Ciencias Exactas y Ciencias de la Salud

Permanent URI for this collectionhttps://hdl.handle.net/11285/551039

Pertenecen a esta colección Tesis y Trabajos de grado de las Maestrías correspondientes a las Escuelas de Ingeniería y Ciencias así como a Medicina y Ciencias de la Salud.

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  • Tesis de maestría
    Towards a digital twin lifecycle management framework
    (Instituto Tecnológico y de Estudios Superiores de Monterrey, 2021-12-03) Villegas Torres, Luis Felipe; ROMERO DIAZ, DAVID CARLOS; 2219178; Romero Díaz, David Carlos; puemcuervo, emipsanchez; Rodríguez González, Ciro Ángel; Vazquez Lepe, Elisa Virginia; Bustamante Bello, Martín Rogelio; Urbina Coronado, Pedro Daniel; School of Engineering and Sciences; Campus Ciudad de México
    Smart Manufacturing has become one of the most important strategic priorities for manufacturing industries since it plays an important role in Industry 4.0 and Industrial Internet. Sensors and data transmission technologies are starting to be used most commonly to collect data at different stages of the product lifecycle, including product design, manufacturing, distribution, maintenance, and recycling. Big data analysis can enable the use of data to discover the causes of failures, simplify the supply chain, optimize product performance, improve production efficiency, etc. But to achieve these goals, they should first be able to overcome the challenge of connecting the physical product with its virtual product. The rapid development of advanced emerging technologies such as simulation, data acquisition, and data communication has helped to hold data synchronization between the physical product and the virtual product. In this way, is how Digital Twins (DT) came up to state the interactions between physical product and virtual product through a main channel called “Digital Thread” and generate the desired value from the captured data. Digital Twins, as an evolution of a cyber-physical system, has been paid more and more attention by academia and industry. DT can integrate physical and virtual data throughout the product lifecycle, thereby generating massive amounts of data that can be processed through advanced analysis. The results of the analysis can then be used to improve the performance of the product/process in the physical space. Being a relatively new concept, it lacks standards that homogenize the definition, maturity model, lifecycle, etc. among academic and industrial researchers. In this thesis, after conducting an exploration of the state-of-the-art, it was found that there is a need to make a first effort to establish a framework that guides DT designers throughout the entire lifecycle of a Digital Twin. This thesis presents a first approach towards a Digital Twin Lifecycle Management Framework that is sufficiently robust and comprehensive for its application in different use cases within the industry.
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