Ciencias Exactas y Ciencias de la Salud
Permanent URI for this collectionhttps://hdl.handle.net/11285/551014
Pertenecen a esta colección Tesis y Trabajos de grado de los Doctorados correspondientes a las Escuelas de Ingeniería y Ciencias así como a Medicina y Ciencias de la Salud.
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- Development of a competitive technology intelligence methodology to identify technology dynamics: the case of M-health for diabetes(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-11-13) Castillo Valdez, Pedro Fernando; Rodríguez Salvador, Marisela; emipsanchez; Martínez Ledesma, Emmanuel; Díaz de la Garza, Rocío Isabel; Hernánez Brenes, Carmen; Tejeda Alejandre, Raquel; School of Engineering and Sciences; Rectoría Tec de MonterreyThe unprecedented development of technological advances brings new challenges and opportunities to create competitive advantages. It is necessary the effective use of technology as a facilitator to bring better products and services in all sectors such as industry, business, education, healthcare, and government. An adequate assessment of science and technology is fundamental to impact present and future Research and Development (R&D) and innovation decisions. Diverse disciplines based on metrics analysis have emerged to facilitate science and technology understanding, such as scientometrics, patentometrics, and altmetrics. They offer fundamental theoretical and methodological contributions to quantify scientific research literature, patents, scholarly activities on social networks and websites, aiming to reveal the process of scientific and technology development. However, the current accelerated technological advances require researchers to implement a superior approach to detect continuous changes in the external environment identifying opportunities and vulnerabilities to strengthen the decision-making process regarding R&D and innovation. Organizations can increase their advantages by systematically analyzing the external environment, identify movements of competitors and detect opportunities for growth. In this context, Competitive Technology Intelligence (CTI) offers a strategic approach where information is transformed into opportunities for an actionable result. This research proposes a CTI methodology of eight steps that incorporates experts feedback, a scientometrics and a word distribution analysis into a process to provide a broader scope to science and technology. This thesis provides a more robust analytical approach than traditional scientometric analysis where indicators as relevant authors, institutions, countries, citations, and impactful articles are identified. In this context, this thesis goes further since current hotspots and landscape of main research topics are also determined as well as technological trends, gaps, and opportunity areas to research, evolving the traditional scientometric approach. To demonstrate the methodology proposed, a case study was carried out around diabetes m-Health which is particularly relevant given the worldwide increase in diabetes prevalence. Identifying its technological dynamics can facilitate the adoption of effective technologies that enhance patients' quality of life. As a result of all this process, three scientific publications were developed and published in Q1, and Q2 journals. In the first publication (2021) the proposed CTI methodology is VII presented, while in the second publication (2024) the methodology is applied through a scientometric analysis where current hotspots on diabetes m-Health are determined. Finally, the third publication (2024) provides a landscape of main research topics in diabetes m-Health, and technological trends and opportunity areas to research are identified. These studies aim to contribute researchers, decision makers, and policy makers to prioritize R&D efforts, consolidate areas of interest and explore new research topics.
- Design and operational considerations for optimizing DC-iEK devices for particle manipulation in low voltage implementations(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-11-15) Santos Ramírez, Jesús Martín de los; Pérez González, Víctor Hugo; emimmayorquin; Benavides Lozano, Jorge Alejandro; Lapizco Encinas, Blanca H.; Xuan, Xiangchun; Vázquez Lepe, Elisa Virginia; Escuela de Ingeniería y Ciencias; Campus MonterreyDirect current insulator-based electrokinetic (DC-iEK) devices have been around for two decades and in that time, they have positioned among the most popular microfluidic particle manipulation techniques used in conceptual applications. They have been demonstrated to manipulate (e.g., trap, concentrate, and separate), from synthetic particles like polystyrene-based microspheres to biological samples such as mammal cells, bacteria, exosomes, and proteins. In addition, DC-iEK are considerably cheap and easy to fabricate, adding a plus to their implementation. However, the major challenge that DC-iEK system have presented has been their (almost unpractical) operating voltage conditions, that are not uncommon to be in the orders or thousands or volts, needed to produce the necessary electric field inside the channel required for proper particle manipulation. This work focused on identifying the main design parameters that influence DC-iEK systems capabilities to produce the necessary electric field while reducing their voltage requirements. The results from this project have elucidated how each of the geometric design parameters contribute to DC-iEK systems performance. This allowed for the design and testing of low voltage particle trapping, separation, and characterization in voltages between 80 and 18 V which seems to be close to the limits that DC-iEK are capable of achieving.
- Analysis of optimization models under different approaches to deal with uncertainty regarding pre-disaster planning in food bank supply chains(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-12-11) Rivera Morales, Adrian Fernando; Smith Cornejo, Neale Ricardo; emimmayorquin; Cárdenas Barrón, Leopoldo Eduardo; Bourguet Díaz, Rafael Ernesto; Güemes Castorena, David; Vázquez Lepe,Elisa Virginia; Ciencias de la ingeniería Escuela de Ingeniería y Ciencias; Campus Monterrey; Ruiz, AngelA critical decision in humanitarian logistics pre-disaster planning is the sufficient pre-establishment of relief supplies to provide efficient and quick operations in the aftermath of the event. This thesis identifies some of the challenges faced by food banks from an operations management perspective. To support managers making such decisions, we propose four mathematical formulations that seek to optimize food prepositioning (before the event) and further distribution (after the event) in order to minimize unmet demand (MUD). These formulations will first be analyzed under the assumption of uncertainty in demand, finally comparing results considering uncertainty in supply. The two first formulations adopt the cardinality-constrained (CC) approach to handle uncertainty. These formulations differ in their objective functions, the first formulation’s objective seeks to MUD, whilst the second incorporates equity in the way that demand is satisfied. The two remaining formulations are scenario-based (SB), and as in the previous two formulations, seek to MUD with and without equity considerations, respectively. The formulations are applied to a case study where a food bank faces the arrival of a hurricane in Mexico. For the formulations with uncertainty in demand, we compare the differences between the solutions produced by the proposed formulations and the solutions that would have been produced without uncertainty (perfect information) to have a better understanding of their performance and their behavior. A discussion of the strengths and weaknesses of each model is provided to help managers choose the model that best suits their needs. For the formulations with uncertainty in supply, a series of experiments was done to compare results and further conclusions. Our results demonstrate that the CC approach has an acceptable behavior for every situation, while the SB approach can have exceptional outcomes when the predicted scenarios are good, but worst solutions otherwise, making this approach dependent of the capability of predicting good scenarios.
- Síndromes geriátricos en pacientes con artropatía inflamatoria(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-09-17) Lozano Lozano, Rodrigo; Vega Morales, David; González Guerra, José Luis; Sánchez Ávila, María Teresa; Esquivel Valerio, Jorge Antonio; Garza Elizondo, Mario Alberto; Escuela de Medicina y Ciencias de la Salud; Campus MonterreyLas enfermedades reumatológicas son alteraciones observadas con mucha frecuencia en nuestra población en general, sin embargo, son cada vez más comunes en los adultos mayores. Son a menudo infradiagnosticadas e infratratadas y como nos podemos imaginar, la prevalencia de dichas enfermedades aumenta de forma importante con la edad.El proceso del envejecimiento se asocia a múltiples cambios tanto físicos como mentales y sociales que afectan directamente sobre la capacidad funcional del adulto mayor, ocasionando una pérdida de la autonomía y la aparición de la dependencia. Es posible que las características fisiológicas del envejecimiento estén siendo amplificadas por las propias enfermedades reumatológicas o por los medicamentos utilizados en ellas; ocasionando con ello la aparición de grandes síndromes geriátricos e impactando principalmente tanto en la salud como en la vida diaria del adulto mayor. Debido a esto, es importante tomar medidas para aumentar la conciencia, la prevención, la detección y el tratamiento de todos los síndromes geriátricos.En esta línea de investigación, se desarrollaron diversos proyectos con la finalidad de estudiar los grandes síndromes geriátricos presentados en los pacientes con enfermedades reumatológicas. Primero se realizó una encuesta en formato digital a los geriatras certificados por el Consejo Mexicano de Geriatría para buscar las escalas más adecuadas dentro de la valoración geriátrica integral para realizar a nuestros pacientes. Al obtener un consenso sobre las escalas más utilizadas en nuestro país, se realizó una Clínica de Reumageriatría donde se evaluaron a pacientes tanto por Reumatología como por Geriatría. Derivado de esto, seobtuvieron múltiples protocolos de investigación y 4 proyectos que mencionaremos más adelante.En el primero se estudió la prevalencia de la polifarmacia y la interacción medicamentosa en los adultos mayores de 65 años con enfermedad reumática, en el segundo se buscó la prevalencia de fragilidad en la misma población, en el tercero se evaluó la prevalencia del deterioro cognitivo en los ancianos con artritis reumatoide y en el cuarto se analizó la relación de los puntajes DAS-28 y HAD-DI con el riesgo de caídas en pacientes con artritis reumatoide.Los resultados de estos proyectos son el primer escalón para poder abordar este problema tan complejo que tienen los reumatólogos y que cada vez se hace más frecuente. Y con esto recordar la importancia de la geriatrización no solo en la reumatología si no en todas las especialidades, ya que eso nos podrá permitir llevar a nuestros pacientes a un envejecimiento saludable.
- Methodology to improve compact extended range ev-powertrain module(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2023-06-07) Puma Benavides, David Sebastian; Calderón Nájera, Juan de Dios; puemcuervo, emipsanchez; Galluzzi Aguilera, Renato; Bustamante Bello, Martin Rogelio; Loyola Morales, Félix; School of Engineering and Sciences; Campus Monterrey; Izquierdo Reyes, JavierThe constant growth of the vehicle fleet means that more and more emissions are being emitted into the environment, with the transportation sector contributing around 21% of CO2 in data updated to 2023. Reducing emissions and carbon footprint, leaving aside the dependence on fossil fuels, has been the premise for developing vehicles with new technologies and developing clean energy for their use. As a result, the sale of internal combustion vehicles reached its highest peak in 2017, and from there, the sale of electric and hybrid vehicles has grown yearly. However, combustion, electric and hybrid vehicles have yet to achieve optimal efficiency; therefore, generating optimizations in their powertrain is viable as research topics, as well as for the extension of the range in electric vehicles, which at the moment is a factor that makes their purchase unattractive. Therefore, this thesis aims to review and evaluate technologies that can function as range extenders for electric vehicles, considering their efficiency, low pollution levels, and compatibility for integration into electric vehicle platforms. To facilitate this evaluation, an algorithm incorporating equations representing characteristic curves of mechanical or electrical components will be developed for Extended Range Electric Vehicle EREV. This algorithm will provide valuable insights into the behavior and energy analysis of potential range extender (ICE-Alternator/Generator). Furthermore, the optimization of the entire powertrain system will be considered to ensure all components operate at peak efficiency. These objectives constitute the core of this dissertation. Through powertrain improvement, specifically by adjusting the differential gear ratio from 4.3 to 3.54, significant improvements in vehicle performance can be achieved. Energy savings during standardized driving cycles such as NEDC, WLTC-2, and WLTC-3 can reach up to 10%. Additionally, integrating an auxiliary power unit (APU) into the vehicle architecture can substantially enhance the vehicle's range. By employing an ICE-Alternator configuration with a maximum power of 12.8 kW, the vehicle's travel distance can be extended by up to 170%. Alternatively, an ICE-Generator configuration with a maximum power of 3.2 kW can increase travel distance by up to 39%. Implementing an effective control strategy that optimizes fuel consumption based on the battery's state of charge further enhances APU utilization, resulting in efficiency gains of up to 3.5%. The proposed methodology for developing extended-range electric vehicles, along with the validated algorithm through practical implementations and testing, enables comprehensive energy analyses. This approach provides a more accurate understanding of the performance of vehicle platforms incorporating range extenders.
- DC-DC high-order converters for renewable applications(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-05-07) Garza Arias, Enrique; Valdez Resendiz, Jesús Elias; Rosas Caro, Julio César; Escobar Valderrama, Gerardo; Castañeda Cuevas, Herman; Escuela de Ingeniería y Ciencias; Campus Monterrey; Guillén Aparicio, DanielThis dissertation develops new theory and techniques for DC-DC power converters interfacing renewable energies, specifically for converters classified as high-order based on their mathematical model, and thus generally more complex and difficult to analyze. The thesis encompasses the development of a new circuit topology, a control design focused on high-order dynamic systems, and the study of conversion systems between renewables and the utility grid. The dynamics of high-order converters present challenges for the stability and synthesis of controllers. By studying the nonlinear model of fourth-order non-minimum phase converters through zero dynamics, it has been established that some of these can attain stability under direct voltage at high regulation, in contrast to second-order converters. Controllers have been designed to take advantage of these results in order to remove the current sensing stage while still achieving high bandwidth voltage regulation. Research has also been conducted into creating novel DC converters with a lower component size than similar circuits. From this, an improved super-boost converter has been developed that requires lower energy storage on its capacitors and inductors than the traditional boost and fourth-order boost converters, for a given specification of input current and output voltage ripples. Additionally, an inverter system for a fuel cell stack has been developed using double-dual converters in cascade to increase the voltage gain and power handling of the system. Results from tests at different power and voltage levels show that the proposed system also achieves reduced switch voltage stresses and reduced output current ripples. Simulation and experimental results were obtained in order to validate the theoretical analysis. Mathematical models, steady state values and stability conditions were calculated, and prototype designs were developed to corroborate the performance of the converters, control and efficiency.
- Commercial delivery policies: inventory management models with power demand pattern(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-06-29) Khan, Md. Al-Amin; Cárdenas Barrón, Leopoldo Eduardo; emiggomez, emipsanchez; Loera Hernández, Imelda de Jesús; Smith, Neale R.; Treviño Garza, Gerardo; Bourguet Díaz, Rafael Ernesto; School of Engineering and Sciences; Campus MonterreyIn the fast-paced, ever-changing environment of contemporary business, inventory management stands as a vital, ongoing endeavor. The discipline of decision-making in inventory management assumes a central role in this dynamic environment that is marked by continuous change and intense competition. Navigating the complexities of decision-making poses challenges, particularly in accurately assessing the multifaceted aspects of decision-making processes amid varying circumstances, including demand fluctuations, different types of discounts, and contractual agreements. At the same time, the increasing concern among consumers regarding the environmental footprint of their purchases, coupled with government-mandated regulations, complicates the decision-making process for businesses. In this milieu, sustainable inventory management practices have emerged as a pertinent research area, prompting heightened scrutiny of the impacts of emission guidelines on inventory practices, not only aimed at addressing broader societal concerns but also at ensuring the financial sustainability of businesses. This thesis adopts a specialized demand structure known as the power demand pattern (PDP) to depict fluctuations in demand over the storage period of a company, providing a robust framework for understanding customer demand dynamics across various products. The company maintains its inventory by acquiring items through quantity discounts in exchange for a quantity-sensitive prepayment as part of a contractual arrangement. This thesis introduces a novel concept by considering the installment frequency for fulfilling prepayment obligations as a decision variable for the company, incorporating a transaction fee for each installment. Furthermore, theoretical formulas are developed under different sorts of demand structures, incorporating the influences of selling price and storage time, to assess the profitability of inventory management processes under a combined link-to-order prepayment and quantity discount scheme. A significant advancement by integrating sustainability considerations into both inventory management and pricing strategies within the framework of the PDP is accomplished in this thesis. Through systematic identification and comparison of sustainable inventory management practices under varying emission guidelines, this study provides valuable insights aimed at optimizing profits within the PDP. Therefore, the insights derived from this study offer organizations practical techniques to navigate the complex regulatory environment effectively and achieve sustainable financial performance. Moreover, intensive and comprehensive in-depth sustainable inventory practices under the PDP are established specifically for growing items (GIs). This thesis investigates the impact of weight loss resulting from bleeding and non-consumable components on optimal pricing and inventory strategies for a farm, delving into previously unexplored areas within the literature on GIs. A comparative analysis is conducted on the operations of a livestock farm, operating within several environmental regulations. Consequently, the comprehensive methodology improves sustainable inventory management techniques and offers practical strategies to mitigate environmental impacts and enhance economic feasibility in livestock production.
- Extracting the embedded knowledge in class visualizations from artificial neural networks for applications in dataset and model compression and combinatorial optimization(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-04-25) Abreu Pederzini, Jose Ricardo; Terashima Marín, Hugo; emiggomez, emipsanchez; González Mendoza, Miguel; Juárez Jiménez, Julio Antonio; Rosales Pérez, Alejandro; Bendre, Nihar; School of Engineering and Sciences; Campus Monterrey; Ortiz Bayliss, José CarlosArtificial neural networks are efficient learning algorithms, considered universal approxima-tors for solving numerous real-world problems in areas like computer vision, language processing, or reinforcement learning. To approximate any given function, neural networks train a large number of parameters that can go up to the millions or even billions in some cases. The large number of parameters and hidden layers in neural networks makes them hard to interpret, which is why they are often referred to as black boxes. In the quest to make artificial neural networks interpretable in the field of computer vision, feature visualization stands outas one of the most developed and promising research directions. While feature visualizations are a useful tool to gain insights about the underlying function learned by a neural network, they are still considered simply as visual aids that require human interpretation. In this doctoral work, we propose that feature visualizations—class visualizations in particular—are analogous to mental imagery in humans and contain the knowledge that the model extracted from the training data. Therefore, when correctly generated, class visualiza-tions can be considered as a conceptual compression of the data used to train the underlying model, resembling the experience of perceiving the actual training samples just as mental imagery resembles the real experience of perceiving the actual physical event. We present results showing that class visualizations can be considered a conceptual compression of the training data used to train the underlying model and present a methodology that enables the use of class visualizations as training data. To achieve this goal, we show that class visualizations can be used as training data to develop new models from scratch, achieving, in some cases, the same accuracy as the underlying model. Additionally, we explore the nature of class visualizations through different experiments to gain insights on what exactly class visualizations represent and what knowledge is embedded in them. To do so, we com- pare class visualizations to the class average image from the training data and demonstrate how the other classes that a model is trained on affect the shape and the knowledge embedded in a class visualization. We show that class visualizations are equivalent to visualizing the weight matrices of the output neurons in shallow network architectures and demonstrate that class visualizations can be used as pretrained convolutional filters. We experimentally show the potential of class visualizations for extreme model compression purposes. Finally, we present a novel methodology to enable the use of Artificial Neural Networks along with class visualizations for the solution of combinatorial optimization problems, such as the 2D Bin Packing Problem, by training an Artificial Neural Network to score potential solutions to a 2D BPP and then using that network to generate an ’optimal’ (local optima) solution to the problem by extracting a class visualization from the network via backpropagation to the network’s input. Even though we show the use of class visualizations as a tool to solve the bin packing problem, it is important to note that class visualizations have the potential to be used in the same way to solve other types of combinatorial optimization problems. For other types of combinatorial optimization problems, we just need to design a neural network that is capable of scoring solutions to the particular combinatorial optimization problem and extract class visualizations from such a network to generate a candidate solution to the problem.
- A digital twin model with knowledge graph-driven dense captioning(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-06) Wajid, Mohammad Saif; Ortiz Bayliss, José Carlos; 212577; Therasima Marin, Hugo; emiggomez, emipsanchez; Ortiz Bayliss, José Carlos; Carrasco Jiménez, José Carlos; Ceballos Cancino, Héctor Gibrán; School of Engineering and Sciences; Campus Monterrey; Najafirad, PeymanThis dissertation is submitted to the Graduate Programs in the School of Engineering and Sciences in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Computer Science. This document explores how a digital twin model for the TEC District is developed and how the knowledge graph can be used for dense captioning of security events happening in the TEC District digital twin model. This also describes and analyses factors responsible for security breaches in the city using the concept of neutrosophy. The thesis proposes novel techniques for advancing dense captioning with the integration of knowledge graphs and neutrosophy, harnessing the capabilities of digital twin technology. Digital twins, as virtual replicas of physical entities or systems, offer a comprehensive framework for understanding and simulating real-world scenarios. They have emerged as a powerful tool in various industries, including manufacturing, healthcare, and urban planning. These models rely on detailed simulations of cities, including video data, to analyze and describe various security events using dense captioning. However, the accuracy and relevance of these simulations depend heavily on the quality of the captions generated for the video content. Captioning of videos based on temporal information presents a challenging task, involving the limitation of distracting information over time and space, which is crucial but poses difficulties. Additionally, ensuring robustness to false positives during captioning and addressing storage issues are significant challenges obtained from the literature. Also gathering information from knowledge graphs and providing context is another key task because of the presence of indeterminacy in data. This poses a challenge for defining aggression subjectively and automatically describing events while optimizing classifiers for faster caption generation and selecting optimal parameters. A widely used technique for dense video captioning is a knowledge graph that provides a structured representation of knowledge, organizing and connecting information extracted from videos. By incorporating knowledge graphs into the digital twin model, the relevance and context of the captions are significantly enhanced. However, knowledge graphs may fail to capture indeterminate factors that can dramatically impact situation analysis. Indeterminate factors, such as unpredictable human behavior or environmental conditions, are crucial in determining event sequences in digital twin models. In this dissertation, we aim to create a digital twin model for the TEC District for effective dense captioning of events with the knowledge graph model district area. In the proposed model, knowledge graphs play a crucial role in enhancing the context and relevance of captions by organizing and connecting information extracted from videos. It provides a structured representation of knowledge, enabling a more comprehensive understanding of video content. We have also utilized neutrosophy to address indeterminate and uncertain events, thereby enhancing the efficiency of dense captioning. This work is carried out in three phases; the first phase identifies various traits of character taken from datasets and literature, leading to different events among the masses using Neutrosophic Cognitive Maps (NCMs). This is done to identify the significance of various determinate and indeterminate factors while analyzing the security events. This task was earlier performed using Fuzzy Cognitive Maps (FCMs) in some research domains other than dense video captioning where indeterminate or uncertain factors were not considered. Therefore, we provide a brief comparison between NCMs and FCMs and show how effective NCMs are when considering the uncertainty of concepts while carrying out tests for describing events. In the second phase, a knowledge graph model for dense captioning is developed. As captioning is based on a knowledge graph, the time consumption for generating the video captions was considerably reduced. Also, we used the Bidirectional Long Short-Term Memory (BiLSTM) classifier to analyze the flow of the information provided by the captions, and the efficiency is further enhanced by using the Recurrent Neural Network (RNN). The enabling of the Squacc optimization algorithm in both RNN and BiLSTM effectively optimized the classifier’s parameters and helped to obtain an efficient output. The performance metrics BLEU, ROUGE, CIDEr, METEOR, and SPICE demonstrated the superiority of the research. Later in the third phase, we developed a digital twin for the TEC District, Monterrey, Nuevo Leon, Mexico. We carried out this work by defining and developing five layers in our digital twin model: the ground layer, BIM layer, Mobility infrastructure, district 3D model, and finally, the digital twin. Here, we used some common software applications for the development of TEC District Digital Twin, such as Esri ArcGIS for data management (Map data, GeoJson, and 2D data), City Engine for assigning rule files of buildings, vegetation, water, road network and manipulation of 2D, 3D data, and QGIS for shape files. 3D modeling software Blender, and Nvidia Omniverse for the final digital twin was used. Using the potential of these tools and techniques, Digital Twin is proposed for the buildings, road network, and vegetation of the TEC District (Tecnologico De Monterrey District) region. Here, we integrated our dense captioning model with the TEC Distrcit digital twin to obtain captions of security events using knowledge graphs. The general idea of this investigation is to provide a better understanding of digital twins and dense video captioning. By leveraging the capabilities of these technologies, organizations can generate more accurate and insightful analyses of digital twin models, enabling a wide range of applications in various fields. These technologies will also aid surveillance and security in urban planning, offering significant benefits for organizations looking to optimize their operations and enhance their decision-making processes. All the models described in this investigation can be applied to a wider range of instances to achieve acceptable results with respect to time and quality.
- Compliant mechanisms and joints: control and tailored stiffness via architectured metamaterials(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-06-05) Arredondo Soto, Mauricio; Gómez Espinosa, Alfonso; dnbsrp; Román Flores, Armando; Urbina Coronado, Pedro Daniel; Jiménez Martínez, Moisés; School of Engineering and Sciences; Campus Monterrey; Cuan Urquizo, EnriqueIn this work the compliance matrix method is used to develop an analytical methodology for the kinetostatic analysis of Flexure-Based Compliant Parallel Mechanisms (FBCPM) under arbitrary force and displacements conditions. Furthermore, the characteristics of metamaterials, specifically the zero Poisson's ratio lattice structures, are used to design a novel type of prismatic compliant joints namely Zero Poisson's ratio Prismatic Compliant Joints (ZP-PCJ) with advantageous features such as high flexibility in a desired direction while achieving favorable levels of stiffness in the non-desired directions, and accurate analytical models that allow their implementation in FBCPM. In the first chapter, the relevant concepts are presented, in addition, the main problem, hypothesis and objectives are stated. The second chapter presents the literature review on the state of the art in the topics of the kinematic analysis of compliant mechanisms and the use of metamaterials for compliance purposes. Chapter three deals with the theoretical background corresponding to the Compliance Matrix Method (CMM) and the kinematic analysis of FBCPM using this method, ending with a summary of the CMM that synthesizes and unifies all the variants commonly found in the literature. In chapter four the proposed analytical method for the kinematic analysis of FBCPM is presented and successfully validated by three cases: i) using a 2D-FBCPM comparing with FEA-simulation results, ii) using a 3D-FBCPM comparing with FEA-simulation results, and iii) using a Compliant Spherical Parallel Mechanism (CSPM) comparing with both FEA-simulation and experimental results where input displacements were also used. Chapter five deals with the concept of meta-flexures, introducing the new type of prismatic compliant joints called ZP-PCJ based on the advantageous characteristics of zero Poisson's lattice structures. The compliance matrices of these ZP-PCJs are obtained analytically using Castigliano's second theorem and compliance simplification, and successfully validated with both FEA-simulations and experimental tests. In addition, the proposed ZP-PCJs are implemented in a 2D-FBCPM whose kinetostatic analysis is performed with the method presented in the previous chapter, demonstrating via FEA-simulations, the validity and accuracy of their analytical models. Finally, conclusions and future work are described in chapter six.
