Die heutige und zukünftige Entwicklung beim Entwurf von Axialverdichtern für die Anwendung in Flugzeugtriebwerken ist immer stärker davon geprägt, ein möglichst großes Druckverhältnis mit möglichst wenigen Stufen zu erzeugen. Ziel ist es, möglichst viel Leistung mit möglichst geringem Gewicht umzusetzen, um die Effizienz der Maschine weiter zu verbessern. Um dies zu erreichen, muss eine Erhöhung der Stufendruckverhältnisse und damit eine Erhöhung der Stufenbelastung in Kauf genommen werden. Die höhere Belastung hat jedoch einen Anstieg der Verluste aufgrund der stärker werdenden Sekundärströmungen zur Folge, und wirkt sich zunächst negativ auf die Stabilität und den Wirkungsgrad der Maschine aus. Diese negativen Effekte können nur durch eine Weiterentwicklung der Schaufelgeometrie kompensiert werden. Hierbei stoßen die derzeit benutzten Entwurfsmethoden jedoch an ihre Grenzen. Aus diesem Grund wurde ein neues Verfahren für den dreidimensionalen Entwurf von Rotoren in Axialverdichtern entwickelt. In dieser Arbeit wird dessen Entwicklung präsentiert. Das Verfahren umfasst die systematische Anwendung von Pfeilung und V-Stellung, sowie die dreidimensionale inverse Berechnung der radialen Skelettlinienverteilung. Um damit eine Verbesserung des Rotorwirkungsgrades zu erreichen, müssen vor allem die kritischen wand- bzw. spaltnahen Bereiche optimal an die Strömungsumgebung angepasst werden. Die vorliegende Arbeit beschreibt ausführlich die theoretischen Grundlagen der Rotorströmung und des Rotorentwurfs. Basierend darauf werden für die Umsetzung eines vollständigen dreidimensionalen Schaufelentwurfs zwei Panelverfahren zur Berechnung der dreidimensionalen jedoch reibungslosen Strömung, zur Lösung der Nachrechen- bzw. der Entwurfsaufgabe, entwickelt. Die Panelverfahren werden angewandt, um eine Methodik für den effektiven Einsatz von Pfeilung, V-Stellung und inverser Skelettlinienberechnung für den dreidimensionalen Rotorentwurf festzulegen. Die gewonnenen Erkenntnisse werden anschließend für den Entwurf eines hochbelasteten Rotors in einem einstufigen Niedergeschwindigkeitsverdichter nach dieser neuen Entwurfsmethodik genutzt. Anhand von Ergebnissen aus Rechnungen und Messungen für diesen Rotor wird die Wirksamkeit des Verfahrens demonstriert. / The recent and future design of axial compressors for aero engines is strongly affected by the aim to generate a high pressure ratio with less stages to increase power and reduce weight to achieve an improved efficiency. This can only be achieved when the stage pressure ratio is raised which leads to increased stage loading. But the higher stage loading results in higher losses caused by stronger secondary flows. This has a negative effect on compressor stability and efficiency. To counteract the negative effects enhanced blade geometries are necessary. With the recently used design methods this is hardly to achieve. For that reason a new method for the three-dimensional design of rotors and stators in axial compressors has been developed. This report summarizes that work. The method accounts for the systematic application of sweep and dihedral as well as the three-dimensional inverse calculation of the camber-line distributions along blade height. To achieve improved efficiency the regions close to the end-walls and the tip and hub gap have to be adapted to the flow environment. The recent report described in detail the theoretical background of the compressor blade flow and compressor blade design. Based on that, two inviscid panel methods for the fully three-dimensional design of compressor blades are described. The panel methods are applied to define a methodology for the effective application of sweep, dihedral and inverse camber-line calculation in a three-dimensional blade design process. Afterwards the findings are used to design a highly-loaded single-stage low-speed research compressor rotor. The validity of the presented design method is proven with CFD and test results.

No description available.

Cellular mechanical metamaterials (CMMs) are a special class of materials that consist of microstructural architectures of macroscopic hierarchical frameworks that can have extraordinary properties. These properties largely depend on the topology and arrangement of the unit cells constituting the microstructure. The material hierarchy facilitates the synthesis and design of CMMs on the micro-scale to achieve enhanced properties (i.e., improved strength, toughness, low density) on the component (macro)-scale. However, designing on-demand cellular metamaterials usually requires solving a challenging inverse problem to explore the complex structure-property relations. The first part of this study (Ch. 3) proposes an experience-free and systematic design methodology for microstructures of CMMs using an advanced stochastic searching algorithm called micro-genetic algorithm (μGA). Locally, this algorithm minimizes the computational expense of the genetic algorithm (GA) with a small population size and a conditionally reduced parameter space. Globally, the algorithm employs a new search strategy to avoid local convergence induced by the small population size and the complexity of the parameter space. What's more, inspired by natural evolution in the GA, this study applies the inverse design method with the standard GA (sGA) as a sampling algorithm for intuitively mapping material-property spaces of CMMs, which requires the selection of objective properties and stochastic search of property points within the property space. The mapping methodology utilizing the sGA is proposed in the second part of the study (Ch. 4). This methodology involves a robust strategy that is shown to identify more comprehensive property spaces than traditional mapping approaches. The resulting property space allows designers to acknowledge the limitations of material performance, and select an appropriate class of CMMs based on the difficulty of the realization and fabrication of their microstructures. During the fabrication process, manufacturing defects cause uncertainty in the microstructures, and thus the structural properties. The third part of the study (Ch. 5) investigates the effects of the uncertainty stemming from manufacturing defects on the material property space. To accelerate the uncertainty quantification (UQ) via the Monte Carlo method, this study utilizes a machine learning technique to bypass the expensive simulations to compute properties. In addition to reducing the computational expense of the simulations, the deep learning method has been proven to be practical to accomplish non-intuitive design tasks. Due to the numerous combinations of properties and complex underlying geometries of metamaterials, it is numerically intractable to obtain optimal material designs that satisfy multiple user-defined performance criteria at the same time. Nevertheless, a deep learning method called conditional generative adversarial networks (CGANs) is capable of solving this many-to-many inverse problem. The fourth part of the study (Ch. 6) proposes a new inverse design framework using CGANs to overcome this challenge. Given combinations of target properties, the framework can generate a group of geometric patterns providing these target properties. Therefore, the proposed strategy provides alternative solutions to satisfy on-demand requirements while increasing the freedom in the fabrication process. Besides, with the advances in additive manufacturing (AM), the design space of an engineering material can be further enlarged by multi-scale topology optimization. As the interplay between microstructure and macrostructure drives the overall mechanical performance of engineering materials, it is necessary to develop a multi-scale design framework to optimize structural features in these two scales simultaneously. The final part of the study (Ch. 7) presents a concurrent multi-scale topology optimization method of CMMs. Structures in micro and macro scales are optimized concurrently by utilizing sequential quadratic programming (SQP) with the Solid Isotropic Material with Penalization (SIMP) method and a numerical homogenization approach. / Doctor of Philosophy / Cellular materials widely exist in natural biological systems such as honeycombs, bones, and wood. Recent advances in additive manufacturing have enabled us to fabricate these materials with high precision. Inspired by architectures in nature, cellular mechanical metamaterials (CMMs) have been introduced recently as a new class of architected systems. The materials are formed by hierarchical microstructural topologies, which have a decisive influence on the structural performance at the macro-scale. Therefore, the design of these materials primarily focuses on the geometric arrangement of their microstructures rather than the chemical composition of their base material. Tailoring the microstructures of these materials can lead to several outstanding features, such as high stiffness and strength, low density, and high energy absorption. However, it is challenging to design microstructures that satisfy user-defined requirements for properties and material costs. This is mainly due to the trade-off between the accuracy and computing times of the optimization process. In the first part of this study (Ch. 3), a design framework is proposed to overcome this issue. The framework employs a global search algorithm called the genetic algorithm (GA). With a newly designed search algorithm, the framework reduces errors between target and optimized material properties while improving computational efficiency. Inspired by the algorithm behind the GA, the second part of the study (Ch. 4) employs a similar algorithm to identify a material property chart demonstrating all possible combinations of mechanical properties of CMMs. Each axis of the material property chart corresponds to a selected mechanical property, such as Young's modulus or Poisson's ratio, along different directions. The boundary of the property space helps designers understand material performance limitations and make informed decisions in engineering practices. In the fabrication process, unexpected material properties might be achieved due to defects and tolerances in additive manufacturing (AM), such as uneven surfaces, shrinkage of pores, etc. The third part of the study (Ch. 5) investigates the uncertainty propagation on mechanical properties as a result of these manufacturing defects. To investigate the uncertainty propagation problem efficiently, the study uses a deep learning method to predict the variations (stochasticity) of properties. Consequently, the material property space boundary also varies with the uncertainty of properties. In addition to their computational efficiency, deep learning methods are beneficial for solving many-to-many inverse design problems. Traditionally, the global and local search/optimization methods retrieve alternative optimal solutions in their Pareto front set, where each solution is considered to be equally good. A deep learning method called conditional generative adversarial networks (CGANs) can bypass the property calculation to accelerate the simulation process while obtaining a group of candidates with on-demand properties. The fourth part of the study (Ch. 6) employs CGANs to build a new inverse design framework to increase flexibility in the fabrication process by generating alternative solutions for the microstructures of CMMs. Besides, as fabrication technologies have advanced, designing engineering systems has become increasingly complex. Material design is now not only focused on meeting micro-scale requirements but also addressing needs at multiple scales. The interaction between the microstructure (small-scale) and macrostructure (large-scale) significantly influences the overall performance of engineering systems. To optimize structures effectively, there is a need for a design framework that considers these two scales simultaneously. Thus, the final part of the study (Ch. 7) introduces a method called concurrent multi-scale topology optimization. To obtain the extreme performance of a multi-scale structure, this approach optimizes its structure at both micro- and macro-scales concurrently, using gradient-based optimization algorithms with density-based property determination methods in the two scales.

Nanophotonic and plasmonic research have seen many breakthroughs lately which has created a demand for automated design algorithms to optimize optical elements at the nanometer scale. This work focuses on plasmonic nanostructures that are small metal particles interacting with electromagnetic radiation on length scales typically less than the wavelength. Plasmonic effects from nanostructures are used for enhancing and manipulating electromagnetic fields at the nanometer scale which have seen many applications in sensing requiring an ultra-high sensitivity and a small resolution. This work is about how deep learning methods can be used for designing plasmonic gold nanostructures. In particular, we investigate how convolutional neural networks can be used to predict the optical properties of nanostructures and how conditional generative adversarial networks (cGAN) can be used for designing structures with desired optical properties. The data in this work consist of images with differently shaped nanostructures and the corresponding spectral data for the scattering cross section, the absorption cross section, the polarization rotation and the polarization ellipticity. Utilizing the convolutional EfficientNet architectures, we train a forward model to predict the spectral data of anisotropically shaped nanostructures with images of the structure shape as input. We achieve a mean squared error of 2.5 × 10−4, 2.5 ×10−4, 6.0 ×10−4, and 4.9 ×10−4 respectively for each variable which agrees with the literature for similar problems. For the inverse design models, we show that label projection can be used to improve the results of two common GAN architectures in combination with a novel label embedding network. We use the Wasserstein GAN method with gradient penalty for training the models to generate images of nanostructure shapes conditioned on spectral data. The best model achieves a pixelwise mean absolute error of 4.9×10−3 and an estimated spectral mean absolute error of 8.4×10−3 between original and generated images when trained on a dataset containing cylindrical dimer structures. Furthermore, we have shown that the pixelwise mean absolute error is reduced when more conditional input variables are added to the model and that the model can learn different nanostructure shapes when trained on a large dataset containing different anisotropic gold nanostructure shapes. The best pixelwise mean absolute error found is 1.1×10−2 and the estimated spectral mean absolute error is 1.7 × 10−2 on the full dataset using all available input data.

<div>The rapid development of topology optimization in photonics has greatly expanded the number of photonic structures with extraordinary performance. The optimization is usually solved by using a gradient-based optimization algorithm, where gradients are evaluated by the adjoint sensitivity analysis. While the adjoint sensitivity analysis has been demonstrated to provide reliable gradients for designs of dielectrics, there has not been too much success in plasmonics. The difficulty of obtaining accurate field solutions near sharp edges and corners in plasmonic structures, and the strong field enhancement jointly increase the numerical error of gradients, leading to failure of convergence for any gradient-based algorithm. </div><div> </div><div>We present a new method of calculating accurate sensitivity with the FDTD method by direct differentiation of the time-marching system in frequency domain. This new method supports general frequency-domain objective functions, does not relay on implementation details of the FDTD method, works with general isotropic materials and can be easily incorporated into both level-set-based and density-based topology optimizations. The method is demonstrated to have superior accuracy compared to the traditional continuous sensitivity. Next, we present a framework to carry out density-based topology optimization using our new sensitivity formula. We use the non-linear material interpolation to counter the rough landscape of plasmonics, adopt the filteringand-projection regularization to ensure manufacturability and perform the optimization with a continuation scheme to improve convergence. </div><div> </div><div>We give two examples involving reconstruction of near fields of plasmonic structures to illustrate the robustness of the sensitivity formula and the optimization framework. In the end, we apply our method to generate a rectangular temperature profile in the recording medium of the HAMR system. </div>

With the demand of computing power from electronic chips on a constant rise, innovative methods are needed for effective and efficient thermal management. Forced convection cooling through an array of micro pin-fins acts not only as a heat sink, but also allows for the electrical interconnection between stacked layers of integrated circuits. This work performs a multi-objective optimization of three shapes of pin-fins to maximize the efficiency of this cooling system. An inverse design approach that allows for the design of cooling configurations without prior knowledge of thermal mapping was proposed and validated. The optimization study showed that pin-fin configurations are capable of containing heat flux levels of next generation electronic chips. It was also shown that even under these high heat fluxes the structural integrity is not compromised. The inverse approach showed that configurations exist that are capable of cooling heat fluxes beyond those of next generation chips. Thin film heat spreaders made of diamond and graphene nano-platelets were also investigated and showed that further reduction in maximum temperature, increase in temperature uniformity and reduction in thermal stresses are possible.

Ce travail porte sur le développement de méthodes numériques innovantes pour la conception aéro-acoustique optimale de forme des configurations supersoniques. Ce manuscrit présente tout d'abord l'analyse et le développement des approches numériques pour la prévision du bang sonique . Le couplage du calcul CFD tridimensionnel en champ proche prenant en compte la décomposition multipolaire de Fourier et la propagation atmosphérique basée sur un algorithme de tracé de rayons est amélioré par l’intégration d'un processus automatique d' adaptation anisotrope de maillage. La deuxième partie de ce travail se concentre sur l’élaboration et l'application des techniques de conception pour l'optimisation d'une configuration aile-fuselage supersonique. Un module de conception inverse, AIDA , fournit à partir d'une signature acoustique cible au sol à faible bang sonique la géométrie de la configuration correspondante. Pour améliorer a la fois les performances acoustique et aérodynamique, des techniques d'optimisation directes de forme sont utilisées pour résoudre des problèmes d'optimisation mono et multi- disciplinaires et une analyse détaillée est réalisée. Des stratégies innovantes basées sur la coopération et les jeux compétitifs sont enfin appliquées au problème d'optimisation multidisciplinaire offrant une alternative aux algorithmes traditionnels MDO . L’hybridation de ces deux stratégies ouvre la voie a une nouvelle façon d'explorer le front de Pareto de manière efficace. Celle-ci est mise en application sur un cas pratique. / This work addresses the development of original numerical methods for the aero-acoustic optimal shape design of supersonic configurations. The first axis of the present research is the enhancement of numerical approaches for the prediction of sonic boom. The three dimensional CFD near-field prediction matched using a multipole decomposition approach coupled with atmospheric propagation using on a ray-tracing algorithm is improved by the integration of an automated anisotropic mesh adaptation process. The second part of this work focuses on the formulation and development of design techniques for the optimization of a supersonic wing-body configuration. An inverse design module, AIDA, is able to determine an equivalent configuration provided a target shaped signature at ground level corresponding to a low-boom profile. In order to improve both the aerodynamic and the acoustic performance, direct shape optimization techniques are used to solve single and multi-disciplinary optimization problems and a detailed analysis is carried out. At last, innovative strategies based on cooperation and competitive games are then applied to the multi-disciplinary optimization problem providing an alternative to traditional MDO algorithms. Hybridizing the two strategies opens a new efficient way to explore the Pareto front and this is shown on a practical case.

<p>The demand for the faster information transport and better computational abilities is ever increasing. In the last few decades, the electronic industry has met this requirement by increasing the number of transistors per square inch. This lead to the scaling of devices to tens of nm. However, the speed of the electronics is limited to few GHz. Using light, the operating speed of photonic devices can be much larger than GHz. But the photonic devices are diffraction limited and hence the size of photonic device is much larger than the electronic components. Plasmonics is an emerging field with light-induced surface excitations, and can manipulate the light at nanoscale. It can bridge the gap between electronics and photonics. </p> <p>With the present scaling of devices to few nm, the scientific community is looking for alternatives for continued progress. This has opened up several promising routes recently, including two-dimensional materials, quantum computing, topological computing, spintronics and valleytronics. The discovery of graphene has led to the immense interest in the field of two-dimensional materials. Two dimensional-materials have extraordinary properties compared to its bulk. This work discusses the applications of plasmonics in this emerging field of two-dimensional materials and for heat assisted magnetic recording.</p> <p>Black phosphorus is an emerging low-direct bandgap two-dimensional semiconductor, with anisotropic optical and electronic properties. It has high mobility and is promising for photo detection at infrared wavelengths due to its low band gap. We demonstrate two different plasmonic designs to enhance the photo responsivity of black phosphours by localized surface plasmons. We use bowtie antenna and bowtie apertures to increase the absorption and polarization selectivity respectively. Plasmonic structures are designed by numerical electromagnetic simulations, and are fabricated to experimentally demonstrate the enhanced photo responsivity of black phosphorus. </p> <p>Next, we look at another emerging two-dimensional material, bismuth telluride selenide (Bi₂Te₂Se). It is a topological insulator with an insulating bulk but conducting electronic surface states. These surface states are Dirac like, similar to graphene and can lead to exotic plasmonic phenomena. We investigated the optical properties of Bi₂Te₂Se and found that the bulk is plasmonic below 650 nm wavelength. We study the distinct surface plasmons arising from the bulk and surface state of the topological insulator, Bi₂Te₂Se. The propagating surface plasmons at a nanoscale slit in Bi₂Te₂Se are imaged using near-field scanning optical microscopy. The surface state plasmons are studied with a below band gap excitation of 10.6 µm wavelength and the surface plasmons of the bulk are studied with a visible wavelength of 633 nm. The surface state plasmon wavelength is 100 times shorter than the incident wavelength in sharp contrast to the plasmon wavelength of the bulk. </p> <p>Next, we look at the application of plasmonics in heat assisted magnetic recording (HAMR). HAMR is one of the next generation data storage technology that can increase the areal density to beyond 1 Tb/in². Near-field transducer (NFT) is a key component of the HAMR system that locally heats the recording medium by concentrating light below the diffraction limit using surface plasmons. In this work, we use density-based topology optimization for inverse design of NFT for a desired temperature profile in the recording medium. We first perform an inverse thermal calculation to obtain the required volumetric heat generation (electric field) for a desired temperature profile. Then an inverse electromagnetic design of NFT is performed for achieving the desired electric field. NFT designs for both generating a small heated spot size and a heated spot with desired aspect ratio in recording medium are demonstrated. The effect of waveguide, write pole and moving recording medium on the heated spot size is also investigated. </p>

Die heutige und zukünftige Entwicklung beim Entwurf von Axialverdichtern für die Anwendung in Flugzeugtriebwerken ist immer stärker davon geprägt, ein möglichst großes Druckverhältnis mit möglichst wenigen Stufen zu erzeugen. Ziel ist es, möglichst viel Leistung mit möglichst geringem Gewicht umzusetzen, um die Effizienz der Maschine weiter zu verbessern. Um dies zu erreichen, muss eine Erhöhung der Stufendruckverhältnisse und damit eine Erhöhung der Stufenbelastung in Kauf genommen werden. Die höhere Belastung hat jedoch einen Anstieg der Verluste aufgrund der stärker werdenden Sekundärströmungen zur Folge, und wirkt sich zunächst negativ auf die Stabilität und den Wirkungsgrad der Maschine aus. Diese negativen Effekte können nur durch eine Weiterentwicklung der Schaufelgeometrie kompensiert werden. Hierbei stoßen die derzeit benutzten Entwurfsmethoden jedoch an ihre Grenzen. Aus diesem Grund wurde ein neues Verfahren für den dreidimensionalen Entwurf von Rotoren in Axialverdichtern entwickelt. In dieser Arbeit wird dessen Entwicklung präsentiert. Das Verfahren umfasst die systematische Anwendung von Pfeilung und V-Stellung, sowie die dreidimensionale inverse Berechnung der radialen Skelettlinienverteilung. Um damit eine Verbesserung des Rotorwirkungsgrades zu erreichen, müssen vor allem die kritischen wand- bzw. spaltnahen Bereiche optimal an die Strömungsumgebung angepasst werden. Die vorliegende Arbeit beschreibt ausführlich die theoretischen Grundlagen der Rotorströmung und des Rotorentwurfs. Basierend darauf werden für die Umsetzung eines vollständigen dreidimensionalen Schaufelentwurfs zwei Panelverfahren zur Berechnung der dreidimensionalen jedoch reibungslosen Strömung, zur Lösung der Nachrechen- bzw. der Entwurfsaufgabe, entwickelt. Die Panelverfahren werden angewandt, um eine Methodik für den effektiven Einsatz von Pfeilung, V-Stellung und inverser Skelettlinienberechnung für den dreidimensionalen Rotorentwurf festzulegen. Die gewonnenen Erkenntnisse werden anschließend für den Entwurf eines hochbelasteten Rotors in einem einstufigen Niedergeschwindigkeitsverdichter nach dieser neuen Entwurfsmethodik genutzt. Anhand von Ergebnissen aus Rechnungen und Messungen für diesen Rotor wird die Wirksamkeit des Verfahrens demonstriert. / The recent and future design of axial compressors for aero engines is strongly affected by the aim to generate a high pressure ratio with less stages to increase power and reduce weight to achieve an improved efficiency. This can only be achieved when the stage pressure ratio is raised which leads to increased stage loading. But the higher stage loading results in higher losses caused by stronger secondary flows. This has a negative effect on compressor stability and efficiency. To counteract the negative effects enhanced blade geometries are necessary. With the recently used design methods this is hardly to achieve. For that reason a new method for the three-dimensional design of rotors and stators in axial compressors has been developed. This report summarizes that work. The method accounts for the systematic application of sweep and dihedral as well as the three-dimensional inverse calculation of the camber-line distributions along blade height. To achieve improved efficiency the regions close to the end-walls and the tip and hub gap have to be adapted to the flow environment. The recent report described in detail the theoretical background of the compressor blade flow and compressor blade design. Based on that, two inviscid panel methods for the fully three-dimensional design of compressor blades are described. The panel methods are applied to define a methodology for the effective application of sweep, dihedral and inverse camber-line calculation in a three-dimensional blade design process. Afterwards the findings are used to design a highly-loaded single-stage low-speed research compressor rotor. The validity of the presented design method is proven with CFD and test results.

Additive manufacturing (AM) technology is a key emerging field transforming how customized products with complex shapes are manufactured. AM is the process of layering materials to produce objects from three-dimensional (3D) models. AM technology can be used to print objects with complicated geometries and a broad range of material properties. However, the issue of ensuring the quality of printed products during the process remains an obstacle to industry-level adoption. Furthermore, the characteristics of AM processes typically involve complex process dynamics and interactions between machine parameters and desired qualities. The issues associated with quality assurance in AM processes underscore the need for research into smart quality assurance systems. To study the complex physics behind process interaction challenges in AM processes, this dissertation proposes the development of a data-driven smart quality assurance framework that incorporates in-process sensing and machine learning-based modeling by correlating the relationships among parameters, signatures, and quality. High-fidelity AM simulation data and the increasing use of sensors in AM processes help simulate and monitor the occurrence of defects during a process and open doors for data-driven approaches such as machine learning to make inferences about quality and predict possible failure consequences. To address the research gaps associated with quality assurance for AM processes, this dissertation proposes several data-driven approaches based on the design of experiments (DoE), forward prediction modeling, and an inverse design methodology. The proposed approaches were validated for AM processes such as fused filament fabrication (FFF) using polymer and hydrogel materials and laser powder bed fusion (LPBF) using common metal materials. The following three novel smart quality assurance systems based on a parameter–signature–quality (PSQ) framework are proposed: 1. A customized in-process sensing platform with a DOE-based process optimization approach was proposed to learn and optimize the relationships among process parameters, process signatures, and parts quality during bioprinting processes. This approach was applied to layer porosity quantification and quality assurance for polymer and hydrogel scaffold printing using an FFF process. 2. A data-driven surrogate model that can be informed using high-fidelity physical-based modeling was proposed to develop a parameter–signature–quality framework for the forward prediction problem of estimating the quality of metal additive-printed parts. The framework was applied to residual stress prediction for metal parts based on process parameters and thermal history with reheating effects simulated for the LPBF process. 3. Deep-ensemble-based neural networks with active learning for predicting and recommending a set of optimal process parameter values were developed to optimize optimal process parameter values for achieving the inverse design of desired mechanical responses of final built parts in metal AM processes with fewer training samples. The methodology was applied to metal AM process simulation in which the optimal process parameter values of multiple desired mechanical responses are recommended based on a smaller number of simulation samples. / Doctor of Philosophy / Additive manufacturing (AM) is the process of layering materials to produce objects from three-dimensional (3D) models. AM technology can be used to print objects with complicated geometries and a broad range of material properties. However, the issue of ensuring the quality of printed products during the process remains a challenge to industry-level adoption. Furthermore, the characteristics of AM processes typically involve complex process dynamics and interactions between machine parameters and the desired quality. The issues associated with quality assurance in AM processes underscore the need for research into smart quality assurance systems. To study the complex physics behind process interaction challenges in AM processes, this dissertation proposes a data-driven smart quality assurance framework that incorporates in-process sensing and machine-learning-based modeling by correlating the relationships among process parameters, sensor signatures, and parts quality. Several data-driven approaches based on the design of experiments (DoE), forward prediction modeling, and an inverse design methodology are proposed to address the research gaps associated with implementing a smart quality assurance system for AM processes. The proposed parameter–signature–quality (PSQ) framework was validated using bioprinting and metal AM processes for printing with polymer, hydrogel, and metal materials.