<p dir="ltr">For additive manufacturing to be a viable method to build metal parts for industries such as nuclear, the manufactured parts must be of higher quality and have lower variation in said quality than what can be achieved today. This high variation in quality bars the techniques from being used in high safety tolerance fields, such as nuclear. If this obstacle could be overcome, the benefits of additive manufacturing would be in lower cost for complex parts, as well as the ability to design and test parts in a very short timeframe, as only the CAD model needs to be created to manufacture the part. In this study, work to achieve this lower variation of quality was approached in two ways. The first was in the development of surrogate models, utilizing machine learning, to predict the end quality of additively manufactured parts. This was done by using experimental data for the mechanical properties of built parts as outputs to be predicted, and in-situ signals captured during the manufacturing process as inputs to the model. To capture the in-situ signals, cameras were used for thermal and optical imaging, leveraging the natural layer-by-layer manufacturing method used in AM techniques. The final models were created using support vector machine and gaussian process regression machine learning algorithms, giving high correlations between the insitu signals and mechanical properties of relative density, elongation to fracture, uniform elongation, and the work hardening exponent. The second approach to this study was in the development of a reduced order model for a computer simulation of an AM build. For project, a ROM was built inside the MOOSE framework, and was developed for an AM model designed by the MOOSE team, using proper orthogonal decomposition to project the problem onto a lower dimensional subspace, using POD to design the reduced basis subspace. The ROM was able to achieve a reduction to 1% the original dimensionality of the problem, while only allowing 2-5% relative error associated with the projection.</p>

L’objet de la thèse est la modélisation de systèmes hétérogènes intégrant différents domaines de la physique et à signaux mixtes, numériques et analogiques (AMS). Une étude approfondie de différentes techniques d’extraction et de calibration de modèles comportementaux de composants analogiques à différents niveaux d’abstraction et de précision est présentée. Cette étude a mis en lumière trois approches principales qui ont été validées par la modélisation de plusieurs applications issues de divers domaines: un amplificateur faible bruit (LNA), un capteur chimique basé sur des ondes acoustiques de surface (SAW), le développement à plusieurs niveaux d’abstraction d’un capteur CMOS vidéo, et son intégration dans une plateforme industrielle. Les outils développés sont basés sur les extensions AMS du standard IEEE 1666 SystemC mais les techniques proposées sont facilement transposables à d’autres langages tels que VHDL-AMS ou Verilog-AMS utilisés en conception de dispositifs mixtes. / The thesis objective is the modeling of heterogeneous systems. Such systems integrate different physical domains (mechanical, chemical, optical or magnetic) therefore integrate analog and mixed- signal (AMS) parts. The aim is to provide a methodology based on high-level modeling for assisting both the design and the verification of AMS systems. A study on different techniques for extracting behavioral models of analog devices at different abstraction levels and computational weights is presented. Three approaches are identified and regrouped in three techniques. These techniques have been validated through the virtual prototyping of different applications issued from different domains: a low noise amplifier (LNA), a surface acoustic wave-based (SAW) chemical sensor, a CMOS video sensor with models developed at different abstraction levels and their integration within an industrial platform. The flows developed are based on the AMS extensions of the SystemC (IEEE 1666) standard but the methodologies can be implemented using other Analog Hardware Description Languages (VHDL-AMS, Verilog-AMS) typically used for mixed-signal microelectronics design.

Im Zuge der Miniaturisierung werden mechanische Nichtlinearitäten immer wichtiger für die Auslegung und Optimierung von mikromechanischen Drehratensensoren. Die vorliegende Arbeit beschäftigt sich mit neuen Simulationsmethoden zur Beschreibung dieser mechanischen Nichtlinearitäten. Die Methoden werden mit Benchmark-Simulationen und Messergebnissen validiert. Die Genauigkeit der neuen Simulationsmethoden erlaubt den Einsatz in der Designoptimierung von kommerziellen MEMS Drehratensensoren. / In this thesis, new simulation methods for the mechanical nonlinearities in microelectromechanical gyroscopes are developed and validated with benchmark simulations and experimental results. The benchmark simulations use transient finite element analysis that consider geometric nonlinear effects. Experimental results are from Laser Doppler Vibrometry and electrical measurements on wafer level. Two different simulation methods, the energy- and stiffness-based approach, are compared with respect to numerical performance and accuracy. In order to evaluate these methods, four different mechanical structures are taken into account: a doubly-clamped beam, a gyroscope test structure and two state-of-the-art gyroscopes with 1 and 2 axes. For the accuracy measurement, the simulated frequency shifts of modes are compared to the true frequency shifts that are developed from either benchmark simulation, Laser Doppler Vibrometry or electrical measurement. The presented methods allow to predict the frequency shift of modes accurately and with a minimum of computational cost. Furthermore, the methodologies allow to generate modal reduced order models which are compatible with common model order reduction in the field. This makes it possible to incorporate mechanical nonlinearity in already established reduced order models of gyroscopes. The simulation and modeling strategies are applicable for generic actuated structures that can be also in different fields of study such as the aerospace and earthquake engineering.