Development of hybrid optimization techniques of mechanical components employing the cartesian grid finite element method

• Autores: David Muñoz Pellicer
• Directores de la Tesis: Juan José Ródenas García (dir. tes.), Enrique Nadal Soriano (dir. tes.)
• Lectura: En la Universitat Politècnica de València ( España ) en 2024
• Idioma: inglés
• Tribunal Calificador de la Tesis: Ludovic Chamoin (presid.), Manuel Tur Valiente (secret.), Icíar Alfaro Ruiz (voc.)
• Programa de doctorado: Programa de Doctorado en Ingeniería y Producción Industrial por la Universitat Politècnica de València
• Materias:
  • Matemáticas
    • Ciencia de los ordenadores
      • Inteligencia artificial
    • Análisis numérico
  • Ciencias tecnológicas
    • Tecnología e ingeniería mecánicas
      • Diseño de maquinas
• Enlaces
  • Tesis en acceso abierto en: RiuNet
• Resumen

  • This thesis explores innovative approaches for structural optimization, encompassing a variety of commonly used optimization algorithms in this field. It specifically focuses on shape optimization (SO) and topology optimization (TO). The first contribution of this research revolves around ensuring and maintaining a desired level of accuracy throughout the TO process and the proposed solution. By establishing confidence in the suggested components of the TO algorithm, our attention can then shift to the subsequent contribution.

    The second contribution of this thesis aims to establish effective communication between TO and SO algorithms. To achieve this, our goal is to directly convert the optimal material distribution proposed by the TO algorithm into geometry. Subsequently, we optimize the geometry using SO algorithms. Facilitating seamless communication between these two algorithms presents a non-trivial challenge, which we address by proposing a machine learning-based methodology. This approach seeks to extract a reduced number of geometric modes that can serve as a parameterization for the geometry, enabling further optimization by SO algorithms.

    Lastly, the third contribution builds upon the previous idea, taking it a step forward. The proposed methodology aims to derive new components through knowledge-based approaches instead of relying solely on physics-based TO processes. We argue that this knowledge can be acquired from the historical designs employed by a given company as they retain invaluable immaterial know-how. This methodology also relies on machine learning algorithms, but we also consider techniques for analyzing high-dimensional data and more suitable interpolation strategies.