Structural reinforcement layout optimization can be a very useful tool in the preliminary stages of design. In this research, sizing optimization techniques are used to generate results very similar to traditional layout optimization techniques with advantages in composite modeling and available strength and stability responses. Both linear and nonlinear sizing-to-design variable relationships are applied to a composite advanced sail design problem with high and low-complexity finite element models. An alternate methodology based on fractionalactorial-design and response surface modeling is also presented with promising results for finding the globally optimum reinforcement layout design. The stiffener layouts obtained from the different approaches are used to define an improved stiffener layout for sizing optimization for minimum weight. A weight savings of more than 19% is obtained over a baseline model using these methodologies.

No description available.

Crowdsourcing involves releasing tasks on the internet for people with diverse backgrounds and skills to solve. Its adoption has come a long way, from scaling up problem-solving to becoming an environment for running complex experiments. Designing tasks to obtain reliable results is not straightforward as it requires many design choices that grow with the complexity of crowdsourcing projects, often demanding multiple trial-and-error iterations to properly configure. These inherent characteristics of crowdsourcing, the complexity of the design space, and heterogeneity of the crowd, set quality control as a major concern, making it an integral part of task design. Despite all the progress and guidelines for developing effective tasks, crowdsourcing still is addressed as an ``art'' rather than an exact science, in part due to the challenges related to task design but also because crowdsourcing allows more complex use cases nowadays, where the support available has not yet caught up with this progress. This leaves researchers and practitioners at the forefront to often rely on intuitions instead of informed decisions. Running controlled experiments in crowdsourcing platforms is a prominent example. Despite their importance, experiments in these platforms are not yet first-class citizens, making researchers resort to building custom features to compensate for the lack of support, where pitfalls in this process may be detrimental to the experimental outcome. In this thesis, therefore, our goal is to attend to the need of moving crowdsourcing from art to science from two perspectives that interplay with each other: providing guidance on task design through experimentation, and supporting the experimentation process itself. First, we select classification problems as a use case, given their importance and pervasive nature, and aim to bring awareness, empirical evidence, and guidance to previously unexplored task design choices to address performance concerns. And second, we also aim to make crowdsourcing accessible to researchers and practitioners from all backgrounds, reducing the requirement of in-depth knowledge of known biases in crowdsourcing platforms, experimental methods, as well as programming skills to overcome the limitations of crowdsourcing providers while running experiments. We start by proposing task design strategies to address workers' performance, quality and time, in crowdsourced classification tasks. Then we distill the challenges associated with running controlled crowdsourcing experiments, propose coping strategies to address these challenges, and introduce solutions to help researchers report their crowdsourcing experiments, moving crowdsourcing forward to standardized reporting.

No description available.

The equations governing the thermodynamic behavior of a military aircraft have been implemented by the Air Force Research Lab (AFRL) and other Integrated Vehicle Energy Technology Demonstration (INVENT) contributors into a cohesive, adaptable, dynamic aircraft simulation program in Mathworks' Simulink®. The resulting model known as the "Tip-to-tail" model meets the design specifications set forth by the INVENT program. The system consists of six intimately linked subsystems that include a propulsion subsystem (PS), air vehicle subsystem (AVS), robust electrical power subsystem (REPS), high power electric actuation subsystem (HPEAS), advanced power and thermal management subsystem (APTMS), and a fuel thermal management subsystem (FTMS). The model's governing equations are augmented with experimental data and supported by defined physical parameters. In order to address the problems associated with the additional power and thermal loads for in more electric aircraft (MEA), this research utilizes exergy analysis and mission-integrated synthesis/design optimization to investigate the potential for improvement in tip-to-tail design/performance. Additionally, this thesis describes the development and integration of higher fidelity transient heat exchanger models for use in the tip-to-tail. Finally, the change in performance due to the integration of new heat exchanger models developed here is presented. Additionally, this thesis discusses the results obtained by performing mission-integrated synthesis/design optimization on the tip-to-tail using heat exchanger design parameters as decision variables. These results show that the performance of the tip-to-thermal management subsystems improves significantly due to the integration of the heat exchanger models. These results also show improvements in vehicle performance due to the mission-integrated optimization. / Master of Science

Pressing of metallic powders is a manufacturing process widely investigated in the research field and in the industry. This thesis project is focused on optimizing the pressing process of cemented carbide powder utilized for the production of triangular shaped cutting tool inserts. In particular, the filling of powder into the die cavity was investigated with respect to different pressing parameters. The aim of the project was to obtain a uniform density distribution of the powder into the die cavity, and hence to reduce the variation of the height of the insert obtaining more precise dimension of the latter. The tests were carried out at the Sandvik Coromant production department which is the creator of the project. The optimization of the pressing process was performed according to the Design of experiments theory. The dynamic of the sintering process was also investigated. The results showed a significant improvement in the filling of the die cavity and a significant decrease of the variation of the height of the inserts. The new insert obtained has more precise dimensions and is able to meet the more demanding requirements of the customers. The results achieved are directly applicable to a larger number of products, and indicate the direction to follow for further development of the manufacturing process.

Cheng and Wu (2001) introduced a method for response surface exploration using only one design by using a 3-level design to first screen a large number of factors and then project onto the significant factors to perform response surface exploration. Previous work generally involved selecting designs based on projection properties first and aliasing structure second. However, having good projection properties is of little concern if the correct factors cannot be identified. We apply Jones and Nachtsheim’s (2009) method for finding optimal designs with minimal aliasing to find 18, 27, and 30-run designs to use for single-step screening and optimization. Our designs have better factor screening capabilities than the designs of Cheng and Wu (2001) and Xu et al. (2004), while maintaining similar D-efficiencies and allowing all projections to fit a full second order model.

Denna studie är en kandidatuppsats om kvalitetssäkring där syftet har varit att undersöka hur tillverkningsföretag arbetar med kvalitetsarbete inom produktion för att öka mervärde åt kund och stärka företags konkurrenskraft. För att besvara syftet har följande frågeställningar utformats: ➢Hur sker prioritering av kvalitetsproblem i tillverkningsföretag? ➢Hur arbetar tillverkande företag med produktkvalitet och hur påverkar det företagets konkurrenskraft. Grunden till studien är en forskningsansats utifrån fallstudiemetoden där existerande vetenskapliga teorier har jämförts med ett verklighetsbaserat kvalitetsproblem. Den teoretiska referensramen har använts för att skapa förståelse och insikt för problemet i en bred kontext. Fallstudien har utförts på ett medelstort formsprutningsföretag i Mellansverige där kvalitetsbrister har resulterat i defekter på detaljer. Studiens ansats var aktionsforskning med både kvalitativa och kvantitativa metoder i form av observation, intervjuer, dokumentation och experiment. Resultaten visar att flera kriteriersom exempelvis rätt prioritering, standardisering och kunskap om processenhar betydelse för kvalitetsarbete samt att kriteriernas relationer är väsentliga för att säkerställa produktkvalitet. Från experimentet framkomdet att direktiv från den vetenskapliga referensramen till parameterinställningar var tvärt emot vad experimentets utfall visade. Det är dock viktigt att uppmärksamma att förändringar av parameterinställningar kan leda till förbättring av en defekt men kan ge uppkomst till en annan. Därmed är det väsentligt att vid komplexa processer ha fungerande kommunikationsamt bred kunskap och insikt iprocessen. Genom att fokusera på kvalitetsarbete kan företag öka kapacitet och kundnöjdhetvilket skapar grunden till konkurrenskraft.

Atualmente o Projeto de Experimentos (DOE) vem sendo largamente utilizado para determinar os fatores de projetos e processos mais significativos afetando uma variável resposta e para estabelecer modelos empíricos entre os fatores, entretanto este método ainda é pouco utilizado e processo de forjamento a quente. O presente trabalho analisa a influência individual e cumulativa de alguns parâmetros controláveis de um processo de forjamento a quente em matriz fechada na força de prensagem necessária para sua realização. Esta análise foi realizada utilizando a técnica de Projeto de Experimentos (DOE). Para a determinação da influência dos parâmetros de processo selecionados na variável resposta do Projeto de Experimentos (DOE), uma série de ensaios variando o lubrificante utilizado, o diâmetro da geratriz e a temperatura de forjamento foram realizados. A variável resposta para o experimento foi definida como a força de prensagem exigida do equipamento para a realização do forjamento. Para este trabalho foi utilizado o material ABNT 4140 fornecido em barras trefiladas de 28,6mm (1.1/8″) posteriormente forjado a quente em matriz fechada. Os resultados mostraram que o fator que mais influencia na força de prensagem para a situação ensaiada é o lubrificante. Os demais fatores, mesmo combinados, não apresentaram uma influência significativa na variável resposta. O presente estudo demonstra que é possível a utilização de técnicas de ajuste e definição de parâmetros de processo de forjamento a quente de maneira confiável e sem a necesside da aplicação dos complexos programas de simulação computacional e os métodos de tentativa e erro ainda presentes na indústria. Finalmente este trabalho reforça a versatilidade do Projeto de Experimentos (DOE) ainda pouco aplicado em processos de forjamento. / Nowadays the Design of Experiments (DOE) has been widely used to determine the most significant project and process factors affecting a response variable and to establish empirical models among the factors, although this method is still little used and the process of hot forging. This work analyzes the individual and cumulative influence of some controllable parameters in a closed die hot forging process in the pressing force required for its realization. This analysis was performed using a Design of Experiments (DOE) method. To determine the influence of variable factors selected in response Design of Experiments (DOE), a several tests varying the lubricant, the diameter of the billet and the forging temperature was performed. The response variable for the experiment was defined as pressing force. For this study, it was used the ABNT 4140 steel provided in drawn bars of 28,6mm (1 1/8 ″). The fator that had more influence over the pressing force was the lubricant. Other factors, whether or not combined, did not show a significant influence on the response variable. This study demonstrates that it is possible use techniques to set hot forging process parameters reliably and without necesside the application of complex computer simulation programs or the trial and error system. Finally, this work reinforces the versatility of Design of Experiments (DOE) wich is still little used in forging processes.

Process development of new complex reactions in the pharmaceutical and fine chemicals industries is challenging, and expensive. The field is beginning to see a bridging between fundamental first-principles investigations, and utilisation of data-driven statistical methods, such as machine learning. Nonetheless, process development and optimisation in these industries is mostly driven by trial-and-error, and experience. Approaches that move beyond these are limited to the well-developed optimisation of continuous variables, and often do not yield physical insights. This thesis describes several new methods developed to address research questions related to this challenge. First, we investigated whether utilising physical knowledge could aid statistics-guided self-optimisation of a C-H activation reaction, in which the optimisation variables were continuous. We then considered algorithmic treatment of the more challenging discrete variables, focussing on solvents. We parametrised a library of 459 solvents with physically meaningful molecular descriptors. Our case study was a homogeneous Rh-catalysed asymmetric hydrogenation to produce a chiral γ-lactam, with conversion and diastereoselectivity as objectives. We adapted a state-of-the-art multi-objective machine learning algorithm, based on Gaussian processes, to utilise the descriptors as inputs, and to create a surrogate model for each objective. The aim of the algorithm was to determine a set of Pareto solutions with a minimum experimental budget, whilst simultaneously addressing model uncertainty. We found that descriptors are a valuable tool for Design of Experiments, and can produce predictive and interpretable surrogate models. Subsequently, a physical investigation of this reaction led to the discovery of an efficient catalyst-ligand system, which we studied by operando NMR, and identified a parametrised kinetic model. Turning the focus then to ligands for asymmetric hydrogenation, we calculated versatile empirical descriptors based on the similarity of atomic environments, for 102 chiral ligands, to predict diastereoselectivity. Whilst the model fit was good, it failed to accurately predict the performance of an unseen ligand family, due to analogue bias. Physical knowledge has then guided the selection of symmetrised physico-chemical descriptors. This produced more accurate predictive models for diastereoselectivity, including for an unseen ligand family. The contribution of this thesis is a development of novel and effective workflows and methodologies for process development. These open the door for process chemists to save time and resources, freeing them up from routine work, to focus instead on creatively designing new chemistry for future real-world applications.