<p>This study is focused on the prediction of residual stresses and microstructure development of steel and aluminum alloys during laser-based manufacturing processes by means of multi-physics numerical modeling.</p> <p>A finite element model is developed to predict solid-state phase transformation, material hardness, and residual stresses produced during laser-based manufacturing processes such as laser hardening and laser additive manufacturing processes based on the predicted temperature and geometry from a free-surface tracking laser deposition model. The solid-state phase transformational model considers heating, cooling, and multiple laser track heating and cooling as well as multiple layer tempering effects. The residual stress model is applied to the laser hardening of 4140 steel and to laser direct deposition of H13 tool steel and includes the effects of thermal strain and solid-state phase transformational strain based on the resultant phase distributions. Predicted results, including material hardness and residual stresses, are validated with measured values.</p> <p>Two dendrite growth predictive models are also developed to simulate microsegregation and dendrite growth during laser-based manufacturing processes that involve melting and solidification of multicomponent alloys such as laser welding and laser-based additive manufacturing processes. The first model uses the Phase Field method to predict dendrite growth and microsegregation in 2D and 3D. It is validated against simple 2D and 3D cases of single dendrite growth as well as 2D and 3D cases of multiple dendrite growth. It is then applied to laser welding of aluminum alloy Al 6061 and used to predict microstructure within a small domain. </p> The second model uses a novel technique by combining the Cellular Automata method and the Phase Field method to accurately predict solidification on a larger scale with the intent of modeling dendrite growth. The greater computational efficiency of the this model allows for the simulation of entire weld pools in 2D. The model is validated against an analytical model and results in the literature.

Indiana University-Purdue University Indianapolis (IUPUI) / In immersive eLearning environments, it has been demonstrated that incorporating haptic feedback improves the software's pedagogical effectiveness. Due to this and recent advancements in virtual reality (VR) and mixed reality (MR) environments, more immersive, authentic, and viable pedagogical tools have been created. However, the advanced manufacturing industry has not fully embraced mixed reality training tools. There is currently a need for effective haptic feedback techniques in advanced manufacturing environments. The MR-AVML, a proposed CNC milling machine training tool, is designed to include two forms of haptic feedback, thereby providing users with a natural and intuitive experience. This experience is achieved by tasking users with running a virtual machine seen through the Microsoft HoloLens and interacting with a physical representation of the machine controller. After conducting a pedagogical study on the environment, it was found that the MR-AVML was 6.06% more effective than a version of the environment with no haptic feedback, and only 1.35% less effective than hands-on training led by an instructor. This shows that the inclusion of haptic feedback in an advanced manufacturing training environment can improve pedagogical effectiveness.

<p>  </p> <p>High-resolution enclosed channels/fibers are highly demanded by different disciplines such as microfluidic channels for chemical synthesis, bioreactors for drug metabolism, magnetic locomotor for drug delivery, and wearable devices for motion detection. However, the current fabrication techniques for enclosed channels/fibers are restricted to a few millimeters in size. Their manufacturing often involves time and energy-consuming multi-step processes with insufficient resolution. In this work, we demonstrate a novel co-flow-enabled fabrication method to resolve the technological restrictions in the fabrication of high-resolution enclosed channels/fibers with efficient production time, controllable morphologies, and high throughput manner.</p> <p>An epoxy-based enclosed microfluidic channel was first built. A non-reactive paraffin oil and a liquid resin were pumped into a 3D-printed co-flow generator and worked as core and shell fluids, respectively. The epoxy resin was cured by external heat stimulus. As a result, the reaction region was limited between the generator wall surface and the boundary of core flow, eliminating the need for precise control over the curing system. The experiment was successfully conducted to cure build resin channel inside copper and resin tubes with good shell thickness.</p> <p>Conductive hollow hydrogel microfibers were also fabricated by this method. Sodium Alginate and Calcium Chloride were chosen as the shell and core flows, respectively. The ionic crosslinking happens at the boundary of two flows and expands outwards across the radial direction. Thus, the diameter of the hollow channel can be easily adjusted by tuning the flow rate and the size of the core flow injection needle. PEDOT: PSS, a conductive polymer, was mixed with Sodium Alginate to impart fibers with excellent electrical conductivity. The synthesized hollow microfibers have shown their functionality in stretching movement detection by serving as a fundamental building element of motion sensors. </p>

Metal additive manufacturing can be used for producing complex and functional components in the aerospace industry. This thesis deals with the process-structure-property relationships in selective laser melting of three aerospace alloys: Invar 36, stainless steel 316L, and Ti-6Al-4V. These alloys are weldable but hard to machine, which make them good candidates for the selective laser melting process. Invar 36 has a very low coefficient of thermal expansion because of its nickel concentration of 36% and stainless steel 316L contains 16-18% chromium that gives the alloy a corrosion resistance property. Ti-6Al-4V offers high strength-to-weight ratio, high biocompatibility, and outstanding corrosion resistance. Any changes in the chemical composition of these materials could affect their performance during application. In this thesis, a full factorial design of experiments is formulated to study a wide range of laser process parameters. The bulk density, tensile mechanical properties, fractography, microstructure, material composition, material phases, coefficient of thermal expansion, magnetic dipole moments, and residual stresses of the parts produced are experimentally investigated. An optimum process window has been suggested for each material based on experimental work. The thermal cycle, residual stresses, and part distortions are examined using a thermo-mechanical finite element model. The model predicts the residual stress and part distortion after build plate removal. The thesis introduces two laser energy densities for each material: brittle-ductile transition energy density, ET, and critical laser energy density, EC. Below the brittle-ductile transition energy density, the parts exhibited void formation, low density, and brittle fracture. Above the critical energy density, the parts showed vaporization of some alloying elements that have low boiling temperatures. Additionally, real-time measurements were taken using a pyrometer and a high-speed camera during the selective laser melting process. The trends found in the numerical results agree with those found experimentally. / Thesis / Doctor of Philosophy (PhD)

The prevalence of big data has rapidly changed the usage and mechanisms of data analytics within organizations. Big data is a widely-used term without a clear definition. The difference between big data and traditional data can be characterized by four Vs: velocity (speed at which data is generated), volume (amount of data generated), variety (the data can take on different forms), and veracity (the data may be of poor/unknown quality). As many industries begin to recognize the value of big data, organizations try to capture it through means such as: side-channel data in a manufacturing operation, unstructured text-data reported by healthcare personnel, various demographic information of households from census surveys, and the range of communication data that define communities and social networks. Big data analytics generally follows this framework: first, a digitized process generates a stream of data, this raw data stream is pre-processed to convert the data into a usable format, the pre-processed data is analyzed using statistical tools. In this stage, called statistical learning of the data, analysts have two main objectives (1) develop a statistical model that captures the behavior of the process from a sample of the data (2) identify anomalies in the process. However, several open challenges still exist in this framework for big data analytics. Recently, data types such as free-text data are also being captured. Although many established processing techniques exist for other data types, free-text data comes from a wide range of individuals and is subject to syntax, grammar, language, and colloquialisms that require substantially different processing approaches. Once the data is processed, open challenges still exist in the statistical learning step of understanding the data. Statistical learning aims to satisfy two objectives, (1) develop a model that highlights general patterns in the data (2) create a signaling mechanism to identify if outliers are present in the data. Statistical modeling is widely utilized as researchers have created a variety of statistical models to explain everyday phenomena such as predicting energy usage behavior, traffic patterns, and stock market behaviors, among others. However, new applications of big data with increasingly varied designs present interesting challenges. Consider the example of free-text analysis posed above. There's a renewed interest in modeling free-text narratives from sources such as online reviews, customer complaints, or patient safety event reports, into intuitive themes or topics. As previously mentioned, documents describing the same phenomena can vary widely in their word usage and structure. Another recent interest area of statistical learning is using the environmental conditions that people live, work, and grow in, to infer their quality of life. It is well established that social factors play a role in overall health outcomes, however, clinical applications of these social determinants of health is a recent and an open problem. These examples are just a few of many examples wherein new applications of big data pose complex challenges requiring thoughtful and inventive approaches to processing, analyzing, and modeling data. Although a large body of research exists in the area of anomaly detection increasingly complicated data sources (such as side-channel related data or network-based data) present equally convoluted challenges. For effective anomaly-detection, analysts define parameters and rules, so that when large collections of raw data are aggregated, pieces of data that do not conform are easily noticed and flagged. In this work, I investigate the different steps of the data analytics framework and propose improvements for each step, paired with practical applications, to demonstrate the efficacy of my methods. This paper focuses on the healthcare, manufacturing and social-networking industries, but the materials are broad enough to have wide applications across data analytics generally. My main contributions can be summarized as follows: • In the big data analytics framework, raw data initially goes through a pre-processing step. Although many pre-processing techniques exist, there are several challenges in pre-processing text data and I develop a pre-processing tool for text data. • In the next step of the data analytics framework, there are challenges in both statistical modeling and anomaly detection o I address the research area of statistical modeling in two ways: - There are open challenges in defining models to characterize text data. I introduce a community extraction model that autonomously aggregates text documents into intuitive communities/groups - In health care, it is well established that social factors play a role in overall health outcomes however developing a statistical model that characterizes these relationships is an open research area. I developed statistical models for generalizing relationships between social determinants of health of a cohort and general medical risk factors o I address the research area of anomaly detection in two ways: - A variety of anomaly detection techniques exist already, however, some of these methods lack a rigorous statistical investigation thereby making them ineffective to a practitioner. I identify critical shortcomings to a proposed network based anomaly detection technique and introduce methodological improvements - Manufacturing enterprises which are now more connected than ever are vulnerably to anomalies in the form of cyber-physical attacks. I developed a sensor-based side-channel technique for anomaly detection in a manufacturing process / PHD / The prevalence of big data has rapidly changed the usage and mechanisms of data analytics within organizations. The fields of manufacturing and healthcare are two examples of industries that are currently undergoing significant transformations due to the rise of big data. The addition of large sensory systems is changing how parts are being manufactured and inspected and the prevalence of Health Information Technology (HIT) systems in healthcare systems is also changing the way healthcare services are delivered. These industries are turning to big data analytics in the hopes of acquiring many of the benefits other sectors are experiencing, including reducing cost, improving safety, and boosting productivity. However, there are many challenges that exist along with the framework of big data analytics, from pre-processing raw data, to statistical modeling of the data, and identifying anomalies present in the data or process. This work offers significant contributions in each of the aforementioned areas and includes practical real-world applications. Big data analytics generally follows this framework: first, a digitized process generates a stream of data, this raw data stream is pre-processed to convert the data into a usable format, the pre-processed data is analyzed using statistical tools. In this stage, called ‘statistical learning of the data’, analysts have two main objectives (1) develop a statistical model that captures the behavior of the process from a sample of the data (2) identify anomalies or outliers in the process. In this work, I investigate the different steps of the data analytics framework and propose improvements for each step, paired with practical applications, to demonstrate the efficacy of my methods. This work focuses on the healthcare and manufacturing industries, but the materials are broad enough to have wide applications across data analytics generally. My main contributions can be summarized as follows: • In the big data analytics framework, raw data initially goes through a pre-processing step. Although many pre-processing techniques exist, there are several challenges in pre-processing text data and I develop a pre-processing tool for text data. • In the next step of the data analytics framework, there are challenges in both statistical modeling and anomaly detection o I address the research area of statistical modeling in two ways: - There are open challenges in defining models to characterize text data. I introduce a community extraction model that autonomously aggregates text documents into intuitive communities/groups - In health care, it is well established that social factors play a role in overall health outcomes however developing a statistical model that characterizes these relationships is an open research area. I developed statistical models for generalizing relationships between social determinants of health of a cohort and general medical risk factors o I address the research area of anomaly detection in two ways: - A variety of anomaly detection techniques exist already, however, some of these methods lack a rigorous statistical investigation thereby making them ineffective to a practitioner. I identify critical shortcomings to a proposed network-based anomaly detection technique and introduce methodological improvements - Manufacturing enterprises which are now more connected than ever are vulnerable to anomalies in the form of cyber-physical attacks. I developed a sensor-based side-channel technique for anomaly detection in a manufacturing process.

Esta tese busca, de início, pesquisar comparativamente, com a abrangência possível, os métodos e processos pelos quais as empresas componentes do setor siderúrgico nacional avaliam - sob o ponto de vista da viabililidade - a tomada de suas decisões relativamente a seus peculiares e vultosos investimentos em equipamentos de tecnologia avançada de manufatura. Revisita primária e metodologicamente amplo repertório de métodos de avaliação consagrados pela prática e pela literatura, tendo como cenário o setor em referência representado pelas várias modalidades industriais que o suportam. A pesquisa é realizada com base no estudo de múltiplos casos realizados em grandes empresas siderúrgicas, a partir de visitas a suas instalações e entrevistas com seus especialistas de análise de viabilidade de projetos de investimento. Ao final, oferece modelo de avaliação a ser adotado complementarmente aos critérios utilizados pelo setor em referência, a partir das observações e discussões efetuadas em campo. / This thesis initially seeks to search, in a comparative basis and with the permissible largeness, the methods and procedures through which the companies comprising the national siderurgic sector appraise their decisions referring to their peculiar and huge investments in AMT equipments, under a feasibility point of view. A primary review is made, in a comparative way, of a large well known evaluation methods repertoire, in a background at which act the various concerned industrial modalities. The search is accomplished based on the study of multiple cases realized at big siderurgic companies, by visiting their installations and by interviewing their specialists in investment feasibility project analysis. Eventually offers an evaluation model to be adopted by this sector, based upon the observation and discussions made in the related field research.

Esta tese busca, de início, pesquisar comparativamente, com a abrangência possível, os métodos e processos pelos quais as empresas componentes do setor siderúrgico nacional avaliam - sob o ponto de vista da viabililidade - a tomada de suas decisões relativamente a seus peculiares e vultosos investimentos em equipamentos de tecnologia avançada de manufatura. Revisita primária e metodologicamente amplo repertório de métodos de avaliação consagrados pela prática e pela literatura, tendo como cenário o setor em referência representado pelas várias modalidades industriais que o suportam. A pesquisa é realizada com base no estudo de múltiplos casos realizados em grandes empresas siderúrgicas, a partir de visitas a suas instalações e entrevistas com seus especialistas de análise de viabilidade de projetos de investimento. Ao final, oferece modelo de avaliação a ser adotado complementarmente aos critérios utilizados pelo setor em referência, a partir das observações e discussões efetuadas em campo. / This thesis initially seeks to search, in a comparative basis and with the permissible largeness, the methods and procedures through which the companies comprising the national siderurgic sector appraise their decisions referring to their peculiar and huge investments in AMT equipments, under a feasibility point of view. A primary review is made, in a comparative way, of a large well known evaluation methods repertoire, in a background at which act the various concerned industrial modalities. The search is accomplished based on the study of multiple cases realized at big siderurgic companies, by visiting their installations and by interviewing their specialists in investment feasibility project analysis. Eventually offers an evaluation model to be adopted by this sector, based upon the observation and discussions made in the related field research.

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L’accroissement du champ d’application et de la performance des microcontrôleurs s’accompagne d’une augmentation de la puissance consommée limitant l’autonomie des systèmes nomades (smartphones, tablettes, ordinateurs portables, implants biomédicaux, …). L’étude menée dans le cadre de la thèse, consiste à réduire la consommation dynamique des circuits fabriqués en technologie CMOS 80 nm avec mémoire non-volatile embarquée (e-NVM) ; à travers l’amélioration des performances des transistors MOS. Pour augmenter la mobilité des porteurs de charge, des techniques de fabrication utilisées dans les nœuds les plus avancés (40 nm, 32 nm) sont d’abord étudiées en fonction de différents critères (intégration, coût, gain en courant/performance). Celles sélectionnées sont ensuite optimisées et adaptées pour être embarquées sur une plate-forme e-NVM 80 nm. L’étape suivante est d’étudier comment transformer le gain en courant, en gain sur la consommation dynamique, sans dégrader la consommation statique. Les approches utilisées ont été de réduire la tension d’alimentation et la largeur des transistors. Un gain en consommation dynamique supérieur à 20 % est démontré sur des oscillateurs en anneau et sur un circuit numérique conçu avec près de 20 000 cellules logiques. La méthodologie appliquée sur le circuit a permis de réduire automatiquement la taille des transistors (évitant ainsi une étape de conception supplémentaire). Enfin, une dernière étude consiste à optimiser la consommation, les performances et la surface des cellules logiques à travers des améliorations de conception et une solution permettant de réduire l’impact de la contrainte induite par l’oxyde STI. / The increase of the scope of application and the performance of microcontrollers is accompanied by an increase in power consumption reducing the life-time of mobile systems (smartphones, tablets, laptops, biomedical implants, …). Here, the work consists of reducing the dynamic consumption of circuits manufactured in embedded non-volatile memories (e-NVM) CMOS 80 nm technology by improving the performance of MOS transistors. In order to increase the carriers’ mobility, manufacturing techniques used in the most advanced technological nodes (40 nm, 32 nm) are firstly studied according to different criteria (process integration, cost, current/performance gain). Then, selected techniques are optimized and adapted to be used on an e-NVM technological platform. The next step is to study how to transform the current gain into dynamic power gain without impacting the static consumption. To do so, the supply voltage and the transistor widths are reduced. Up to 20 % in dynamic current gain is demonstrated using ring oscillators and a digital circuit designed with 20,000 standard cells. The methodology applied on the circuit allows automatic reduction to all transistor widths without additional design modifications. Finally, a last study is performed in order to optimize the consumption, the performance and the area of digital standard cells through design improvements and by reducing the mechanical stress of STI oxide.