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.
- Optimisation of a parametric CAD geometry by means of a genetic algorithm(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2010-08-01) Ríos Soltero, Edgar Francisco; Ríos Soltero, Edgar Francisco; 242246; Cortés Ramírez, Jorge Armando; Galván Rodríguez, Arturo; Elizalde Siller, Hugo Ramón; Tecnológico de Monterrey, Campus Monterrey; Engineering Graduates Programme; Engineering DivisionThis thesis has the aim of optimising a three-dimensional geometry whose dimensions have been previously defined by parameters. Such a definition allows the geometry to change its shape by altering the value of a parameter without the need of directly modifying a geometrical feature (extrusion, protrusion, etc.) itself. In this way, a single value may affect more than one characteristic of the design. Then, the task is to find the best combination of parameters which improve the performance of a design. In here, the optimisation is performed by the computer by means of a genetic algorithm to change the values of such parameters. There is extensive work in the subject of optimising the performance of products by means of genetic algorithms (GA). In this regard, much work has been done by using equations which model their behaviour. The widespread use of computerised technology (for design and analysis) eventually allowed researchers to combine GA with computer-aided design (CAD) and engineering (CAE). This resulted in two major branches of GA-based optimisation of geometries: the agglomeration of finite elements and the combination of geometrical features. In here, a GA is used to iteratively modify (in CAD) and evaluate (in finite-element analysis) a parametric geometry. This geometry corresponds to a mechanism recently developed by the author. The device has both dynamic and static characteristics, however, the evaluations (analyses) performed on the design are purely static. An interface between a numerical analysis software (MATLAB1) and a CAD-CAE suite (CATIA1) is developed. The GA is run within the numerical analysis software and is interfaced to the CAD-CAE suite by means of its application programming interface (API). The results show the feasibility of improving a three-dimensional, feature-based and parametric CAD by means of the variations created by a GA.
