Ciencias Exactas y Ciencias de la Salud

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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 Monterrey
In 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 Monterrey
The 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.

Ciencias Exactas y Ciencias de la Salud

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