As the demand on new lightweight materials increases due to economic and environmental reasons, fiber reinforced plastics seem an interesting solution. In order to maximize their potential, ways have to be found to introduce damage tolerant designs. To this end, in the present exercise, the damage caused by cyclic loading is investigated. Cyclic loading causes loss of stiffness and strength of composite structures. In the present work a progressive damage analysis model has been built. Damage is tracked by a macroscopic failure criterion. Failure occurring at levels below the macroscopic level are captured by a degradation theory which requires S-N curves as input. An attempt is made to use S-N curves derived from static tests only. Stress solutions are obtained using a finite element package. In order to bring the macroscopic damage, degradation and stress solution parts together, a cycle jumping algorithm has been developed which minimizes the amount of FEM stress solutions required and the required amount of function evaluations. In its current form, the model is able to properly predict the trend of stiffness and strength behavior. Showing that a model like this has potential. For the proper predictions of mag- nitudes, additional failure modes should be modeled and the usage of S-N curves obtained from large specimens for applying on smaller geometries (elements) should be reconsidered.

Besides building ultra-lightweight sportscars, Donkervoort Automobielen B.V. is developing a novel core material to be applied in complex shaped composite sandwich structures. This material goes by the name of X-Core and has the unique capability of generating the pressure required for facesheet consolidation, enabling a one-shot manufacturing process for the first time. The present research has focused on the phenomena present during the manufacturing process of the foam. In-situ temperature measurements have been carried out to gain a deeper understanding of the thermal behaviour of the material. A numerical model, based on a transient finite difference method, was then constructed to predict the temperature distribution in the material during its cure under different processing conditions, material compositions and cure cycles. The proposed model can now be applied in fine-tuning cure-cycles required to attain certain desirable X-Core properties.

The current concerns regarding global warming and climate change in Europe and other parts of the world also influence developments in the automotive sector. Not only are alternative energy and power sources making their advent, but greenhouse gas emission reduction has also become a major factor in the development of vehicles with combustion engines. As passenger vehicles with such engines will be sold for probably a few more decades, additional options for emission reduction are still welcome. Mass reduction results directly in less greenhouse gas emission per driven kilometer, both in practice as in the official carbon dioxide (CO2) emission determination procedures. This thesis is part of a project that tries to develop an additional option for passenger vehicle mass reduction, more specifically by replacing steel in the exhaust system with fibrereinforced plastic. The principle behind this solution is that fibrereinforced plastic has better mechanical properties per kilogram of material than steel. Yet, no plastic could endure direct exposure to exhaust gas flows because of the maximum gas temperature of 800 1000 ∘C.... @en

Fatigue crack growth in metals has been studied intensively for more than half a century. This research field has been closely connected to the engineering industry throughout history. It is therefore no surprise that the main focus has mostly been on developing better prediction models rather than on understanding the phenomenon of fatigue crack growth. This dissertation focuses on improving our understanding of the underlying physics, and presents several novel insights primarily relating to crack closure modeling and crack growth rate modeling....@en

The popularity of thermoplastic composites (TPCs) has been growing steadily in the last decades in the aircraft industry. This is not only because of their excellent material properties, but also owing to their fast and cost-effective manufacturing process. Fusion bonding, or welding, is a typical joining method for TPCs due to the intrinsic properties of thermoplastic polymers. Among different welding technologies, ultrasonic welding has been regarded as one of the most promising techniques for the assembly of TPC components. Ultrasonic welding is by nature a spot welding technique. As it is known that a series of problems result from using mechanical fasteners for joining composite structures, e.g. breaking fibres during drilling and extensive labour work, ultrasonic spot welding can be considered as a promising alternative from the perspective of fast manufacturing cycle. However, fundamental understanding is still lacking to achieve application of ultrasonic spot welding in composite structures to be achieved: @en

The use of high performance thermoplastic composites (TPCs) in the aviation industry has increased over the last years. TPCs have significant advantages such as superior damage tolerance, excellent chemical resistance and the ability to be welded. To date, mechanical fastening, primarily, and adhesive bonding are the two traditional joining methods used in aviation industry. However, welding of TPCs has attracted significant attention due to its advantageous qualities such as the very short cycle times and the minimised stress concentrations. Most of the research published on welding of TPCs concerns process optimization and the evaluation of the mechanical performance at room temperature (RT) conditions. However, aircraft operate in a wide range of temperatures, typically between -50 °C to 70 °C and depending on the conditions, even up to 93 °C. Considering the temperature dependency of polymer composites properties, the weld performance of TPCs at low and high temperatures needs to be addressed. To the author’s knowledge, prior to the year 2014 there was no available literature assessing the influence of temperature on the mechanical performance of TPCs welded joints and since then, only a few publications have addressed this topic. The primary objective of this thesis was to not only fill the literature gap but also to obtain deeper knowledge and clear understanding of the behaviour of thermoplastic composites welded joints at low and high temperatures. In other words, to understand the phenomena dictating the weld performance which, in turn, would pave the way for further improvement of material properties and weld design.@en

One of the most promising welding techniques for thermoplastic composites is ultrasonic welding. It is mainly known as a spot welding technique. The relatively new technology continuous ultrasonic welding of thermoplastic composites makes it possible to obtain large fully welded seams with higher load carrying capabilities. However, at the start of the research conducted for this dissertation very little knowledge on the process was available. The weld quality in the state-of-the-art research was significantly lower than the weld quality of the statically welded counterpart in terms weld uniformity as unwelded and overwelded areas were present simultaneously, the single-lap strength, and the presence of voids. Hence, before continuous ultrasonic welding can be industrially applied the weld quality needs to be improved. Consequently, the main objective of this dissertation was to acquire a deeper understanding of the continuous ultrasonic welding process of thermoplastic composites to promote its development for future industrial applications.@en

One of the classical solutions to maintain the aircraft structural integrity is to rely on the analysis of non-destructive testing (NDT) inspector with various inspection methods. However, it is relatively expensive in matter of time and costs to train human resources until the certification is reached. Further, in majority of the cases of aircraft scheduled and unscheduled maintenance, most of the detected damages are far below the damage tolerance limit and therefore are considered as a costly false positive because such inspections generally require additional downtime. Structural Health Monitoring (SHM) tries to reduce the wasteful resources in the maintenance, repair, and overhaul (MRO) industry by signaling such false positives during the maintenance process by becoming an integral part of the structure itself. On the other hand, there has been an increase in using the artificial intelligence (AI) methodologies such as computational heuristics and machine learning in many areas of human civilization which includes voice and face recognition, languages translation, and automated driving. There has been a lot of interest on implementing AI to assist SHM in maintaining airworthiness while driving the cost down. Nevertheless, the maintenance of airworthiness (such as but not limited to, EASA Part 145/M and FAA CFR Part 21) is a heavily regulated area and are not easily changed. The current state of the art was captured in the literature review. This includes recent developments of guided wave based SHM and the parameter optimization as well as recent trends and advances in artificial intelligence such as machine and deep learning. The findings from the state of the art were used as the basis to determine the research problem and to propose the solution. The first part of the proposed solution consisted of a short review the damage growth assumption within the damage tolerance framework and the used methodology to generate and capture Lamb wave signal within Finite Element (FE) environment. This methodology is a deterministic solution that can be partially used for solving continuous optimization in deterministic sensor placement problem. It was further expanded to include a semi-stochastic approach to address nonpredictable damage location that includes some metaheuristics search such as genetic algorithm and swarm intelligence. The ultimate first part of solution was a compromise between the deterministic and semi-stochastic actuator-sensor topology. The second part of the proposed solution was the investigation on whether deep learning can be used to treat the Lamb wave signal given the configuration obtained from the first part of the proposed solution. To do so, an assumption based on converging probability measures and generalization bound in deep learning must be taken. Then, the approach is to represent the entity of the captured Lamb wave signal in time-frequency domain either as randomly sampled spectrogram or layers of joined spectrograms. After the training, the hypothesis was validated with A/B Testing. Then, the research was expanded to understand the scalability level of deep learning for SHM for given data size, model parameters, and restriction on physical memory. In this sense, the signal representations were trained sequentially with an example of in hybrid convolutional recurrent network. The investigation was focused on stability behavior of convoluted-recurrent modelling for variable spectrogram length and the experimental validation of the model for classification of the Lamb wave spectrogram signals. @en