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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- A Digital Lean Thinking Framework & Human-Centric Modelling Approach for Patients Processes Continuous Improvement(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2022-02-04) Rodríguez Estrada, Regina; Romero Díaz, David Carlos; dnbsrp; Ruiz Soto, Gabriela María; Montesinos Silva, Luis Arturo; School of Engineering and Sciences; Campus Ciudad de MéxicoLean is a management and engineering methodology that promotes customer-centric value-added creation, continuous improvement, and operational excellence. It provides an organisational culture focused on leadership, communication, empowerment, and teamwork-oriented to customer satisfaction. Furthermore, it offers practical methods and tools to create highly efficient and effective processes for operational excellence. The Lean Healthcare Paradigm aims to create extraordinary patient care in the form of the highest quality, safety, and empathy with the patients, and the easiest and timely access to healthcare services at an affordable cost. It allows hospitals to improve the quality of their healthcare services by reducing errors, risks, and waiting times. Moreover, with the emergence of new digital tools, Lean Hospitals have started their Digital Transformation journey towards a novel Digital Lean Healthcare Paradigm in the pursuit of the next level of operational excellence and patient satisfaction. However, in the healthcare sector, it has been found that hospitals tend to struggle with their “digital transformation” for different reasons, either because they focus only on implementing digital technologies without having control over their processes, or because they do not take into account the needs of the patient, or simply they stay halfway. Thus, this thesis aims to create a Digital Lean Thinking Framework & Human-Centric Modelling Approach for healthcare organisations with a focus on patient-centredness to provide hospitals with a clearer and practical guide on how to carry out their “Digital Lean Healthcare Transformation”. Likewise, the proposed framework emphasises the need to follow a continuous improvement method such as the Plan-Do-Check-Act (PDCA) Cycle to have a more structured strategic planning with clear objectives, tasks, methods, timelines, and responsibilities. Finally, the case study of this thesis focuses on enriching and validating the proposed Digital Lean Thinking Framework & Human-Centric Modelling Approach for Patients Processes Continuous Improvement by “improving” the medical consultation process of a patient in the Gynaecology area of a private hospital in Monterrey, Mexico.
- 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 MéxicoSmart 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.
- Wire harness assembly tasks supported by collaborative robots(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2021-06-01) Navas Reascos, Gabriel Ernesto; Romero Díaz, David Carlos; puelquio; Rodríguez González, Ciro Ángel; Guedea Elizalde, Federico; Bustamante Bello, Martín Rogelio; School of Engineering and Sciences; Campus Ciudad de MéxicoConsidering that there is very little research on automation in the use of collaborative robots in the wire harness assembly process and that this process is carried out manually, causing ergonomic problems for employees who carry out this activity; a development was proposed to incorporate a collaborative robot in this process, for which the UR5 robot and the Cognex IS7905M camera were used, giving an integral solution. Ergonomic problems that could be reduced with the use of the collaborative robot were identified by two methodologies RULA and JSI. With them, it was possible to verify that a collaborative robot reduces non-ergonomic postures in the task of the placing of cable ties.
- An integrated framework for quality improvement based on six sigma and machine learning techniques towards zero-defect manufacturing(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2021) González Santacruz, Margarita Elisa; Romero Díaz, David Carlos; emiggomez, emipsanchez; School of Engineering and Sciences; Campus Ciudad de México; Noguez Monroy, Juana JulietaMaintaining high-quality standards is crucial for organizations in today's competitive market landscape. The emergence of the Quality 4.0 (Q4.0) paradigm has provided an opportunity to integrate Industry 4.0 technologies into Quality Management, particularly into Quality Improvement practices, transitioning from a traditional corrective vision to a predictive one. The Q4.0 paradigm emphasizes data-driven decision-making with a problem-solving approach, which aligns with the Zero-Defect Manufacturing (ZDM) philosophy. Hence, this thesis research presents an “Integrated Framework” that uses Machine Learning (ML) techniques to enhance the “Analyze” stage of the well-established Six Sigma DMAIC cycle for Quality Improvement (QI) to shift towards a Quality Predictive approach, with the identification of process variables that affect the occurrence of product defects and allow patterns recognition through ML techniques, paving the way for future work on real-time quality monitoring. The proposed Integrated Framework was developed based on a systematic literature review and aims to leverage ML techniques within the “Analyze” stage of the Six Sigma DMAIC cycle. Specifically, Supervised ML techniques, such as Logistic Regression, Random Forest, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Gradient Boosting Machine (GBM) – were employed to enhance product defects prediction. Unsupervised ML techniques, including Principal Component Analysis (PCA) and Clustering Analysis, were also used to identify critical process variables influencing product quality and propose QI strategies. The effectiveness of the proposed “Integrated Framework” is demonstrated in two real industrial case studies conducted in different manufacturing industries: Fashion and Automotive. Moreover, Case Study A (Fashion industry) investigates a dataset of 26,097 data points with 16 input variables and one output variable. The Integrated Framework demonstrates the significance of product and process variables such as Style, Fabric Code, and Machine Type in influencing the product defect rate, with Logistic Regression emerging as the optimal predictor. This analysis reveals valuable insights into specific product and process factors and variables that can be optimized to enhance product quality. Moreover, Case Study B (Automotive industry) analyzes a comprehensive dataset comprising 19,006 data points with 23 input variables and one output variable. The results highlight the superior predictive capabilities of KNN and Logistic Regression. At the same time, product variables such as Rake Up, Rake Down, R Push, R Push Distance, LU, R Pull, STN, UnitSN, and LC significantly impact product quality. This analysis yields valuable insights into specific product factors and variables that can be optimized to enhance product quality across both case studies. In conclusion, the proposed Integrated Framework was validated through two real industrial case studies, comparing it with the existing frameworks in the scientific literature, and it is the only framework to provide an experimental approach and the application of the Unsupervised Machine Learning Clustering technique for pattern recognition and analysis of the variables that impact product quality.
- Towards a Selective Laser Melting Process Parameters Optimization Approach using Regression Algorithms for Inconel 718 Manufactured Parts(Instituto Tecnológico y de Estudios Superiores de Monterrey) Arias López, José Alejandro; Romero Díaz, David Carlos; Rodríguez González, Ciro A.; Vázquez Lepe, Elisa V.; Escuela de Ingeniería y Ciencias; Escuela de Ingeniería y Ciencias; Campus Monterrey; Ruiz Huerta, LeopoldoIn recent decades, Additive Manufacturing (AM) technologies have received increasing interest from both academia and industry. Thanks to an unprecedented opportunity to create designs and products difficult to create through conventional manufacturing processes, such as those from subtractive manufacturing, the understanding of these processes have become imperative for the creation of reliable products. Different processes may produce parts from different materials, and from the many processes available, Powder Bed Fusion (PBF) stands out for its capacity to produce high-quality products with metallic alloys. From these metallic alloys, nickel-based superalloys are of particular interest for the aerospace and defence industry, because it possesses excellent mechanical properties during high-performance applications, such as those found in turbines, where high stresses and high temperatures bring design and engineering to its limits. Novel crystallographic structures, process complexity, and mechanical defects are but a few of the challenges AM technologies face to produce consistent and reliable parts. Selective Laser Melting (SLM), a subprocess of PBF, has been found to produce defects such as porosities and rough surfaces on additively manufactured parts, which have been found to hinder the fatigue life of as-built products. This research attempts to understand the relationships between variables involved in the SLM process and the formation of these defects. To achieve this, a literature review is realized to create a causal-loop that helps to understand the impact and correlation between the variables involved in the process, and their effect on the mechanical properties of the part. A compilation of governing equations, boundary conditions, and loads was also reviewed to allow the simulation of the SLM process on a Finite Element (FE) environment. Finally, regression analysis is made to determine the significance of the impact the process parameters and temperature gradients determined through the FE Analysis have over the mechanical defects. Recommendations based on this analysis for optimal process parameters values are given. Further research is required to analyse the impact of process parameters on the formation of residual stresses and crack formation.