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.
Browse
Search Results
- Analyzing VR and AR I4.0 technologies for industrial applications: A comparative study and selection approach development(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-12-11) Chavez Najera, Daniela Monserrat; Ahuett Garza, Horacio; emipsanchez; Urbina Coronado, Pedro Daniel; Orta Castañón, Pedro Antonio; School of Engineering and Sciences; Campus MonterreyIn recent years, the implementation of immersive technologies such as Virtual Reality (VR) and Augmented Reality (AR) for Industry 4.0 (I4.0) applications has increased considerably. These technologies enable the connection of virtual and real environments focusing on human centered manufacturing. A challenge when implementing immersive technologies in industrial tasks is the lack of clear paths to select the most appropriate technology for specific operations, and the nonexistence of metrics to evaluate the integration performance. Nonetheless, there are trends in the literature that offer insights to conduct the decision making process for selection between immersive technologies, ensuring the suitability of the application. Based on the decision criteria identified in the literature a decision making approach is developed. This thesis also presents the development workflow of three VR/AR applications implemented in Unity Engine for Meta Quest 3 and Hololens 2. These applications are evaluated using overall performance metrics and are analyzed using the proposed approach.
- Magnetic gripper design optimization for robotic bending cell using artificial intelligence clustering of sheet metal parts(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-11-25) Treviño Treviño, Ana Paula; Ahuett Garza, Horacio; emipsanchez; Urbina Coronado, Pedro Daniel; Orta Castañón, Pedro Antonio; School of Engineering and Sciences; Campus MonterreyThe manufacturing sector is currently facing unprecedented challenges in adapting to the constantly evolving demands of diverse product lines and rapid market changes. Conventional manufacturing systems are struggling to adapt to the increasing variety of production components, leading to notable inefficiencies and heightened expenses. In this context, Reconfigurable Manufacturing Systems (RMS) have emerged as a prominent strategy to boost the adaptability and responsiveness of production processes. Therefore, the design and optimization of grippers for robotic arms are deemed essential to improve efficiency and productivity. The project aims to enhance gripper design by using AI clustering techniques and dimensional analysis to cluster production components and define design parameters for novel gripper configurations. This approach aligns with the tenets of lean manufacturing and data-driven decision-making, empowering manufacturing engineers and designers. The project also aims to optimize internal design and manufacturing, reducing reliance on external suppliers, and improving long-term adaptability and competitiveness by leveraging the cost reduction that in-house processes represent. The case study examines 964 sheet metal production components, highlighting inefficiencies of manual classification, part allocation challenges, and design specification retrieval. Furthermore, it explores different scenarios to render the best cluster quality possible with the supplied dataset and the constraints that materialize when translating the design parameters into actual design properties of the grippers, as well as the gripper-part compatibility. The thesis introduces an innovative method for managing part variety in gripper design by seizing advanced technologies and data-driven decision-making. This results in substantial enhancements in time efficiency, cost reduction, safety optimization, and the eradication of inefficient workflows within the manufacturing sector.
- Development of an augmented reality interface for a manufacturing monitoring system for deployment in Industry 4.0 environments(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2022-06-06) Higinio de Jesús, Miguel Ángel; AHUETT GARZA, HORACIO; 120725; Ahuett Garza, Horacio; tolmquevedo/mscuervo; Orta Castañon, Pedro Antonio; Urbina Coronado, Pedro Daniel; School of Engineering and Sciences; Campus MonterreyNowadays, the use of augmented reality (AR) has increased significantly in Industry 4.0 applications, it enables operators to visualize digital information over the physical world. The AR world offers access to important information, real-time monitoring, support and training for operators and interaction with 3D objects. The main objective of this work is to develop an AR interface for manufacturing monitoring of two processes and display setup instructions of a 3D printer. The first process is a fused deposition modeling 3D printing, and the second process is the inspection of the 3D printed parts. The AR interface will be displayed on Microsoft HoloLens 2 to use hand gestures recognition as input to perform an action. However, it can also be used on mobile devices. This work will allow to access and visualize data stored in the Cloud, inside and outside of a manufacturing cell. To achieve this work will be integrated different tools in Unity software such as Vuforia, Mixed Reality Toolkit, Apps Script and Visual Studio to solve the technical challenges of AR.
- Proof of concept for implementation of integration of additive manufacturing with vision system monitoring aided with a robot arm(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2021-12-13) Ozorno del Angel, Oscar Alain Gerardo; AHUETT GARZA, HORACIO; 120725; Ahuett Garza, Horacio; emijzarate/puemcuervo; Orta Castañon, Pedro; Urbina Coronado, Pedro; School of Engineering and Sciences; Campus MonterreyIn any process a competitive advantage means in saving of time, money, resources and in a process as 3D printing where many aspects can go wrong in the final part as lack of filament, bad adhesion printing or out of tolerance shapes by bad melting. To avoid some of these errors to happen by detecting them and stop the process of a wrong printing saving time, money and resources also with the potential to be scalable to an industrial process. To achieve this a proposed proof of concept of a semiautomatic 3D printer aided with a robot and a vision system to work autonomously with the least human interaction needed and the ability to do process monitoring to ensure quality in pieces and remove mistaken pieces while in the process save resources, this is achieved by Implementation of a synchrony routine in an arduino with programming, putting together decision making and pick and place operations to reduce human interaction. The main contribution is the implementation and architecture to achieve the synchrony of the three technologies 3D printer, vision system, and a robot arm working together to do a continuous process with inspection in real time in a 3D printer.
- Vision system for quality inspection of automotive parts based on non-defective samples(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2021-06-11) Vázquez Nava, Alberto; Ahuett Garza, Horacio; puelquio; Orta Castañón, Pedro Antonio; Urbina Coronado, Pedro Daniel; School of Engineering and Sciences; Campus MonterreyNowadays, companies in the automotive industry focus on delivering high-quality products to their customers, however, this task tends to be more complex as new car models emerge because new quality requirements must be learned. Currently in some companies, vision systems are used for the part quality inspection process, however, their learning process requires many correct and defective data to generate better predictions. Although it is possible to learn from correct samples, it is difficult to learn from defective parts because they are difficult to find in a company with strict quality standards. In this work, the implementation of machine learning classifier algorithms is proposed to detect correct and defective samples of different part types from the learning of only samples that meet quality standards. The feature extraction from images corresponding to suspension control arms and engine front covers was carried out, then a data augmentation process was applied to be analyzed by classifying algorithms in two stages: Part Identification and Geometric Quality Inspection. As a result, it was obtained that the Support Vector Machine classifier was the best algorithm in both stages, resulting in 100.0% accuracy in identifying the parts, 96.0% accuracy in detecting defective suspension control arms and 100.0% accuracy in finding defective front cover arms.
- Online process monitoring using a multivariate CUSUM approach with winsorization(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2020-06-12) Márquez Alderete, María Fernanda; AHUETT GARZA, HORACIO; 120725; Ahuett Garza, Horacio; tolmquevedo, emipsanchez; Escuela de Ciencias e Ingeniería; Campus Monterrey; Tercero Gómez, Víctor GustavoWith the development of Industry 4.0 (I4.0), companies are transforming the way products are designed, manufactured and distributed. The application of new technologies in production and data acquisition exacerbates the need to foster quantitative approaches in the quality management of manufactured products, such as statistical process monitoring (SPM). A measuring system machine for evaluating die-casted workpieces was designed following the previous trend. This machine already applies part of the theoretical concepts of I4.0. The presented thesis complements the application of I4.0 concepts to the device, by using SPM methods, specifically, a multivariate CUSUM to assess small and sustained shifts; where winsorizing was used to create robustness over isolated changes that can be detected using complementing Shewhart-type charts. Additionally, an online dashboard was created to display the plotting statistics in real-time.
- A proof of concept system for the implementation of path planning strategies in the context of additive manufacturing of composites(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2020-06-05) Salinas Sáenz, Sergio Alejandro; Ahuett Garza, Horacio; ilquio/tolmquevedo; Orta Castañón, Pedro; Urbina, Pedro; School of Engineering and Sciences; Campus MonterreyIn recent years, the use of additive manufacturing (AM) technologies has increased significantly in industrial applications. In AM processes, which can produce complex shapes layer by layer, the end-product presents anisotropic properties that depend mostly on the deposition trajectory. The problem is that there is a bottleneck in research and improvement of these properties, due to limitations on the deposition trajectory control. In the case of commercial systems, the end-product mechanical properties are not taken into consideration, and the limited selectable options impedes the designer’s tool-path strategies to be implemented. This thesis presents a proof of concept system integrated by an adapted machine system and a software framework that allows the designer to implement and test the path planning strategies for the deposition trajectory control. An overview of the hardware conditioning is explained, and a proof of concept strategy is proposed for increasing the deposition trajectory continuity, as a proof of use of the system in the context of additive manufacturing of composites.
- A proof-of-concept system for the implementation of a smart hybrid structure tool holder for cobots(Instituto Tecnológico y de Estudios Superiores de Monterrey) Frías Zárate, Paul; Ahuett Garza, Horacio; tolmquevedo, emipsanchez; School of Engineering and Sciences; Campus MonterreyIn the automotive industry has significantly increased the demand of improve the quality in the final product of steel structural parts in an optimized way in industrial applications. Nevertheless, the present processes do not include an intelligent tooling approach, limiting themselves to using simple tools based on non-real-time systems and without considering a structural complement that enhances its characteristics. In the case of industrial tools, a feature important not taken into in industrial manufacturing processes is the capacity to collect information that allows monitoring the status and performance of the process as well as that of the tool. This thesis presents a proof-of-concept system of an intelligent tool holder with a mechanical structure improved by a combination of manufacturing techniques. In addition, the possibility of being a real-time data acquisition system is proposed, allowing the implementation of a digital twin compatible with commercial systems corroborating the effectiveness of the use of cobots in industrial processes and test the system's ability to collect useful signals during a deburring process.
