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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- Methodological approach to incorporate Deep Generative and Natural Language Processing models in the engineering design process(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-06) de la Tejera de la Peña, Javier Alberto; Ramírez Mendoza, Ricardo Ambrocio; emimmayorquin; Hernández Luna, Alberto Abelardo; Aguayo Téllez, Humberto; Anthony, Brian; School of Engineering and Sciences; Campus Monterrey; Bustamante Bello, Martín RogelioThe engineering design process allows to create or enhance designs to fulfill any particular need methodologically. With the advances in artificial intelligence, mainly in Deep Learning, a new perspective is coming to systems engineering design, aided by intelligent algorithms. However, a downfall of engineering design is the lack of quantitative outcomes, making troublesome the use of artificial intelligence. For this, one of the solutions is using axiomatic design (AD) in the design process of systems. The work presented in this thesis, introduces a methodology that involves classic engineering methodologies and the proposal of incorporating current state-of-the-art algorithms and models for the conceptual design, as the main contribution of the research work. This research work and methodology proposal is meant to reduce the time of the design process and understand the needs/requirements implicated in the same design process, with the possibility of developing more robust designs closer to the original needs/requirements.
- A methodology for modeling multiscale multiphysics nature that bridges basic science with sustainable manufacturing technologies using human and Artificial intelligence(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-05-22) Estrada Diaz, Jorge Alfredo; Elías Zúñiga, Alex; emimmayorquin; Martínez Romero, Oscar; Palacios Pineda, Luis Manuel; Ruiz Huerta, Leopoldo; Escuela de Ingeniería y Ciencias; Campus Monterrey; Olvera Trejo, DanielThis dissertation deals with the modeling of multiscale multiphysics phenomena. These complex processes involve the interaction between physical occurrences of different nature, at different time and space scales, turning its description, prediction and control into a daunting task. Being pivotal technologies for the manufacturing of advanced materials, this work revolves around the complex technologies of Selective Laser Melting (SLM), electrospray, Ultrasonic Micro-Injection Molding (UMIM) and smart materials, i.e. Magneto-Rheological Elastomers (MRE). Modeling efforts are taken into action through classical yet powerful methodologies such as dimensional analysis and cutting-edge approaches such as fractal analysis and artificial intelligence, i.e., Artificial Neural Networks (ANNs) and Multiobjective Evolutionary Algorithms (MOEAs), with promising results that reflect on their ability to capture the intricate interplay of process parameters and material properties in these convoluted phenomena. Offering complementary benefits (attaining of meaningful physical insights and efficient handling computational processing operation and pattern identification in data, respectively) both approaches should be jointly exploited for handling multiscale multiphysics phenomena.