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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- The augmented robotic cell: design and implementation of a testing cell that incorporates mixed reality and iot(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2022-06-20) Tavares Rodríguez, José Alberto; TAVARES RODRIGUEZ, JOSE ALBERTO; 835771; Urbina Coronado, Pedro Daniel; puelquio/mscuervo; Orta Castañón, Pedro; Ahuett Garza, Horacio; Escuela de Ingeniería y Ciencias; Campus MonterreyMixed Reality (MR) has the potential to be one of the key technologies aligned with the Industry 4.0 principles that many companies around the world are adopting. This technology, combined with the Internet of Things and the enhanced capabilities introduced by the Cyber-Physical Systems, can contribute to building a collaborative industrial environment. The integration of these concepts represents a new tool to improve and accelerate human-machine communication through digitalization and the connectivity between devices. Currently, many of the equipment employed within the Manufacturing Industry demand a minimum level of knowledge and experience for them to be used. In some cases, this can lead to the specialization of workers, which can have some advantages like reducing human error and operation time. However, this can also decrease the worker’s flexibility for performing different tasks or using other equipment than the ones it is used to. Besides, the learning process is commonly time-consuming and costly, and the risk of errors is not entirely eradicated. MR technologies, especially Augmented Reality (AR), can solve this issue by creating a virtual environment that can be used for training purposes or guidance while performing different tasks. AR makes it possible to merge the real world with the digital world, providing digital tools that enhance the visualization of valuable and easy-to-understand information in real-time. This can provide feedback to the user so the process can be better understood and help identifying possible improvements and failures while ensuring a safer environment for the worker. Although being a technology with many possible benefits, it is relatively new and comes with limitations that are yet to be overcome before being fully exploited. Therefore, it is crucial to study the current state of this technology in terms of capabilities, the feasibility of its integration for industrial applications, and the expected performance in this environment. This research work aims to implement an Augmented Robotic Cell that integrates AR technology with IoT. The system is used to test a 3D printed Compliant Mechanism, obtain data from this process, and display important information in an MR environment in near real-time. The AR device is able to retrieve relevant information from the Physical System and display it to the user while allowing him to interact with it.
- Machine learning to predict rework time for CNC router(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2021-11-30) González Giacoman, Daniel Alejandro; URBINA CORONADO, PEDRO DANIEL; 298324; Urbina Coronado, Pedro Daniel; puemcuervo; Orta Castañón, Pedro Antonio; Ahuett Garza, Horacio; School of Engineering and Sciences; Campus MonterreyThe industry is always in constant change and looking for ways to gain an advantage over its competitors. The fourth industrial revolution has brought massive change to the way things are done in the industry. The fourth industrial revolution brought Big Data, the Internet of things and Artificial intelligence, which gives us new ways to gather a lot of information from different sources and use it for our benefit. The present work develops a methodology to create a new machine learning algorithm to predict rework time for pieces that come out of a CNC router, using python and prove that for this case the created algorithm is better than a statistical model. To validate the methodology and prove the hypothesis of the thesis an experiment will be made to obtain 2 results: the best set of cutting parameters for the selected material and which is the best machine learning algorithm for this problem. To make the experiment the parameters must be set, a database needs to be created to train and test the ML algorithms and the code and libraries to be used should be created to fit the problem to be solved. This will be done by giving a background into databases, artificial intelligence, and how to know by the given results which type of artificial intelligence method is the best for the proposed problem.
- The mechanics of additively manufactured reentrant honeycombs: apparent elastic modulus and energy absorption ability under cyclic loadings.(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2021-06-14) Chapa Cárdenas, Amador; URBINA CORONADO, PEDRO DANIEL; 298324; Urbina Coronado, Pedro Daniel; emipsanchez; Román Flores, Armando; Escuela de Ingeniería y Ciencias; Campus Monterrey; Cuan Urquizo, EnriqueAdvances in additive manufacturing (AM) technologies have made possible the design and fabrication of more complex parts such as the cellular solids. Auxetic honeycombs are a type of cellular solid with already demonstrated enhanced mechanical properties and great potential as energy absorber. This work consists in the fabrication and characterization of reentrant honeycombs structures to study the feasibility of AM technology fused deposition modeling (fdm) as the manufacturing process and its effect on the mechanical properties of the printed parts. Numerical and experimental analysis were carried out to obtain the apparent elastic modulus of reentrant honeycombs and its relationship with the relative density of the specimens. Disadvantages of selecting fdm include low accuracy in the shapes printed and inability to print cell-wall thicknesses lower than 1 mm in cellular solids. A non-linear relationship between relative density of auxetic honeycombs and their apparent elastic modulus was found.
- Towards a digital twin by merging discrete event simulations, augmented reality and the internet of things(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2020-06-15) Valdivia Puga, José Abraham; URBINA CORONADO, PEDRO DANIEL; 298324; Urbina Coronado, Pedro Daniel; ilquio, emipsanchez; Ahuetl Garza, Horacio; Orta Castañón, Pedro Antonio; Escuela de Ingeniería y Ciencias; Campus MonterreyThis work presents the creation and implementation of an augmented reality system to a partial digital twin for a manufacturing process through the application of discrete event simulations, IoT tools and communication protocols in an industrial environment through a mobile app presentation. This approach will take a non-connected manufacturing process and adapt it to a smart factory environment creating an intercommunicated DES system with IoT and augmented reality capabilities. This approach was achieved using a real study case which consists in a machine generating items from a percentage of the input material while the rest of the material is stored in a recovery chamber to use it again in another item creation. This process was used as an example to create a partial digital twin and implement all the tools mentioned before. This work will allow to know all the process parameters, data related to the process like cycle time, number of pieces, time per process status, etc., this information will be used to create a system in which a real-time visualization of the process and its data can be performed through the use of augmented reality tools achieved by Unity 3D software. This approach will help to create a visualization for the partial digital twin, its process and results in a real context and even allow the user to modify its conditions before their implementation through a user interface, allowing to create an infrastructure that support different scenarios, process optimizations, and know possible future conditions.
- Discrete event simulation with integration of optimizations and the internet of things(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2020-06-12) García Martínez, Rubén Febronio; URBINA CORONADO, PEDRO DANIEL; 298324; Urbina Coronado, Pedro Daniel; RR; Ahuett Garza, Horacio; Orta Castañón, Pedro Antonio; Escuela de Ingeniería y Ciencias; Campus MonterreyThere is a lack of development in the discrete event simulation area. Representing a real process through a digital model has proven to be a useful tool because modifications can be carried out at no cost to corporations within a simulated environment. Even so, within the creation of DES for specific processes, it has different limitations (data collection, visual representation of the model, platform integration for the final simulation). That is why the present work proposes the integration of discrete event simulation (DES) with the environment of industrial internet of things and an industrial process. A simulated process was developed using Simpy, a Python tool, and Siemens Plant Simulation. Variables obtained from Python simulation were saved on a remote server using Google Scripts. The results of the Plant simulation were sent to an Excel data sheet. Comparisons of both simulations were performed, with variables such as cycle time, number of parts per day and other indicators. After comparing the simulations, other scenarios were tested to illustrate how the Python model can work with different data. Then, optimizations were carried out to maximize the number of items produced in different scenarios. Future work includes implementation in real factory environment and the interconnection with other technologies as augmented reality.