresses are present, enabling reliable structural integrity assessments of composite structures.

This thesis focuses on designing and optimizing a shock table using Finite Element Analysis (FEA) to predict and assess Shock Response Spectrum (SRS) levels. The goal is to create a cost-effective,  and accurate shock table capable of replicating launch vehicle shock environments. A structured methodology is adopted, including concept generation, simulation, and design space exploration. The results show that careful tuning of impactor characteristics, fixture geometry, and material selection significantly improves shock table performance.

Quantum computation encodes and operates on data in a radically different way than classical logic. This difference allows researchers in nearly all applied sciences to explore how quantum-accelerated computing could enhance the efficiency of their most computationally demanding tasks.
This thesis studies the introduction of quantum-accellerated computing in structural mechanics, a field that traditionally leverages computational techniques at both industrial and research scales. The scope is limited to practical and mostly near-term quantum computing. Therefore, the algorithms analyzed or proposed are evaluated for their runtime as end-to-end routines and for their ability to run on near-term quantum devices.
Given the vastness of the application domain, the research was developed in three separate threads. The first is a review of quantum algorithms applicable to partial differential equations (PDEs) in structural mechanics. The second is an application of a variational quantum algorithm for linear systems of equations to the discrete Poisson equation, while the last studies how quantum machine learning, specifically quantum kernel methods, discriminates damage-inducing loading states in a composite plate with a cutout.
Each of the three parts reveals the potentials and limitations of practical quantum-accelerated computing.
The PDE review questions the end-to-end advantage of fault-tolerant quantum algorithms and highlights the need to specialize near-term alternatives to problems in mechanics.
The work on the variational quantum linear solver emphasizes the matrix decomposition bottleneck and proposes a method to factorize the discrete Poisson equation matrix.
Finally, the work on kernel methods for damage identification shows how heuristically selected and trained quantum kernels reach scores comparable to classical best-practice kernels, but also hints at the fact that potential quantum advantage requires more systematic kernel optimization and retaining performance when scaling quantum systems beyond classical simulation.

This thesis explores finite element modelling of fibre-matrix debonding and frictional sliding in
Abaqus, with particular attention given to their influence on neighbouring fibres. It aims to improve model accuracy through advanced simulations and validation methods. A literature review underscores the need for robust finite element models in predicting composite material behaviour in fatigue. Theoretical equations for validating the finite element model are developed.
Single-fibre and multi-fibre models are utilised, with the former being used primarily for validation and the latter being used to simulate various realistic cases. Results demonstrate successful validation and provide insights into the effects of friction along the debond interface on stress concentrations in neighbouring fibres. Key findings indicate that reducing interfacial friction increases crack tip energy release rates, leading to stress fields that could potentially cause fractures in neighbouring fibres near the debond crack tip.

Fibre-reinforced composites are increasingly used in aircraft structures as an alternative to traditional metallic components due to their lighter weight and tailorable mechanical properties. However, certifying these materials for critical aircraft components still relies heavily on extensive mechanical testing, which is both resource-intensive and expensive. This reliance highlights the need for virtual testing frameworks that can accurately predict trans-laminar failure behaviour in composite laminates and assess large damage capabilities to create damage-tolerant designs critical for the certification process. Addressing this need, this thesis introduces a novel global-local modelling approach for analyzing trans-laminar damage growth in notched composite laminates. This approach aims to facilitate large-scale structural analysis with reduced computational demands, making it suitable for comprehensive damage assessment in composite aircraft structures.

This research investigates progressive failure analysis in notched composite laminates, with a particular focus on simulating stable damage growth and arrest within the transition region of tapered laminates. A multi-fidelity modelling approach was adopted using flat laminates as a baseline for evaluating damage growth behaviour in the transition regions. Consistent ply stack orientations in both tapered and flat laminates allow for direct comparisons of their distinct failure behaviours. The methodology includes a low-fidelity model that utilizes cohesive elements to simulate self-similar crack growth initiating from notch tips. For more detailed predictions, a high-fidelity model incorporating a discrete ply-by-ply approach with cohesive interactions was used to capture both intra-laminar and inter-laminar damage mechanisms. In this high-fidelity approach, two intralaminar damage models are compared: a user-defined continuum damage model (VUMAT) and a built-in Hashin damage model available in ABAQUS. The VUMAT model applies specific softening laws and element sizes for each failure mode to ensure accurate energy dissipation, while the Hashin model serves as a simpler built-in alternative.

The results show that, despite simplifications, low-fidelity models provide reasonable estimates of failure loads and stiffness in the elastic region, aligning closely with high-fidelity models and existing experimental data. The high-fidelity model effectively captures complex damage modes, with the VUMAT model outperforming the Hashin model in accuracy. The global-local approach proves reliable, offering results comparable to a globally refined meshing approach. It shows potential for use in analyzing larger, computationally demanding structures. Overall, the findings also highlight the importance of fibre-aligned meshing and precise cohesive zone modelling in predicting damage initiation and progression in notched composite laminates, providing insights for designing damage-tolerant composite structures.

Military vehicles are often subjected to dynamic loads from mines. To protect these vehicles, steel plating underneath the vehicle is applied. However, these steel plates can be quite heavy, resulting in a slower vehicle. Composite laminates, however, are much lighter and also prove to be capable of protecting these vehicles from mines.
During an explosion, the energy released from the blast will be absorbed by the composite material. This often results in the delamination of plies within the laminate. Due to the delamination, bending loads will be taken over by membrane loads. This is proven advantageous for composite materials as they are stronger in membrane loading.
Unfortunately, modelling sizeable composite structures with a Direct Numerical Simulation (DNS) requires the use of a lot of elements. This, in turn, results in long computational times, particularly for non-linear analyses. Multiscale modelling is a possible solution to this problem.
This study explores the method of Computational Homogenisation for delamination in composite laminates as an alternative to 3D DNS modelling. Two-dimensional Shell-Interface-Shell elements (SIFS elements) are introduced on the macroscale. These double-layered shell elements consist of two stacked Mindlin-Reissner shell elements with an interface element connecting the two shells. Each integration point of a SIFS element is linked to a mesoscopic 3D coupled Representative Volume Element (cRVE), which is also split into two shells with an interface in between. By applying linear and periodic boundary conditions that incorporate the macroscopic strains on the cRVE, mesoscopic stresses are determined, leading to macroscopic stresses and the macroscopic stiffness matrix.
The proposed multiscale framework is validated by a set of load cases with different ply configurations. The results are then compared to those of a 3D DNS. The multiscale framework performs reasonably well; however, it is not without its limitations. Firstly, the cRVE exhibits width dependence, requiring the implementation of a sufficiently narrow cRVE for accurate results. Additionally, SIFS elements may lack kinematic consistency with 3D solid elements, constraining certain deformations and resulting in overly stiff responses for SIFS analyses. The proposed multiscale framework might not perform as accurately as the 3D DNS in specific load cases, one of which is explored in this work.
Returning to the original goal of this work for the multiscale model, certain extensions still need to be implemented to design composite laminates for blast protection. Implementation of the arc-length method will provide insight into snapback behaviour that could occur during loading. Next, the macroscale and mesoscale models need to be adapted for multiple delaminations over the height of a laminate. Furthermore, the implementation of dynamic loading is a necessary step, as blast loads induce strong dynamic behaviour. Finally, the integration of Artificial Intelligence / Machine Learning into the framework could improve the model by further reducing computational time.

The objective of this thesis was to develop a microstructure-based FE modeling technique to be used in solder joints for semiconductor packaging applications. This technique is capable of generating random solder joint microstructures featuring β − Sn grains and grain boundaries by means of 3D Voronoi tessellations. The anisotropic material behavior of β − Sn grains is described by the Garofalo-Hill creep model, which combines the Garofalo creep equation with the anisotropic Hill equivalent stress definition . The β − Sn grains also feature anisotropic elastic behavior. The grain boundaries are implemented in the FE models as interface elements and they are fitted with a custom-developed constitutive model. This constitutive model combines isotropic creep and isotropic elasticity. The granular microstructure of SAC solder joints is generated via random Voronoi tessellations. The tessellations are used to automatically generate large amounts of unique solder joints with 5 to 9 grains each, with each grain having a random material orientation. The modeling technique was used to qualitatively estimate the stochastic variability that the microstructural differences introduce in the creep response of the solder joints.
Multiple simulation campaigns were performed to analyze this variability on a single solder joint level as well as on a product level. A number of sample products with random combinations of unique solder joints were developed for the latter campaign.

This work develops a stand-alone Finite Element-based Unit Cell for the modelling of Fibre Reinforced Composites, capable of capturing 3D stress states and geometrical imperfections at the micro-scale. The project considers different beam and 3D elements for this purpose and draws comparisons between them based on computational efficiency, accuracy, and ease of implementation. A new setup is proposed to greatly reduce computational effort and facilitate user interaction and parametrisation by drawing from some of the most promising concepts identified from the present literature. The thesis presents such an FE model which can model a Multi-Fibre-matrix Unit Cell including codes for the generation and perturbation of the geometry, multi-scale meshing, solving, and post-processing. Some typical results for Micro-scale Unit Cells are presented to validate and demonstrate the capabilities of the model, and avenues for improvement and development of this concept are also detailed.

This project aims at developing a model to predict the damage initiation and propagation in a cylindrical filament-wound cord-rubber structure under internal pressurization using non-linear FEA, and is conducted in cooperation with TANIQ BV, Netherlands.
Cord-reinforced rubber composites are used in several safety-critical industries such as oil and gas and civil plumbing. Despite their widespread use, limited research is available that focuses on the single-cycle damage phenomenon that may occur in events such as over-pressurization.

The aim of this project is to close this knowledge gap by developing a theory that accounts for the key damage modes present in CRC structures. This involves experimental studies for material characterization and identification of relevant damage modes, the creation of a novel fibre overlap model that accurately replicates the meso-level filament-wound structure, and translation of the experimentally verified damage modes into functional damage initiation and propagation laws using a global-local FEA model. Verification of the created damage model is done experimentally on samples manufactured and tested at TANIQ, with differences between model predictions and experimental burst pressures being $\approx 6.5\%$. This is a marked improvement over Taniq's current FEA model, which overpredicts the solution by $\approx 27\%$.

The successful implementation of this model would help industries like TANIQ build efficient, strong, and lightweight rubber composite parts for various industries, thus adapting aerospace design principles to the development of more commonplace apparatus.

The aerospace engineering industry is continuously striving for faster methods to solve and optimize engineering and research problems with a higher degree of accuracy. It is therefore relevant to investigate possibilities that are expected to accelerate computational speed, such as quantum computing. For this reason, the objective of this thesis is to investigate the feasibility of optimizing 2D determinate truss structures with a quantum algorithm run on a Gate-Based Quantum Computer (GBQC). The Quantum Approximate Optimization Algorithm (QAOA) is currently expected to be the most suitable quantum algorithm candidate for optimization problems, because of its simplicity and robustness. The most important take-away from this thesis is that it is possible to map a truss structure to QAOA format to optimize it on a GBQC. The program proves to be working on quantum virtual machines. However, it is currently not possible to obtain correct results when running on real quantum hardware due to noise.

Due to an exponential growth in the number of shipments of Unmanned Aerial Systems (UAS), the amount of these devices operating in the sky has increased remarkably over the last few years. This led to an increasing number of proximity incidents with manned aircraft. Since these devices share certain airspace with rotorcraft, the question arises how much damage a helicopter could sustain after an impact with a UAS. Within this thesis, a risk assessment was completed initially to determine which collision in terms of type of UAS and helicopter impact location poses the highest risk to the operator of the helicopter. Subsequently a validated model of a DJI Phantom III was developed and impacted onto a rotorcraft windshield in explicit Finite Element software. The sustained damage was compared with a simulated bird strike event to determine whether the prevailing certification requirements would suffice to guarantee safety of the crew.

In this thesis, a new data-driven finite element is developed, which is referred to as a neuromorphic element (designated as NmT2). Its goal is to reduce the computational expense of FEA models with- out compromising solution accuracy by embedding a neural network, trained on an element level. The neural network is developed such that the traditional trial-and-error approach to determin- ing its hyperparameters may be bypassed. This is achieved through a multi-objective optimization algorithm that builds networks with random configurations and uses Latin Hypercube sampling to test them on a fraction of the overall data repository. Once the algorithm reaches a state of diminish- ing returns over the development of multiple networks, the program is halted and the best perform- ing neural network is saved. The resultant network is then trained over the entire data repository consisting of over half a million datasets. The entire process of a self-designing neural network is called a neuromorphic engine. The neuromorphic engine is designed to determine the local nodal force vector of a truss mem- ber based on the structure’s geometry and axial nodal displacements. Axial tension and compres- sion are the two modes of loading that are considered and are pushed to the nonlinear regimes by including post-buckling and material plasticity. In addition, the user is provided with the option of including structural defects in the truss members. Once trained, the neuromorphic engine can be inserted within a user-element subroutine and deployed in ABAQUS. The neuromorphic element is essentially a truss element which includes the deformation ca- pabilities of beam elements. Unlike traditional FEA methods requiring multiple beam elements, a single NmT2 element can be used when meshing a truss member to model complex behaviour such as post-buckling deformation. To test the capabilities of the neuromorphic element, three case studies are designed as a proof of concept, comparing the performance of NmT2 elements against traditional FEA elements (T2D2 or B22). Overall, the NmT2 elements managed to accel- erate the computing time of an FEA model by up to 1,000%, while maintaining solution accuracy within 5%. These results affirm the potential of neural networks within active FEA simulations in the field of data-driven computational mechanics as a means to define complex nonlinear element formulations.

Quantum computing is a new form of computational technology, which can potentially be used to solve certain problems faster than is possible using classical computers. For this reason, there is an industry drive to develop early quantum computing applications. In this thesis, an overview of quantum computing technologies is provided, along with a practical discussion of the Traveling Salesman Problem, making use of the D-Wave quantum annealer. Subsequently, the main objective of the thesis can be investigated, which is to
explore how quantum computing can be used to aid in solving structural optimization problems. Two methods are developed with which simple 2-dimensional truss systems can be optimized using the D-Wave quantum annealer. The methods aim to find the most lightweight choices for the truss cross-sectional areas while complying with material limit stress constraints. The first method directly casts such an optimization problem into a QUBO format. However, due to difficulties with formulating the stress constraint, this method was found to produce a trivial optimization problem. The second method attempts to symbolically solve a truss finite-element problem, using the resulting symbolic expressions to set up an optimization objective function. Although these objective functions are confirmed to work via classical brute-force analysis, the quantum annealer is shown to have difficulty finding the global optimum solution for truss systems with three or more elements. These results indicate that it is not currently beneficial to use quantum annealing for these structural optimization problems. Nevertheless, some improvements to the method for setting up the objective functions are suggested. The next generation of quantum annealers is expected to perform better for these practical applications, potentially becoming a useful tool in the engineering toolbox.

For expensive computational simulations, such as the finite element method (FEM) or computational fluid dynamics (CFD), whose evaluation can take even tens of hours or more, the use of direct optimization is often not feasible in practice. If simplifying the model is not an acceptable option, an alternative is to train a cheaper surrogate model on a limited amount of samples. This is called the surrogate-based optimization (SBO) approach. It consists of four main steps: 1) sampling , 2) computational analyses, 3) surrogate’s training, 4) optimization. Due to the repetitiveness, the evaluation of the samples is the primary bottleneck, therefore the smart selection of the training samples is of primary importance. The design of experiments (DoE) is a systematic approach of determining the samples. Static DoEs have been used and studied extensively. However, they are designed to covert only the input space uniformly, which means that only a half of the available information is used. Therefore, adaptive DoE methods that consider also the response in the determination of new samples have been proposed as an improvement. In the last 10 years, their research has gained momentum and numerous adaptive DoE methods have been proposed. The identified scientific gap is the lack of their overview as well as numerical benchmarking. Within this master thesis project, a modular SBO framework, suitable for such benchmarking, was developed, and the proposed adaptive DoE methods reviewed in more detail. With this at hand, a follow-up work can smoothly proceed into the actual benchmarking, which is estimated a task of itself for a project of similar master thesis scale. The results presented within this master thesis include the validation of the framework on the MATLAB peaks, Binh and Tanaka benchmark problems and the proof-of-concept evaluation of adaptive DoE on the MATLAB peaks, Judge, McCormick and Michalewicz problems against static DoE. It is demonstrated that using an adaptive DoE, the amount of required training samples is lower, or the same at worst, as with the static DoE. Additionally, thanks to the cooperation with Škoda Transportation, the practical use of the framework is presented on a structural FEM optimization of a novel tram car body, the SegTram. The developed framework is suitable both for research purposes as well as practical, industrial applications, and it is openly available at github.com/apanzo/optimization.

The demand for better performing structural designs gives rise to the interest in topology optimization. An accurate geometry description is fundamental in the optimization process. The Cut Finite Element Method (CutFEM) can describe the object surface on a fixed grid with an immersed boundary. To this level set method, multiple techniques from the density method can be added. In this thesis project, the performance of 3D topology optimization using CutFEM is tested. A topology optimization model using CutFEM was developed. For the elements on the boundary (Cut Elements), a cut is made to split the element in a solid and fluid part. The Gauss points that represent each part are calculated in order to find the stiffness of the Cut Elements. The performance has been tested by performing finite element analysis with CutFEM. In order to perform topology optimization, the sensitivities are calculated with the adjoint method, a filter was used with Heaviside projection and mapping, and the Method of Moving Asymptotes (MMA) is used in order to find the design of the next iteration. In order to increase the length scale, an optional robust design method was implemented which creates an eroded and dilated design. The performance of topology optimization using CutFEM was tested by optimizing a structure for minimum compliance for a set of loading conditions. This was compared to topology optimization with classical Solid Isotropic Material with Penalization (SIMP) method. Firstly, it was found that Cut Elements are an accurate method to perform a finite element analysis. Next, it was found that topology optimization using CutFEM is able to obtain a better objective function than topology optimization using the SIMP method. The computational costs of the CutFEM method are substancially higher. In 3D topology optimization using CutFEM, the design changes can happen everywhere on the boundary, so that the initial structural design is of less importance than for 2D. Next, it was found that the robust design method works well in increasing the length scale, but the objective function is decreased and the initial design is more important. It is thought that CutFEM could best be used to perform optimization with the initial design computed by the SIMP method. Finally, the CutFEM method has been used in a Navier Stokes fluid solver with Brinkman penalization implementation. It was found that this does not work, and a fluid solver with a hard boundary method is required. It is recommended to implement the CutFEM method in a fluid solver with Nitsches method.

This research project was initiated as a result of a curiosity and desire to investigate the applicability of surrogate modelling to analyse complex non-linear behaviour in aircraft structures. The study chose to focus on modelling damage in composite plates, and through a literature review, deemed that the generation of graphical outputs was a domain worthy of attention. Thus, the research questions subsequently formulated were centred around the modelling of artificial neural networks for the generation of damage patterns on composite plates.

Data for training and evaluating these neural networks was first generated through 20 finite element models solved using Abaqus 2017. Standard neural networks trained to directly reproduce these damage patterns, as well as reduced-image neural networks trained to reproduce a reduced formof these patterns (obtained using convolutional neural networks) were analysed, and both were found in want ofmore training data. The generation of a further 421 finite element models resulted in a striking improvement in the performance of both networks, but with the standard neural network outperforming the reduce-image neural network. Thereafter, it was discovered that a hybrid network that combined facets of the standard and convolutional neural networks performed superior to both.

In the process of training these networks, it was recognised that while the performance metrics served as an indicator of the resemblance between the predicted and actual outputs in terms of colours and contours, the same trends did not apply to the image quality. In order to improve the visual quality of outputs from the hybrid network, the use of the Structural Similarity Index (SSIM) was explored. It was eventually determined that pre-training the network using the Mean Square Error (MSE) as its optimising metric before then doing the final training using SSIM resulted in a model with impressive results. This fine-tuned model carried out predictions with a mean error of 0.0014 on theMSE metric and 0.9804 on the SSIM metric when evaluated on an independent dataset. Finally, the reliability and computational efficiency of the hybrid model was measured. It was found that approximately 95% of the MSE values on the independent dataset were within a value of 0.0040, while the same percentage of SSIM values were over 0.9100. The computation speed, meanwhile, improved by a factor of roughly 34 times on average, with the figure rising to over 443 on specific models.

The wide adoption of composite structures in the aerospace industry asks for reliable numerical methods to account for the effects of damage, among which delamination. Cohesive elements (CEs) are a versatile and physically representative way of reproducing delamination, but, using their standard form, at least 3 elements are required in the narrow cohesive zone, hindering the applicability in practical scenarios. This limitation is due to the inability of
current models to capture the deformation of the delaminating substrates. The present work focuses on the implementation and testing of triangular thin plate substrate elements and compatible cohesive elements, which satisfy C1-continuity at their boundary. The improved regularity meets the continuity requirement coming from the Kirchhoff Plate Theory and the triangular shape allows for conformity to complex geometries. After verification of plate and
cohesive element singularly, the overall model is validated for mode I delamination. Very accurate predictions of the limit load and crack propagation phase are found, using CEs as large as 11 times the cohesive zone.

This thesis is part of a greater effort to use machine learning for the development of flexible and universal unresolved-scale models in large eddy simulation (LES). The novelty in the current work is that a neural network learns to predict the integral forms of the unresolved-scale terms directly without a priori assumptions on the underlying functional relationship. The contribution of this thesis is a validation of a neural-network-based unresolved-scale model for Burgers' equation which paves the way for future application to the Navier-Stokes equations.

Compressive failure mechanisms of fiber reinforced polymers represent a significant challenge when accurately modeling common industrial problems. This thesis aims to deliver a physically representative computational framework capable of simulating Open Hole Compression. A novel approach, the Floating Node Method is adopted for modeling discontinuities. Furthermore, a constitutive law for fiber kinking, incorporating the microscale bending stress of a fiber under the assumptions of the Euler-Bernoulli beam theory, is proposed. This meso-scale Continuum Damage Model poses three requirements for kinking onset: 1) local failure of the matrix in the kink band; 2) fibers fracture due to bending; 3) the longitudinal compressive stress is sufficiently large to satisfy the previous requirement when the kink plane shear stresses is smaller than the traverse shear strength of the ply. The model is validated against experiments of different sized [45/90/-45/0]s laminates. The predicted panel strengths match experiments by less than 8%.

Fibre-reinforced composites have become increasingly attractive for many engineering applicationsin the last decades. A very interesting aspect of these materials is that their mechanical properties canbe tailored for optimum strength and stiffness by controlling the orientation of the fibers embeddedin the matrix material. Composites are characterized by high strength properties, strong corrosionresistance, improved damage tolerance and can lead to considerable weight and cost reduction whencompared to their metallic counterparts. However, accurate modelling of damage in composites is stillan active research topic, as their progressive failure involves the interaction of various intra- and inter-laminar damage mechanisms, which often lead to complex fracture paths. To this regard, the FloatingNode Method (FNM) proved to be particularly suited for the modelling of complex cracking scenarioswithin a finite element. However, its use has so far been limited to geometric linear analyses, wheredeformations and rotations are small enough and the conventional linear FEM formulation is used. Thisthesis work investigates the modelling of geometric non-linear fracture problems in composites usingthe Floating Node Method. A co-rotational approach is proposed as a convenient, conceptually simpleway to include geometric non-linear effects in problems characterized by large rotations but smalldeformations. This approach allows re-use of the conventional linear FEM formulation by separatingrigid body and purely deformational motions at the element level. The co-rotational procedure, firstimplemented and validated on a linear solid brick element, is subsequently applied to the FloatingNode (FN) element. Finally, a demonstration of the element’s potential in capturing geometric non-linear effects is offered, addressing the modelling of crush and impact loading on composite laminates.