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.
- Stability analysis by the EMHPM for regular and multivariable cutting tools in milling operations(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2020-12-03) Sosa López, José de la luz; OLVERA TREJO, DANIEL; 269684; Daniel, Olvera Trejo; RR, emipsanchez; Elías Zúñiga, Alex; Palacios Pineda, Luis Manuel; Urbikain Pelayo, Gorka; School of Engineering and Sciences; Campus Monterrey; Martínez Romero, OscarMachining is a process by which a cutting tool removes material from a workpiece through relative movements between to achieve the desired shape. Milling is a common form of machining using rotary cutters to remove material by advancing a cutter into a work piece. The milling process requires a milling machine, workpiece, fixture, and cutter. When milling vibrations occur, they are usually produced by the impact of the vibration of the previous cut on the current one, this type of vibrations is known as self-excited vibration (chatter) since it occurs between the workpiece and the cutting tool. In this thesis we predict unwanted vibrations during the material removal process in milling using stability lobes. Since milling can be studied using a dynamic equation, a new method for solving a delay differential equation (DDE) is presented by using second- and third-order polynomials to approximate the delayed term using the Enhanced Homotopy Perturbation Method (EMHPM). Different simulations are performed first with regular and later with multivariable tools. To study the proposed method performance in terms of convergency and computational cost in comparison with the first-order EMHPM, Semi-Discretization and Full-Discretization Methods, a delay differential equation that model cutting milling operation process was used. To further assess the accuracy of the proposed method, a milling process with a multivariable cutter is examined to find the stability boundaries. Then, theoretical predictions are computed from the corresponding DDE finding uncharted stable zones at high axial depths of cut. Time-domain simulations based on Continuous Wavelet Transform (CWT) scalograms, Power Spectral Density (PSD) charts and Poincaré Maps (PM) were employed to validate the stability lobes found by using the third-order EMHPM for the multivariable tool and they were compared with experimental results.
