This project was a test of concept for an external heating system for Big Area Additive Manufacturing (BAAM) Fused Deposition Modeling (FDM) 3D printers. To goal of the heating system was to prevent or mitigate delamination and warping in BAAM FDM prints by propelling warm air onto printed layers while not interfering with prior functions of the 3D printer.

<p>Artificial neural networks, including deep neural networks, play a central role in data-driven science due to their superior learning capacity and adaptability to different tasks and data structures. However, although quantitative uncertainty analysis is essential for training and deploying reliable data-driven models, the uncertainties in neural networks are often overlooked or underestimated in many studies, mainly due to the lack of a high-fidelity and computationally efficient uncertainty quantification approach. In this work, a novel uncertainty analysis scheme is developed. The Gaussian mixture model is used to characterize the probability distributions of uncertainties in arbitrary forms, which yields higher fidelity than the presumed distribution forms, like Gaussian, when the underlying uncertainty is multimodal, and is more compact and efficient than large-scale Monte Carlo sampling. The fidelity of the Gaussian mixture is refined through adaptive scheduling of the width of each Gaussian component based on the active assessment of the factors that could deteriorate the uncertainty representation quality, such as the nonlinearity of activation functions in the neural network. </p> <p>Following this idea, an adaptive Gaussian mixture scheme of nonlinear uncertainty propagation is proposed to effectively propagate the probability distributions of uncertainties through layers in deep neural networks or through time in recurrent neural networks. An adaptive Gaussian mixture filter (AGMF) is then designed based on this uncertainty propagation scheme. By approximating the dynamics of a highly nonlinear system with a feedforward neural network, the adaptive Gaussian mixture refinement is applied at both the state prediction and Bayesian update steps to closely track the distribution of unmeasurable states. As a result, this new AGMF exhibits state-of-the-art accuracy with a reasonable computational cost on highly nonlinear state estimation problems subject to high magnitudes of uncertainties. Next, a probabilistic neural network with Gaussian-mixture-distributed parameters (GM-PNN) is developed. The adaptive Gaussian mixture scheme is extended to refine intermediate layer states and ensure the fidelity of both linear and nonlinear transformations within the network so that the predictive distribution of output target can be inferred directly without sampling or approximation of integration. The derivatives of the loss function with respect to all the probabilistic parameters in this network are derived explicitly, and therefore, the GM-PNN can be easily trained with any backpropagation method to address practical data-driven problems subject to uncertainties.</p> <p>The GM-PNN is applied to two data-driven condition monitoring schemes of manufacturing processes. For tool wear monitoring in the turning process, a systematic feature normalization and selection scheme is proposed for the engineering of optimal feature sets extracted from sensor signals. The predictive tool wear models are established using two methods, one is a type-2 fuzzy network for interval-type uncertainty quantification and the other is the GM-PNN for probabilistic uncertainty quantification. For porosity monitoring in laser additive manufacturing processes, convolutional neural network (CNN) is used to directly learn patterns from melt-pool patterns to predict porosity. The classical CNN models without consideration of uncertainty are compared with the CNN models in which GM-PNN is embedded as an uncertainty quantification module. For both monitoring schemes, experimental results show that the GM-PNN not only achieves higher prediction accuracies of process conditions than the classical models but also provides more effective uncertainty quantification to facilitate the process-level decision-making in the manufacturing environment.</p><p>Based on the developed uncertainty analysis methods and their proven successes in practical applications, some directions for future studies are suggested. Closed-loop control systems may be synthesized by combining the AGMF with data-driven controller design. The AGMF can also be extended from a state estimator to the parameter estimation problems in data-driven models. In addition, the GM-PNN scheme may be expanded to directly build more complicated models like convolutional or recurrent neural networks.</p>

This dissertation provides a novel methodology to assess the impact of additive manufacturing (AM) on the aerospace supply chain. The focus is serialized production of structural parts for the aeroengine. This methodology has three fundamental steps. First, a screening heuristic is used to identify which parts and assemblies would be candidates for AM displacement. Secondly, the production line is characterized and evaluated to understand how these changes in the bill of material might impact plant workflow, and ultimately, part and assembly cost. Finally, the third step employs an integer linear program (ILP) to predict the impact on the supply chain network. The network nodes represent the various companies – and depending upon their tier – each tier has a dedicated function. The output of the ILP is the quantity and connectivity of these nodes between the tiers.<br><br>It was determined that additive manufacturing can be used to displace certain conventional manufacturing parts and assemblies as additive manufacturing’s technology matures sufficiently. Additive manufacturing is particularly powerful if adopted by the artifact’s design authority (usually the original equipment manufacturer – OEM) since it can then print its own parts on demand. Given this sourcing flexibility, these entities can in turn apply pricing pressure on its suppliers. This phenomena increasing has been seen within the industry.

In recent years, additive manufacturing (AM) has become one of the most revolutionary and promising technologies in manufacturing. The process of making a product layer by layer is also often referred to as 3D printing. Once employed purely for prototyping, AM is now increasingly used for small series production, for example in aerospace applications. The paper starts with a motivation for AM in hydraulic applications and the development of an AM internal gear pump. For a better understanding of the manufacturing process, a brief introduction to AM highlighting the advantages and challenges is given. The AM internal gear pump is part of an electrohydraulic power pack, which is used to power an electrohydraulic actuator (EHA). The power pack contains all necessary peripherals to realise the hydraulic system of the EHA. The AM process allows for new design possibilities, but the process differs strongly compared to subtractive manufacturing processes and therefore is outlined here. The paper concludes by presenting measurement results of the AM internal gear pump.

No description available.

<p>This dissertation presents research work related to laser micro sintering, laser micro machining and laser-induced plasma. Firstly, we present extensive experimental studies of double-pulse laser micro sintering (DP-LMS), which typically utilizes the high pressure generated by laser-induced plasma over the powder bed surface to promote molten flow and enhance densification. Chapter 2 shows a single-track experimental study of the DP-LMS process using cobalt powder. The related fundamental mechanisms and effects of different laser parameters on the sintering results are analyzed with the help of <em>in-situ</em> time-resolved temperature measurements. Chapter 3 shows a multi-track experimental study of the DP-LMS process using iron powder. The sintered materials are characterized via the top surface porosity, elemental composition, grain microstructure, nanohardness and metal phase. Three strategic guidelines for laser parameter selection are summarized in the end. Chapter 4 shows time-resolved imaging and OES measurements for plasma induced during DP-LMS. The plasma temperature and free electron number density are deduced by its optical emission spectra (OES). These three chapters have clearly demonstrated DP-LMS can produce much more continuous and densified materials than LMS only using the sintering or pressing laser pulses.</p> <p><br></p> <p>Then, we present laser micro grooving of silicon carbide (SiC) in Chapter 5 by ultrasound-assisted water-confined laser micromachining (UWLM), in comparison with laser machining in water without ultrasound and laser machining in air. UWLM applies <em>in-situ</em> ultrasound to the water-immersed workpiece surface to improve the machining quality and/or productivity. Time-resolved water pressure measurements are carried out to help analyze relevant mechanisms. It has been demonstrated UWLM can be a competitive approach to produce high-quality micro grooves on SiC. The crack problem appears to be effectively solved using a high pulse repetition rate.</p> <p><br></p> <p>Finally, we report a double-front phenomenon for plasma induced by high-intensity nanosecond laser ablation of aluminum in Chapter 6. An additional plasma front is observed via an intensified CCD (ICCD) camera, which propagates very fast at the beginning but stops propagating soon after the laser pulse mostly ends. Its formation could be caused by the inverse bremsstrahlung absorption of laser energy by the ionized ambient gas. Three possible mechanisms on how the ambient gas breakdown is initiated are proposed. </p>

[ES] En los últimos años, la fabricación aditiva a través de la extrusión de materiales ha experimentado un desarrollo y adopción acelerados gracias a la amplia disponibilidad de máquinas y materiales de bajo costo. El tamaño de estas máquinas se ha reducido del tamaño del taller al tamaño del escritorio, lo que permite su uso en configuraciones de oficina o en el hogar. Este cambio ha permitido la adopción de la tecnología por la gama más amplia de usuarios que nunca, con o sin experiencia en diseño de ingeniería. Este nuevo paradigma ha creado el desafío de cómo habilitar que estos nuevos usuarios aprovechen las capacidades proporcionadas por esta tecnología. Esta tecnología permite la creación de geometrías complejas y productos personalizados con un coste inferior a los procesos de fabricación convencionales. Además, la gran cantidad de usuarios dispuestos a compartir sus diseños permite encontrar soluciones de diseño desde otros diseñadores. Sin embargo, la amplia gama de configuraciones de máquina, parámetros y materiales requiere brindar soporte para obtener resultados exitosos para cualquier combinación. Esta tesis aborda este desafío identificando las características de diseño y fabricación a considerar e investigando las consideraciones mecánicas y de pos procesamiento. Se propone y evalúa un nuevo marco de diseño que permite a los nuevos usuarios aprovechar las capacidades y considerar las limitaciones. Esta investigación encuentra que es posible crear un conjunto de herramientas de diseño que permita a los usuarios no capacitados diseñar productos utilizando la complejidad habilitada por la tecnología al tiempo que garantiza la funcionalidad y la capacidad de fabricación del producto. / [CA] En els últims anys, la fabricació additiva a través de l'extrusió de materials ha experimentat un desenvolupament i adopció accelerats gràcies a l'àmplia disponibilitat de màquines i materials de baix cost. La grandària d'aquestes màquines s'ha reduït de la grandària del taller a la grandària de l'escriptori, la qual cosa permet el seu ús en configuracions d'oficina o en a casa. Aquest canvi ha permés l'adopció de la tecnologia per la gamma més àmplia d'usuaris que mai, amb o sense experiència en disseny o enginyeria. Aquest nou paradigma ha creat el desafiament de com habilitar que aquests nous usuaris aprofiten les capacitats proporcionades per aquesta tecnologia. Aquesta tecnologia permet la creació de geometries complexes i productes personalitzats amb un cost inferior als processos de fabricació convencionals. A més, la gran quantitat d'usuaris disposats a compartir els seus dissenys permet trobar solucions de disseny des d'altres dissenyadors. No obstant això, l'àmplia gamma de configuracions de màquina, paràmetres i materials requereix brindar suport per a obtindre resultats reeixits per a qualsevol combinació. Aquesta tesi aborda aquest desafiament identificant les característiques de disseny i fabricació a considerar i investigant les consideracions mecàniques i de post processament. Es proposa i avalua un nou marc de disseny que permet als nous usuaris aprofitar les capacitats i considerar les limitacions. Aquesta investigació troba que és possible crear un conjunt d'eines de disseny que permeta als usuaris no capacitats dissenyar productes utilitzant la complexitat habilitada per la tecnologia al mateix temps que garanteix la funcionalitat i la capacitat de fabricació del producte. / [EN] In recent years, additive manufacturing through material extrusion has experienced accelerated development and adoption thanks to the wide availability of low-cost machines and materials. The size of these machines has been reduced from shop floor to desktop size, enabling their usage in office setups or at home. This change has allowed the adoption of the technology by the broadest range of users than ever, with or without an engineering design background. This new paradigm has created the challenge of how to enable these novel users to leverage the capabilities provided by this technology. This technology allows the creation of complex geometry and customised products with a cost lower than conventional manufacturing processes. Furthermore, the large number of users willing to share their designs allows finding design solutions from other designers. However, the wide range of machine configurations, parameters and materials requires providing support to obtain successful results under any combination. This thesis addresses this challenge by identifying the design and manufacturing characteristics to be considered and investigating the mechanical and post-processing considerations. A new design framework that enables new users to leverage the capabilities and consider the limitations is proposed and evaluated. This research finds that it is possible to create a design toolkit that enables untrained users to design products using the complexity enabled by the technology whilst ensuring the product's functionality and manufacturability. / Fernández Vicente, M. (2022). Analysis to Support Design for Additive Manufacturing with Desktop 3D Printing [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/185344 / TESIS

<p dir="ltr">Generation IV high-temperature gas-cooled reactors (HTGR) are designed to exhibit passive safety under all off-normal circumstances. One such scenario, known as depressurized loss of forced circulation (DLOFC), occurs after a break in the coaxial inlet/outlet header. As the headers are traditionally located at the base of the reactor vessel, the low-density helium coolant is preserved in the core following the initial rupture accident. Upon depressurization, however, air from the surrounding reactor environment slowly enters the coolant channel through molecular diffusion. As the incoming fluid continues to deplete the helium concentration, the onset of natural circulation (ONC) can occur causing bulk air ingress leading to the oxidation and degradation of core components. Therefore, investigating methods to improve the time to ONC is critical in impeding reactor core component damage brought about by DLOFC in an HTGR.</p><p dir="ltr">The Transformational Challenge Reactor (TCR) has similar features to those of an HTGR, but the primary difference is the use of a more complex, additively manufactured (AM) fuel geometry. The more compact, AM, ceramic fuel elements can be conveniently produced with optimally configured channels that suppress the air ingress progress and improve thermofluidic performance. DLOFC and air ingress are experimentally studied in a scaled HTGR flow test setup. Distributed temperature measurements and time to ONC data are collected for the experiments conducted. Multiple geometries are analyzed throughout the investigation. The thermal transient and time to ONC data gathered for the different test geometries and temperatures are compared. The results show that the AM and pebble bed elements deter ONC significantly longer than the baseline geometry representative of a prismatic fuel coolant channel. The AM part delayed ONC as compared to the pebble bed test piece at higher temperatures. The distributed temperature sensor shows intra-leg circulation at higher temperature tests.</p><p dir="ltr">Thermophysical properties of the 316 stainless steel AM component are compared to those of a standard 316 stainless steel round bar. The properties ascertained include the density, emissivity, specific heat, and thermal conductivity. The density of the AM part is 1.5% greater than the density of the standard bar. The emissivity of the AM part is determined to be over three times greater than the emissivity of the polished standard stainless steel round. The specific heat of the AM element is 16% greater than that of the standard 316 stainless steel specific heat. The thermal conductivity of the AM component is measured to be within 1.5% of the standard 316 stainless steel round bar thermal conductivity.</p>

This paper focuses on the local additive manufacturing AM for life science ecosystem. It aims to study the roles of the different stakeholders, and the strengths and weaknesses of the local AM ecosystem through the lens of the research on Innovation ecosystems (IE), Innovation Systems (IS), and Innovation clusters (IC). The main framework used in this study is technological innovation systems (TIS). The methodology of this research relies on a mixed-methods approach that involved surveys administered through structured interviews and self-completion questionnaire. The stakeholders involved in the study include organizations from the industrial sector, healthcare providers, academia, public agencies, and innovation support. Data collected from twenty-two participants was compiled and used to determine response frequencies on nine multiple response questions, and mean scores for thirty-two Likert scale questions. The frequency response tables were used to determine the stakeholder roles, while mean scores were used to determine the TIS functional components ratings and overall standings.  The roles of the stakeholders were determined through the lens of the existing literature on IE. The stakeholders have mixed involvement across the ecosystem, sometimes occupying multiple role categories within the ecosystem. It was found that healthcare stakeholders, and industry stakeholders fill direct value creation and value support roles due to their active participation in defining medical needs and supporting the ecosystem. Next, Public agency stakeholders fill leadership roles, due to their regulatory and actor integration roles. Finally Academic stakeholders fill leadership roles by providing research and knowledge to the ecosystem.   In terms of strengths and weaknesses, the TIS framework was used to evaluate the seven original functional components, and an additional component that was added based on the IE research. It was found that Function 3 Knowledge Diffusion was the strongest function, due to the noncompetitive environment that the local AM ecosystem has established. Meanwhile, Function 2 Knowledge Development scored lowest and was determined to be the weakest functional component due to a lack in the number of patents within the innovation ecosystem. In addition, individual strengths and weaknesses within the functional components were highlighted for a more nuanced look into the strengths and weaknesses of individual functional components. The highest rated strength of the ecosystem was determined to be collaboration, and its weakest area was the noncompetitive environment.

Published Aticle / The Centre for Rapid Prototyping and Manufacturing (CRPM) made a humble start in 1997 as a spin-off from a proposed research activity in 1995, at a stage when Technikons were still being seen as occupational training institutions rather than higher education institutions and and as such, were not funded for research. Addressing an area of high importance to the South African industry, the research activity soon grew into a research unit, commercial centre / centre of excellence, technology transfer unit and innovation support centre. Above all, the research started to impact on product development practices to deliver improved products. The paper considers the development of the available technology platforms at the CUT'S CRPM to become a technology power-house on the African continent, and how it impacted on Institutional Research Development in South Africa.