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.
- 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.
