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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- Component Detection based on Mask R CNN(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2023) Charles Garza, Daniel; Morales, Rubén; emimmayorquin; Vallejo Guevara, Antonio; Guedea Elizalde, Federico; Escuela de Ingeniería y Ciencias; Campus MonterreyThis thesis delves into the evolution and utilization of deep learning methodologies in the specific context of object detection and segmentation within the manufacturing industry. It thoroughly examines several state-of-the-art object detection techniques, including YOLO, RCNN, Fast R-CNN, etc. These methods are explored in detail, assessing their effectiveness and applicability in complex object identification and classification tasks. The study then focuses on Mask R-CNN, a method chosen for its outstanding performance in object segmentation and identification; especially, in cluttered and unstructured environments common in manufacturing settings.
- 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.