<p>The design of vehicle components for crashworthiness is one of the most challenging problems in the automotive industry. The safety of the occupants during a crash event relies on the energy absorption capability of vehicle structures. Therefore, the body components of a vehicle are required to be lightweight and highly integrated structures. Moreover, reducing vehicle weight is another crucial design requirement since fuel economy is directly related to the mass of a vehicle. In order to address these requirements, various design concepts for vehicle bodies have been proposed using high-strength steel and different aluminum alloys. However, the price factor has always been an obstacle to completely replace regular body steels with more advanced alloys. To this end, the integration of numerical simulation and structural optimization techniques has been widely practiced addressing these requirements. Advancements in nonlinear structural design have shown the promising potential to generate innovative, safe, and lightweight vehicle structures. In addition, the implementation of structural optimization techniques has the capability to shorten the design cycle time for new models. A reduced design cycle time can provide the automakers with an opportunity to stay ahead of their competitors. During the last few decades, enormous structural optimization methods were proposed. A vast majority of these methods use mathematical programming for optimization, a method that relies on availability sensitivity analysis of objective functions. Thus, due to the necessity of sensitivity analyses, these methods remain limited to linear (or partially nonlinear) material models under static loading conditions. In other words, these methods are no able to capture all non-linearities involved in multi-body crash simulation. As an alternative solution, heuristic approaches, which do need sensitivity analyses, have been developed to address structural optimization problems for crashworthiness. The Hybrid Cellular Automaton (HCA), as a bio-inspired algorithm, is a well-practiced heuristic method that has shown promising capabilities in the structural design for vehicle components. The HCA has been continuously developed during the last two decades and designated to solve specific structural design applications. Despite all advancements, some fundamental aspects of the algorithm are still not adequately addressed in the literature. For instance, the HCA numerically implemented as a closed-loop control system. The local controllers, which dictate the design variable updates, need parameter tuning to efficiently solve different sets of problems. Previous studies suggest that one can identify some default values for the controllers. However, still, there is no well-organized strategy to tune these parameters, and proper tuning still relies on the designer’s experience.</p> <p> </p> <p> Moreover, structures with multiple materials have now become one of the perceived necessities for the automotive industry to address vehicle design requirements such as weight, safety, and cost. However, structural design methods for crashworthiness, including the HCA, are mainly applied to binary structural design problems. Furthermore, the conventional methods for the design of multi-material structures do not fully utilize the capabilities of premium materials. In other words, the development of a well-established method for the design of multi-material structures and capable of considering the cost of the materials, bonding between different materials (especially categorical materials), and manufacturing considering is still an open problem. Lastly, the HCA algorithm relies only on one hyper-parameter, the mass fraction, to synthesize structures. For a given problem, the HCA only provides one design option directed by the mass constraint. In other words, the HCA cannot tailor the dynamic response of the structure, namely, intrusion and deceleration profiles.</p> <p> </p> <p>The main objective of this dissertation is to develop new methodologies to design structures for crashworthiness applications. These methods are built upon the HCA algorithm. The first contribution is about introducing s self-tuning scheme for the controller of the algorithm. The proposed strategy eliminates the need to manually tune the controller for different problems and improve the computational performance and numerical stability. The second contribution of this dissertation is to develop a systematic approach to design multi-material crashworthy structures. To this end, the HCA algorithm is integrated with an ordered multi-material SIMP (Solid Isotropic Material with Penalization) interpolation. The proposed multi-material HCA (MMHCA) framework is a computationally efficient method since no additional design variables are introduced. The MMHCA can synthesize multi-material structures subjected to volume fraction constraints. In addition, an elemental bonding method is introduced to simulate the laser welding applied to multi-material structures. The effect of the bonding strength on the final topology designs is studied using numerical simulations. In the last step, after obtaining the multi-material designs, the HCA is implemented to remove the desired number of bonding elements and reduce the weld length.</p> <p> </p> <p>The third contribution of this dissertation is to introduce a new Cluster-based Structural Optimization method (CBSO) for the design of multi-material structures. This contribution introduces a new Cluster Validity Index with manufacturing considerations referred to as CVI_m. The proposed index can characterize the quality of the cluster in structural design considering volume fraction, size, interface as a measure of manufacturability. This multi-material structural design approach comprises three main steps: generating the conceptual design using adaptive HCA algorithm, clustering of the design domain using Multi-objective Genetic Algorithm (MOGA) optimization. In the third step, MOGA optimization is used to choose categorical materials in order to optimize the crash indicators (e.g., peak intrusion, peak contact force, load uniformity) or the cost of the raw materials. The effectiveness of the algorithm is investigated using numerical examples.</p>

Concurrent Design studies have become of great importance in the space industry reducing the time and costs associated to the feasibility assessments for future space missions. This has also helped companies and space agencies to be at the forefront of this fast-developing sector. These collaborative sessions are carried out by an interdisciplinary group of engineers, experts and customers who are capable of achieving an optimal design solution within a short period of time, typically a few weeks. They make use of dedicated tools, like COMET® which is developed by RHEA Group, to store and share the data within the team, as well as with other partners or stakeholders. As new software tools are developed for Model-Based Systems Engineering (MBSE) applications, parallel improvements are needed for Concurrent Engineering teams, since this can be one of the first steps for a model-based approach. One of the main constraints during Concurrent Design studies is the limited number of analysed options, since evaluating the entire design space would require longer sessions and increased time availability from the experts, and would consequently result in more expensive projects. One solution for this problem can be the application of generative engineering technologies to Concurrent Design studies. This method would allow to explore the entire design space by first defining the study model together with the system constraints, and then using a software to automatically generate all the possible architecture variants for that specific model. An example of state-of-the-art technologies for generative design is Simcenter™ Studio, a recently released tool from Siemens Digital Industries Software. The complexity of space missions requires a very detailed definition and evaluation of the system architecture, even at the early stages of the design process. Therefore, research is needed on the use and implementation of new methodologies that will tackle the challenges related to Concurrent Design. The context of the research presented in this thesis is the new project proposed by RHEA Group, Siemens and OHB, called Generative Concurrent Design (GCD). It aims to combine their software tools COMET® and Simcenter Studio, bringing generative engineering to Concurrent Design. One of the main advantages is achieving more optimised solutions in shorter times, reducing the number of necessary iterations on the system architecture during the entire project lifecycle. An enhanced feature of this tool is the possibility for the users to explore the solutions trade space with the support of an Artificial Intelligence (AI) based system. This thesis presents and demonstrates the application of the GCD methodology to a use case at system level, focused on the evaluation of configuration and assembly options in the design of a spacecraft. Using the mission EnVision, selected in 2021 by ESA’s Science Programme Committee, as the design baseline, the GCD methodology has been implemented in this use case study making use of both software tools and showing potential future features and applications. / Les études de conception concourante ont pris une grande importance dans l'industrie spatiale, en réduisant le temps et les coûts associés aux évaluations de faisabilité des futures missions spatiales. Cela a également permis aux entreprises et aux agences spatiales d'être à l'avant-garde de ce secteur en plein essor. Ces sessions de collaboration sont menées par un groupe interdisciplinaire d'ingénieurs, d'experts et de clients qui sont capables d'obtenir une solution de conception optimale dans un délai court, généralement quelques semaines. Ils utilisent des outils dédiés, comme COMET® qui est développé par RHEA Group, pour stocker et partager les données au sein de l'équipe, ainsi qu'avec d'autres partenaires ou parties prenantes. Au fur et à mesure que de nouveaux outils logiciels sont développés pour les applications d'ingénierie des systèmes basés sur les modèles (MBSE), des améliorations parallèles sont nécessaires pour les équipes d'ingénierie concourante, car cela peut constituer l'une des premières étapes d'une approche basée sur les modèles. L'une des principales contraintes lors des études de conception concourante est le nombre limité d'options analysées, car l'évaluation de l'ensemble de l'espace de conception nécessiterait des sessions plus longues et une plus grande disponibilité des experts, ce qui se traduirait par des projets plus coûteux.  Une solution à ce problème pourrait être l'application des technologies d'ingénierie générative aux études de conception concourante. Cette méthode permettrait d'explorer l'ensemble de l'espace de conception en définissant d'abord le modèle d'étude ainsi que les contraintes du système, puis en utilisant un logiciel pour générer automatiquement toutes les variantes possibles du système pour ce modèle spécifique. Un exemple de technologies de pointe pour la conception générative est Simcenter™ Studio, un outil récemment publié par Siemens Digital Industries Software.  La complexité des missions spatiales exige une définition et une évaluation très détaillées de l'architecture du système, même aux premiers stades du processus de conception. Par conséquent, des recherches sont nécessaires sur l'utilisation et la mise en œuvre de nouvelles méthodologies qui permettront de relever les défis liés à la conception concourante. Le contexte de la recherche présentée dans cette thèse est le nouveau projet proposé par RHEA Group, Siemens et OHB, appelé Conception Concurrente Générative (Generative Concurrent Design ou GCD en anglais). Il vise à combiner leurs outils logiciels COMET® et Simcenter Studio, en apportant l'ingénierie générative à la conception concourante. L'un des principaux avantages est de parvenir à des solutions plus optimisées dans des délais plus courts, en réduisant le nombre d'itérations nécessaires sur l'architecture du système pendant tout le cycle de vie du projet. Une caractéristique améliorée de cet outil est la possibilité, pour les utilisateurs, d'explorer l'espace commercial des solutions avec le soutien d'un système basé sur l'intelligence artificielle (IA). Cette thèse présente et démontre l'application de la méthodologie GCD à un cas d'utilisation au niveau système, centré sur l'évaluation des options de configuration et d'assemblage dans la conception d'un vaisseau spatial. En utilisant la mission EnVision, sélectionnée en 2021 par le Comité du Programme Scientifique de l'ESA, comme base de conception, la méthodologie GCD a été mise en œuvre dans cette étude de cas d'utilisation, en employant les deux outils logiciels et en montrant les fonctionnalités et applications potentielles futures.

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Mass production and assembly lines are yesterday’s manufacturing methods. They have exhausted Earth’s resources and limited the possibilities of design in terms of both form and material, prompting designers to search for new processes. A new generation of making includes biomimicry-inspired technologies such as 3D printing and parametric simulation, which have transformed the production paradigm. Utilizing nature as industry, this thesis explores the possibility of “growing” designed objects by employing nature’s own processes and resources. It integrates bio materials, generative design and additive manufacturing to produce objects for a post-industrial world. The project outcomes employ natural minerals, crystallization and 3D printing to develop new forms of making, proposing a new suite of tools for designers.

This thesis attempts to resolve the contradictory relationship between the ecological merits of wood construction and the significant material intensity of recent free form timber surface structures. The building industry is now adept in the design and construction of freeform surface architecture, however new challenges have been introduced with the environmentally conscious desire to build these structures in wood. Lacking the formal versatility of steel and concrete, wood introduces a great deal of difficulty in the realization of complex form at an architectural scale. Powerful digital design and fabrication tools have recently made it possible to model, analyze and construct these buildings, but at the cost of heavy structural solutions that involve energy intensive fabrication processes and significant material waste. This approach contradicts the ecological benefits of wood, and raises the question of whether it is possible to achieve free and expressive form in timber surface architecture while maintaining an economy of means and material. This question is addressed through the development of a generative design tool for the creation of material conscious free form timber surface architecture. The formation of the tool is informed by the field of computational morphogenesis, which draws from the natural growth processes of biological structures in the virtual synthesis of form. The tool is conceived as a morphogenetic material system, which consists of a generative algorithm that integrates material, structure and form in a single computational process. Specific material saving techniques deployed in the algorithm draw from existing research in timber shell design and material optimization. Established methods in the use of geodesic lines for the structural patterning of wood shells and stress driven material distribution make up the core concepts deployed in the algorithm. The material system is developed, refined and tested through the design and construction of an experimental free form timber lattice.

The aim of this thesis is to explore the role of the diagrams as generative design tools in architectural design process. Identifying the utilization of the diagrams as infrastructural and organizational elements in the design process, it aims to be concentrate on their potency for generating novel design concepts. The search has been for the possibilities of design processes developed and manipulated not through analytical use of diagrams that represents the already established relationships but through their generative use that is responsible for the proliferation of ideas for novel design concepts. The alteration in the definition of diagrams, their active role in the generation of design ideas, and their progression during the design process, as well as the ways in which they contribute to the delay of formal concerns through the practice are scrutinized. In order to explore the generative and mediating roles of diagrams in architectural design practice, this thesis examines the utilization of diagrams by exemplifying the strategies of UN Studio. Through exploration of their pioneering projects, the M&ouml / bius House, the Arnhem Central Station and the Mercedes Benz Museum, it aims to unfold their design methods regarding diagrams. Diagrams are examined in terms of the way they are utilized and operated from conceptualization to building. While standing at a critical distance, it argues for an architectural design process where design ideas are formed and evolved by utilization of diagrams as generative tools from the initial phases of the design to the actualization of the building.

This thesis attempts to resolve the contradictory relationship between the ecological merits of wood construction and the significant material intensity of recent free form timber surface structures. The building industry is now adept in the design and construction of freeform surface architecture, however new challenges have been introduced with the environmentally conscious desire to build these structures in wood. Lacking the formal versatility of steel and concrete, wood introduces a great deal of difficulty in the realization of complex form at an architectural scale. Powerful digital design and fabrication tools have recently made it possible to model, analyze and construct these buildings, but at the cost of heavy structural solutions that involve energy intensive fabrication processes and significant material waste. This approach contradicts the ecological benefits of wood, and raises the question of whether it is possible to achieve free and expressive form in timber surface architecture while maintaining an economy of means and material. This question is addressed through the development of a generative design tool for the creation of material conscious free form timber surface architecture. The formation of the tool is informed by the field of computational morphogenesis, which draws from the natural growth processes of biological structures in the virtual synthesis of form. The tool is conceived as a morphogenetic material system, which consists of a generative algorithm that integrates material, structure and form in a single computational process. Specific material saving techniques deployed in the algorithm draw from existing research in timber shell design and material optimization. Established methods in the use of geodesic lines for the structural patterning of wood shells and stress driven material distribution make up the core concepts deployed in the algorithm. The material system is developed, refined and tested through the design and construction of an experimental free form timber lattice.

In this master’s thesis we explore the idea of using artificial intelligence in the product design process and seek to develop a conceptual framework for how it can be incorporated to make user customized products more accessible and affordable for everyone. We show how generative deep learning models such as Variational Auto Encoders and Generative Adversarial Networks can be implemented to generate design variations of windows and clarify the general implementation process along with insights from recent research in the field. The proposed framework consists of three parts: (1) A morphological matrix connecting several identified possibilities of implementation to specific parts of the product design process. (2) A general step-by-step process on how to incorporate generative deep learning. (3) A description of common challenges, strategies andsolutions related to the implementation process. Together with the framework we also provide a system for automatic gathering and cleaning of image data as well as a dataset containing 4564 images of windows in a front view perspective.

This thesis deals with the basic principles of the functioning of the technology of generative design, using the method of topology optimization and finding their potential with the capabilities of the application in the field of furniture design. The main outcome is a functional prototype of a low relaxing chair, which specific construction is designed using the method of topology optimization.