Tesis

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Colección de Tesis y Trabajos de grado (informe final del proyecto de investigación, tesina, u otro trabajo académico diferente a Tesis, sujeto a la revisión y aceptación de una comisión dictaminadora) presentados por alumnos para obtener un grado académico del Tecnológico de Monterrey.

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  • Tesis de maestría
    Metodología para Generar Mapas de Calidad de Agarre en Bin Picking de Escenarios Novedosos
    (2024-12-04) Robles Gil Candás, Andrés; Morales Menéndez, Rubén; emimmayorquin; Guedea Elizalde, Federico; Campus Monterrey; Vallejo Guevara, Antonio Jr.
    El presente trabajo propone una metodología para la generación de mapas de calidad de agarre en escenarios sintéticos, utilizando Blender, con el objetivo de entrenar una red neuronal para la tarea de bin picking en entornos industriales no estructurados. La metodología propuesta genera datos sintéticos mediante Blender, creando escenarios diversos que simulan condiciones reales, introduciendo aleatoriedad en la disposición, tamaño y orientación de los objetos. Se consideran variables clave como la planitud, suavidad, distancia al centro de masa, proximidad a los bordes, altura y el impacto del vector normal para la generación de mapas de calidad de agarre. Estos mapas sirven como etiquetas para entrenar una red neuronal U-Net, permitiendo identificar puntos óptimos de agarre en entornos nuevos y no estructurados. Los experimentos realizados mostraron que el modelo U-Net entrenado con datos sintéticos alcanzó un 82% de eficiencia en la tasa de éxito de agarre, con tiempos de procesamiento de 0.5 segundos por escenario, lo que lo posiciona como una opción significativamente más rápida y eficaz en comparación con DexNet 4.0. Estos resultados subrayan la capacidad del algoritmo para identificar puntos de agarre más adecuados, priorizando aquellos cercanos al centro de masa, lejos de los bordes, y sobre superficies planas. Además, se desarrolló un complemento de Blender de código abierto, diseñado para replicar y automatizar el proceso de generación de datos sintéticos, promoviendo su uso en futuros estudios o aplicaciones industriales
  • Tesis de maestría
    Costeo y Administración de Proyectos
    (Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024) López Páez, Eduardo; Administrador de proyectos; emimmayorquin; Escuela de Ingeniería y Ciencias; Campus Monterrey
    Una de las claves para que una empresa pueda seguir creciendo y no desaparecer, es lograr incrementar sus ventas a través de la innovación. Hoy en día para todo tipo de industrias hay mucha competencia tanto nacional como internacional y la clave para poder lograr separarse de la competencia es innovar. Prolamsa es una empresa que se dedica a la fabricación de tubería de acero y a la transformación de placa en diferentes formas. Hay una necesidad de poder tener una propuesta para una adecuada visualización y balanceo de proyectos. Por lo mismo hoy en día se desconoce cuánto cuesta iniciar, desarrollar, implementar y dar seguimiento a un proyecto nuevo y por ende también se desconoce el presupuesto que debería de tener el departamento para un año de trabajo. En este trabajo se presentas soluciones a las siguientes tres necesidades, balancear la carga de trabajo, obtener el costo de la administración de proyectos y obtener el presupuesto del departamento de proyectos para un año futuro. A lo largo del trabajo se verán diferentes desarrollos de soluciones, el análisis, la implementación, los resultados, la comparación y las conclusiones de los mismos. Estas herramientas podrán ayudar a tener una mejor planeación, un mejor balance del departamento, un mejor balance de carga del personal que integra el departamento, también se podrá tener el costo de los proyectos lo cual ayudará a entender cuanto se le está invirtiendo por proyecto. La administración de proyectos es algo que semana a semana tiene actualizaciones, cambios y se necesitan hacer ajustes por esto mismo estas herramientas serán de vital importancia en un departamento que recibe e implementa cientos de proyectos por año.
  • Tesis de maestría
    Tool Condition Monitoring System for Competitive Aluminum Milling
    (Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-12-02) Navarro Macías, Horacio Armando; Morales Menendez, Ruben; emimmayorquin; Guedea Elizalde, Federico; School of Engineering and Sciences; Campus Monterrey; Vallejo Guevara, Antonio Jr.
    In recent years, the auto parts industry has experienced a significant transformation, transitioning from gasoline-powered vehicles to electric vehicles, influenced by the Connected, Autonomous, Shared, and Electric (CASE) technologies trends. This shift is increasing the demand for advanced components like sensors and ECUs, requiring enhanced manufacturing techniques such as die casting and machining. However, North American manufacturers face a risk in competitiveness due to must of this mechanical parts are supplied by Asian suppliers, posing risks to increase manufacturing cost related to tariffs and logistics. To stay competitive and embrace these trends, North America needs to establish a CASE manufacturing hub to localize production. Denso is a Japanese mobility supplier that has provided advanced automobile technologies, components, and systems to major manufacturers since 1949, operating in 38 countries Denso (1 10). Established in 1996, Denso México (DNMX) has grown significantly, with four plants—two in northern Mexico, one in Silao, and a recent addition in Irapuato. As of March 2023, DNMX employs over 7,000 people, making it one of the largest facilities within Denso North America and playing a key role in the North American market for CASE products (Connected, Autonomous, Shared, and Electric vehicles). To improve competitiveness in the auto-parts and support the localization of parts the strategy of DNMX is to focus on enhancing the Monozukuri spirit1. The approach involves establishing a manufacturing foundation thru integration of advance industry 4.0 strategies, including IoT, automation, and data analytics, to optimize processes and improve efficiency and quality. In the context of CASE, the emphasis is on producing essential components like aluminum-machined cases for electric parts and inverter motors. To gain a competitive advantage, there is a significant investment in advanced technologies for machining processes, aiming to ensure cost efficiency, enhance productivity, maintain quality, and extend tool life. The real-time autonomous Tool Condition Monitoring System (TCMS) is a key element of this strategy, enhanced by Artificial Intelligence (AI), which leverages machine learning to analyze real-time data, predict tool wear, and prevent potential failures. The development and deployment of the AI-driven TCMS follow the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology, a robust framework widely adopted for data analytics projects. CRISP-DM ensures a structured approach through six phases: business understanding, where goals and objectives align with organizational strategy; data understanding, involving detailed exploration of machining and tool condition data; data preparation, including cleaning and structuring data for analysis; modeling, where machine learning algo-rithms predict tool wear and failure; evaluation, assessing model accuracy and alignment with objectives; and deployment, integrating the AI system into manufacturing processes. This methodology enhances the iterative refinement of predictive capabilities, ensuring alignment with strategic objectives and operational realities. By adopting CRISP-DM, DNMX ensures the systematic development of its AI-integrated TCMS, enhancing machining accuracy and reliability, optimizing maintenance schedules, and reducing downtime. This structured approach continuously improves the system, reinforcing DNMX’s leadership in the North American auto-parts industry and contributing to the transformation towards electric vehicles.
  • Tesis de maestría
    Evaluation of antimicrobial and biocompatibility properties of PEDOT: PSS/PVA hydrogel for biomedical applications in the treatment of wound healing
    (Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-11-24) Tapia Soto, Fátima Alejandra; Sanchez Lopez, Angelica Lizeth; emimmayorquin; Perfecto Avalos, Yocanxochitl; Navarro López, Diego Eloy; Iturbe Ek, Jackeline; School of Engineering and Sciences; Campus Monterrey; Lozano Sánchez, Luis Marcelo
    This study focuses on the development of a hydrogel based on PEDOT:PSS and PVA polymers, designed for biomedical applications, especially in wound healing. The combination of PEDOT:PSS , a conductive polymer, and PVA, a biocompatible and flexible polymer, is intended to produce a hydrogel that possesses antimicrobial, biocompatible and conductive properties suitable for accelerating wound healing and tissue regeneration. The research includes the synthesis of hydrogels with different concentrations of PVA and the evaluation of their properties, such as conductivity, swelling index and antimicrobial activity. Fourier transform infrared spectroscopy (FTIR) confirmed the successful crosslinking between PEDOT:PSS and PVA, validating the formation of a stable polymeric network. Swelling studies indicated a progressive increase in water absorption over 24 hours, demonstrating the ability of the hydrogel to maintain hydration in the wound areas. Conductivity tests revealed that although the inclusion of PVA slightly reduced the conductivity, the hydrogels still exhibited sufficient charge mobility, making them suitable for potential bioelectronic applications in wound care. Biocompatibility and cytotoxicity assessments were conducted using MTS cell viability assays, which showed that the hydrogels were non-toxic and suitable for application over a period of 24 to 48 hours. However, at higher PVA concentrations (notably 5%), the hydrogels began to degrade after 48 hours, indicating a limit on their effective application time. Antimicrobial testing using the agar diffusion method showed that PEDOT:PSS, especially when crosslinked with DMSO, effectively inhibited bacterial growth, particularly against Staphylococcus aureus and Escherichia coli. However, the addition of PVA significantly reduced this antimicrobial activity, with hydrogels containing 7% PVA showing no inhibition zones, suggesting that higher PVA concentrations interfere with the antibacterial effectiveness of PEDOT:PSS. These findings suggest that the PEDOT:PSS/PVA hydrogel formulation holds promise as a wound dressing material, balancing hydration, conductivity, and antimicrobial activity. The study highlights potential for optimization in PVA concentration and crosslinking agents to enhance the hydrogel’s performance in biomedical applications, offering a novel approach to wound management.
  • Tesis de maestría
    A prompt assisted image enhancement model using BERT classifier and modified LMSPEC and STTN techniques for endoscopic images
    (Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-12) Cerriteño Magaña, Javier; Ochoa Ruiz, Gilberto; emipsanchez; Sánchez Ante, Gildardo; Alfaro Ponce, Mariel; School of Engineering and Sciences; Campus Monterrey
    This document presents a research thesis for the Master in Computer Science (MCCi) degree at Tecnologico de Monterrey. The field of medical imaging, particularly in endoscopy, has seen significant advancements in image enhancement techniques aimed at improving the clarity and interpretability of captured images. Numerous models and methodologies have been developed to enhance medical images, ranging from traditional algorithms to complex deep learning frameworks. However, the effective implementation of these techniques often requires substantial expertise in computer science and image processing, which may pose a barrier for medical professionals who primarily focus on clinical practice. This thesis presents a novel prompt-assisted image enhancement model that integrates the LMSPEC and STTN techniques, augmented by BERT models equipped with added attention blocks. This innovative approach enables medical practitioners to specify desired image enhancements through natural language prompts, significantly simplifying the enhancement process. By interpreting and acting upon user-defined requests, the proposed model not only empowers clinicians with limited technical backgrounds to effectively enhance endoscopic images but also streamlines diagnostic workflows. To the best of our knowledge, this is the first dedicated prompt-assisted image enhancement model specifically tailored for medical imaging applications. Moreover, the architecture of the proposed model is designed with flexibility in mind, allowing for the seamless incorporation of future image enhancement models and techniques as they emerge. This adaptability ensures that the model remains relevant and effective as the field of medical imaging continues to evolve. The results of this research contribute to the ongoing effort to make advanced image processing technologies more accessible to medical professionals, thereby enhancing the quality of care provided to patients through improved diagnostic capabilities.
  • Tesis de maestría
    Interactive recipe suggestions for diet and allergen management: utilizing llaMA with HEI and DQI for healthier eating
    (Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-11-15) Estrada Beltrán, Diego; González Mendoza, Miguel; emipsanchez; Gutiérrez Uribe, Janet Alejandra; Domínguez Uscanga, Astrid; Hinojosa Cervantes, Salvador Miguel; School of Engineering and Sciences; Campus Monterrey
    Choosing daily meals can be a complex and overwhelming task, especially when considering nutritional requirements, ingredient availability, preparation time, cooking complexity, dietary restrictions, and allergens. Inadequate nutrition is linked to a variety of health problems, including cardiovascular diseases, obesity, and psychological disorders, highlighting the need for effective dietary management solutions. Existing machine learning approaches, such as food recommender systems, recipe generators, and recipe completion models, often focus on suggesting ingredients or generating recipes based on training data and with some ingredients to start from, but they typically do not address the challenge of creating complete daily meal plans that meet personalized nutritional needs. The advent of Large Language Models (LLMs), including Meta’s LLaMA, OpenAI’s ChatGPT, and Google’s Gemini, offers a promising new avenue for enhancing personalized meal recommendations due to their accessibility and interactive capabilities. This thesis introduces a novel system that leverages LLaMA 3.1 combined with Retrieval-Augmented Generation (RAG) to provide daily meal suggestions tailored to individual users’ nutritional profiles, dietary preferences, and allergen restrictions. Our system evaluates meal recommendations against established nutritional metrics such as the Healthy Eating Index (HEI) and Diet Quality Index (DQI) to ensure they align with dietary guidelines and promote healthy eating. Through the integration of LLaMA’s advanced language understanding and RAG’s contextual retrieval capabilities, the system delivers precise, personalized, and accessible meal recommendations, offering a practical tool for improving dietary management and supporting healthier eating habits. The results demonstrate the effectiveness of this approach in addressing the complexities of meal planning, making it a valuable resource for individuals seeking to optimize their dietary choices through informed and interactive guidance.
  • Tesis de maestría
    Impact of Industry 4.0 on Small and Medium Enterprises: Evaluation of Maturity Indices and Implementation Methodologies
    (Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-12-11) Delgado González, Jessica; Román Flores, Armando; emimmayorquin; Cuan Urquizo, Enrique; School of Engineering and Sciences; Campus Monterrey; Vázquez Hurtado, Carlos
    The digital transformation driven by Industry 4.0 technologies is reshaping global economic and business paradigms. Small and medium-sized enterprises (SMEs) in Mexico, which represent 99.8% of the country's economic units and contribute over 52% to its GDP, face significant barriers such as limited financial resources, technological gaps, and cultural resistance. These constraints, highlighted in recent studies, underscore the need for tailored tools to support their digitalization efforts. This thesis develops a digital maturity model specifically adapted to Mexican SMEs, integrating practical tools such as an assessment framework and a step-by-step action plan. The study begins by analyzing the theoretical foundations of Industry 4.0 and existing digital maturity models while addressing challenges unique to SMEs. Building on this foundation, the proposed model evaluates SMEs' current digital maturity and provides actionable recommendations through a simulation applied to a representative SME. The results demonstrate the model’s utility in identifying areas for improvement, fostering innovation, and enhancing competitiveness and sustainability in a globalized market. This work contributes academically by adapting global models to local contexts and practically by offering a replicable framework to bridge the digital divide in this critical economic sector.
  • Tesis de maestría
    A computer-based method to estimate the level of sensitivity of typical somatosensorial responses
    (Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-11) Cepeda Zapata, Luis Kevin; Alonso Valerdi, Luz Maria; emipsanchez; Angulo Sherman, Irma Nayeli; Muñoz Ubando, Luis Alberto; School of Engineering and Sciences; Campus Monterrey; Ibarra Zaratre, David Isaac
    Understanding somatosensory responses is fundamental to human interaction with the environment, yet quantitative tools for assessing typical tactile responses remain underdeveloped. This thesis introduces a novel computer-based method to evaluate somatosensory processing through electroencephalographic data, focusing on responses to different tactile stimuli. The project will be conducted in three stages: 1) registration of typical somatosensory evoked responses due to touch, air, and vibration in incremental intensities using electroencephalography, 2) validation of the prototypes to evoke tactile evoked potentials, 3) development and evaluation of a classification model to differentiate tactile stimuli and intensities. The study involved the creation of a database of Electroencephalographic recordings from 34 healthy adult volunteers exposed to air, vibration, and caress stimuli, under four diffrent intensity levels intensity levels. The neural responses were analyzed using Discrete Wavelet Transform and classified with machine learning models including K-Nearest Neighbors, Random Forest, and Multilayer Perceptron. For a generalized classification model, an accuracy of 72.6% was achieved for distinguishing stimulus type, 39.3% accuracy for intensity classification and 33.4% for both stimulus type and intensity. Individual classifiers for each subject had an increase in accuracy of 6-10%. Additionally, a deep learning model, EEGNet, was implemented, yielding similar results for stimulus type but lower performance for intensity. Analysis revealed significant inter-subject variability, with subject-specific models outperforming generalized ones, highlighting the need for individualized approaches in somatosensory assessments. This study offers a novel dataset and model framework, which enhances the understanding of neural tactile processing to advance sensory-based interfaces and diagnostic tools in neurophysiological research.
  • Tesis de maestría
    Design and development of a biomimetic robot based on the UCA pugnax CRAB
    (Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-12) Arriaga Ventura, Daniel Alberto; Bustamente Bello, Martín Rogelio; emipsanchez; Navarro Tuch, Sergio Alberto; School of Engineering and Sciences; Campus Ciudad de México; López Aguilar, Ariel Alejandro
    This research focused on developing a biomimetic robot modeled after the Uca Pugnax crab, utilizing a bottom-up approach that replicates the crab’s distinct locomotion and biomechanics. The primary objective was to create a robotic system capable of mimicking the crab’s movement and serving as a platform for further investigations into control systems, particularly in the implementation of bioinspired Central Pattern Generators (CPGs). By employing both Hopf and Kuramoto oscillator-based CPG systems, the robot’s locomotion was effectively demonstrated. The Kuramoto model, in particular, exhibited quicker convergence to the desired phase shifts, resulting in smoother and more reliable movement patterns. Comparative analysis of the reference trajectories generated by the CPG and the actual motor outputs revealed areas for optimizing controller performance, particularly in terms of response speed and amplitude precision. The robot’s morphology closely mirrored that of the crab, with an average proportional error of approximately 7.11%, indicating a successful bio-mimetic design. The robot’s movement dynamics also showed distinct functional push-and-pull motions, with clear advantages along different axes based on pinion position. Despite mechanical design constraints, such as the trade-off between manufacturing simplicity and accurate biomimicry, the robot’s overall performance demonstrated that biomimetic designs can effectively replicate crablike locomotion.
  • Tesis de maestría
    Desarrollo de un material compuesto basado en metal "arcilla metálica" para aplicaciones de manufactura aditiva en la fabricación de microdispositivos
    (Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-12-03) López Solís, Sergio Jesús; Segura Cárdenas, Emmanuel; emimmayorquin; Ulloa Castillo, Nicolas Antonio; Melo Máximo, Dulce Viridiana; Montañez Rodríguez, Abraham; Escuela de Ingeniería y Ciencias; Campus Monterrey
    Está investigación explora el desarrollo y la validación de un compuesto metálico llamado “arcilla metálica” mediante fabricación aditiva por extrusión. Su objetivo es identificar los parámetros clave para la composición del material, el proceso de fabricación y las condiciones de sinterización. El estudio analiza la formulación del compuesto metálico, la adaptación de una impresora 3D para la extrusión y los hallazgos experimentales sobre materiales como Inconel 718 y acero inoxidable 316L. Se estudian y prueban varias proporciones de agua, polvo metálico y aglutinante orgánico para lograr una extruibilidad óptima, y el análisis termogravimétrico y espectroscópico ayuda a comprender las propiedades térmicas. El trabajo también evalúa las optimizaciones de la impresión 3D, incluidos los ajustes de la impresora, los tamaños de las boquillas y las resoluciones de impresión, y examina la fabricación de microcanales con un enfoque en la precisión y los procesos de sinterización para minimizar la porosidad. El estudio concluye con información sobre cómo mejorar la calidad y la reproducibilidad de la impresión, lo que contribuye a la fabricación de microdispositivos de arcilla metálica.
En caso de no señalar algo distinto de manera particular, los materiales son compartidos bajo los siguientes términos: Atribución-No comercial-No derivadas CC BY-NC-ND http://creativecommons.org/licenses/by-nc-nd/4.0
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