Persona:
Rodríguez Prieto, Álvaro

Foto de perfil
Dirección de correo electrónico
ORCID
0000-0002-0712-7472
Fecha de nacimiento
Proyectos de investigación
Unidades organizativas
Puesto de trabajo
Apellidos
Rodríguez Prieto
Nombre de pila
Álvaro
Nombre

Filtros

2021 - 20253
Restablecer filtros

Ajustes

Tipo: artículo ×

Resultados de la búsqueda

Mostrando 1 - 3 de 3
  • Miniatura
    Publicación
    The contribution of leadership to safety
    (Publicaciones DYNA, 2023-05-01) Bernabé Castaño, Miguel; Ferri Revert, Inés; Rodríguez Prieto, Álvaro
    Ineffective occupational safety and health leadership leads to inadequate safety requirements, poor safety supervision, ineffective training and poor safety attitudes. This can lead to a loss of productivity, reducing the economic benefits of the activity and, ultimately - and more importantly - leading to accidents, a negative impact on workers' health or even fatalities.
  • Miniatura
    Publicación
    Mechanical performance of 3D-printed TPU auxetic structures for energy absorption applications
    (ELSEVIER, 2025-02-01) Fuentes del Toro, Sergio; Crespo Sánchez, Jorge; Ayllón Pérez, Jorge; Rodríguez Prieto, Álvaro; Camacho López, Ana María
    The emergence of metamaterials and layered structures obtained through additive manufacturing (AM) techniques opens a new paradigm of mechanical properties for advanced applications that need to be explored. This study investigates the mechanical behavior of 3D-printed auxetic structures, fabricated from thermoplastic polyurethane (TPU), under tensile and compressive loads. Utilizing fused deposition modeling (MEX), we examined the influence of printing direction on the anisotropic mechanical properties of TPU, with a particular focus on energy absorption, stress–strain responses, and damping capabilities. The research employs the Ogden model for hyperelastic characterization, demonstrating excellent correlation with experimental data. Thus, the novelty of this work relies on an approach that – with a focus in the precision and accuracy of the mechanical performance assessment – through a robust novel methodology combining the Ogden’s analytical model with numerical simulation provided by Ansys® and experimental tests of tensile and compression allows to comprehensively understand the mechanical performance of novel auxetic structures intended to energy absorption and impact resistance applications. Our findings reveal significant variations in mechanical performance based on printing orientation, with the 0°direction offering superior ductility and strength. These results suggest that optimizing the printing direction is crucial for enhancing the performance of TPU auxetic structures, particularly in applications requiring high impact resistance, energy absorption, and damping. This study contributes to the advancement of 3D printing technology for the development of next-generation materials with potential applications in protective gear, medical devices or damping devices, among others.
  • Miniatura
    Publicación
    Topological Optimization of Artificial Neural Networks to Estimate Mechanical Properties in Metal Forming Using Machine Learning
    (MDPI, 2021-08-16) Merayo, David; Rodríguez Prieto, Álvaro; Camacho López, Ana María
    The ability of a metal to be subjected to forming processes depends mainly on its plastic behavior and, thus, the mechanical properties belonging to this region of the stress–strain curve. Forming techniques are among the most widespread metalworking procedures in manufacturing, and aluminum alloys are of great interest in fields as diverse as the aerospace sector or the food industry. A precise characterization of the mechanical properties is crucial to estimate the forming capability of equipment, but also for a robust numerical modeling of metal forming processes. Characterizing a material is a very relevant task in which large amounts of resources are invested, and this paper studies how to optimize a multilayer neural network to be able to make, through machine learning, precise and accurate predictions about the mechanical properties of wrought aluminum alloys. This study focuses on the determination of the ultimate tensile strength, closely related to the strain hardening of a material; more precisely, a methodology is developed that, by randomly partitioning the input dataset, performs training and prediction cycles that allow estimating the average performance of each fully-connected topology. In this way, trends are found in the behavior of the networks, and it is established that, for networks with at least 150 perceptrons in their hidden layers, the average predictive error stabilizes below 4%. Beyond this point, no really significant improvements are found, although there is an increase in computational requirements.