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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- Improving the design of multivariable milling tools combining machine learning techniques(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-12-05) Ramírez Hernández, Oscar Enrique; Olvera Trejo, Daniel; emipsanchez; Puma Araujo, Santiago Daniel; Martínez Romero, Oscar; School of Engineering and Sciences; Campus Monterrey; Fuentes Aguilar, Rita QuetziquelChatter in milling operations degrades surface quality, compromises dimensional accuracy, accelerates tool wear and may damage spindle components. One effective strategy to mitigate chatter while maintaining high productivity is the use of specialized milling tools, such as multivariable milling cutting tools (MMCT), designed with variable geometry in their pitch (𝜙) and helix (β) angles. However, identifying the combination of these angles remains challenging because of the absence of analytics models that link MMCT geometrical parameters with dynamic stability limits. This study proposes a novel approach that integrates analytical lobes calculation with machine learning to enhance tool design efficiency. We find optimal tool geometry (pitch and helix angles) and cutting conditions (spindle speed and axial depth) to maximize the Material Removal Rate (MRR) in milling of a single degree of freedom. Our approach employs a genetic algorithm (GA) combined with a pattern recognition neural network (NN) to predict whether specific parameter combinations will yield stable or unstable behavior. The Multilayer Feedforward Neural Network is trained using a database generated from simulation of a SDOF mathematical model of milling, a non-autonomous Delay Differential Equation. The solution to the DDE is approximated through the Enhanced Multistage Homotopy Perturbation Method (EMHPM). The database includes 23,606,700 observations, covering a catalog of 36,318 MMCT configurations and 650 cutting conditions (axial depth of cut and spindle speed) for each tool configuration. The NN training database uses an approach for handling variable cutting coefficients based on exponential fitting model to describe their variation. These coefficients were characterized at small radial immersion of 1.86 mm using cutting forces of five MMCTs with a diameter of 0.5 in. This approach accurately predicts cutting forces, achieving an NRMSE below 10% when compared with experimental signals. The trained NN estimates the stability of the milling process with an error of 3.3%. Additionally, the combined use of the NN and GA reduces computation time by 98% compared to the GA with EMHPM. The selection of five combinations of geometric parameters that maximize MRR in a range between 26% and 120%, compared to the MRR of a regular tool, which is 190,493 mm³/min, has been performed. The rate of increase in MRR depends on each of the five selected geometries (see Chapter 5). Moreover, without the proposed approach, identifying the improved geometry would require up to 25 days using an exhaustive search scheme, where a SLD is generated for 10,000 cutting conditions for every tool configuration.
- Design and manufacture of enhanced running-specific prosthetic blades based on randomly oriented carbon fiber/epoxy composites(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-06) Alcalá González, Sofía Jocelyn; Olvera Trejo, Daniel; emimmayorquin; Elías Zúñiga, Alex; Martínez Romero, Oscar; Ramírez Herrera, Claudia Angélica; School of Engineering and Sciences; Campus Monterrey; Cruz Cruz, IsidroEvery year, more than 850,000 people worldwide undergo major limb amputations due to causes such as vascular diseases and traumatic incidents. Despite the growing number of amputees, access to specific prosthetic devices remains limited, particularly in regions like Mexico. This highlights a significant gap in prosthetic accessibility and technology application. This study addresses this gap by creating a new prosthetic blade design, leveraging innovative material technologies and manufacturing processes. The primary goal is to develop a biomimetic design for a running-specific prosthetic blade using static simulations, assess the mechanical performance of randomly oriented fiber-reinforced composites, and evaluate the feasibility of using forged composites as a more sustainable manufacturing process. This alternative could significantly reduce material waste and production time for expanding production beyond elite athletes to everyday users. The methods involved in the study include developing in-house prepregs and randomly oriented strands to investigate their impact on mechanical properties, designing an improved prosthetic and utilizing Finite Element Analysis (FEA) for geometry selection and design comparisons, characterizing randomly oriented composite material evaluating the impact of reinforcement configuration, and producing components using the suggested curing process. The study demonstrated a reduction in waste since the process averaged a waste of 16%, showcasing a 4% reduction compared to the minimal waste reported for hand layup (20%). The proposed biomimetic blade showed superior strain energy with less deformation than the reference commercial design in static simulations. Also, highlighted the impact of thickness on component performance. Randomly oriented composites fabricated with the alternative curing process demonstrated superior handling and achieved tensile strengths up to 88 MPa and Young’s modulus of 11.03 GPa at 125°C. While comparable to the measured [± 45°] composite properties, there is room for improvement to meet the necessary strength requirements for running blades (≥ 700 MPa). Enhancing fiber distribution, refining heat treatment processes, exploring hybrid composites, and potential automation can further elevate the process's mechanical properties, sustainability, and cost-effectiveness. This study provides valuable insights into advanced composite materials and innovative manufacturing techniques, setting the stage for future advancements in high-performance prosthetic devices.
- High-density coaxial emitter device for electrospray: pushing the limits of additively manufactured microfluidic devices by SLA/DLP(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2022-06) Lozano Sura, Roberto Carlos; Olvera Trejo, Daniel; emimmayorquin; Escuela de Ingeniería y Ciencias; Campus MonterreyPhotopolymerization-based 3D printing technologies like SLA and DLP offer efficient fabrication of diverse structures, eliminating the requirement for molds or machining. These techniques allow fast fabrication of intricate designs such as microfluidics devices at low cost, however, the accuracy and resolution of microchannels are still challenging to control for very intricate microfluidics. This study focuses on understanding the influence of manufacturing parameters on the quality of microchannels and miniaturization limits by developing a mathematical model to predict mechanical properties depending on the curing times. Also, we study the limits of this technology in terms of the accuracy of microchannels for the fabrication of multiplexed coaxial electrospray sources. We demonstrate the effectiveness of the mathematical model to improve the quality of these devices with diameters of 360 um with 41 unclogged coaxial nozzles in 1 cm2. The contribution of this work also demonstrated incorporating purge channels was essential to clean uncured resin and the proposed procedure to ensure proper cleaning of the device. The devices were electrically characterized via electrospray processes observing uniform array operation depending on the nozzle size.
- Experimental analysis of high productivity multivariable cutting tools in milling operations towards the machining of composite material (CFRP/TiAl6V4)(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2021-02-02) Carrasco Mendoza, Heber Nahum; Olvera Trejo, Daniel; puelquio, emimayorquin; Puma Araujo, Santiago Daniel; Elías Zúñiga, Alex; School of Engineering and Information Technology; Campus Monterrey; Urbikain Pelayo, GorkaDuring the last years, there has been an increased interest in the study of composites, but more specifically in Carbon Fiber Reinforced Polymer with Titanium matrix (CFRP/TiAl6V4). Since the machining of composites is different and more complicated than other materials like metals, the cutting theories of metals cannot be used for the machining of composites. This research aims to develop an analytical/mechanistic method that can describe the high productivity multivariable cutting tools and predict the best cutting parameters to avoid chatter using stability lobes; towards the machining of CFRP/TiAl6V4. One outcome is developing and validating a new design for a multivariable cutting tool based on experimental data analysis. The study will be done in a practical and modeling environment to be optimized with a computational approach. Different modeling and optimization techniques will be explored to evaluate the performance of the design cutting tool. To obtain the ideal cutting coefficients, edge characterization was performed. Stability lobes were explored with the multivariable tools to get the best boundaries. Also, time-domain simulations based on the Continuous Wavelet Transform (CWT) graphs, Power Spectral Density (PSD) charts, and Poncairé Maps were used to validate the stability lobes boundaries found by using the first-order EMHPM for the multivariable tools.
- Electrohydrodynamic encapsulation of probiotics in heat-resistant mMicrocapsules for applications in the food industry(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2020-06) Toro Galárraga, David Alejandro; OLVERA TREJO, DANIEL; 269684; Olvera Trejo, Daniel; RR; Soría Hernández, Cintya Geovanna; Elías Zúñiga, Alex; School of Engineering and Sciences; Campus Monterrey; Martinez Romero, OscarProbiotics are an important part of functional foods and are defined as living microorganisms that confer health benefits to the host. Viable probiotics are, however, significantly destroyed during food thermal processing and in the stomach due to harsh digestive conditions. The challenge is to improve the survival of probiotic cells during manufacture, storage, and the passage through the gastrointestinal tract of the host in order to exert their health benefits. Various microencapsulation techniques have been used to protect probiotics against harsh conditions, however, these processes have low encapsulation efficiency, low yield and high energy consumption. On the other hand, electrospray microencapsulation can be used to produce capsules ranging from the micro to the sub-micron sizes, works at room temperature and has high encapsulation efficiency with narrow particle size distribution. The objective of this project was to create heat-resistant microcapsules (HRM) via electrospraying. To accomplish this, core and shell solutions were synthesized to perform encapsulation with metallic and 3D printed electrospray sources to increase the production rate. HRMs of 394.7±44.50 μm in diameter were obtained while physicochemical characterization shows a combination of parameters of both biopolymers, which is attributed to the formation of bonds between alginate and zein in the esterification process. The thermogravimetric analysis also shows an improvement in thermal properties, reducing weight loss due to material degradation at 250 ºC from 40% to 19%. This technology is a promising technology for probiotics encapsulation and fortification of foods thermally processed.