| 01-06-2022 |
Predicción de contactos termo-elasto-hidrodinámicos por medio de métodos de aprendizaje automático
La reducción de las pérdidas por fricción y desgaste en los tribo-contactos lubricados de elementos de máquinas o componentes mecánicos sometidos a grandes cargas es esencial para desarrollar sistemas energéticamente eficientes y fiables. En particular, la modelización de los contactos concentrados y termo-elasto-hidrodinámicamente lubricados (TEHL), en los que se superponen la deformación elástica local de las superficies de fricción y la formación de una lámina de fluido hidrodinámico, es comparativamente compleja y computacionalmente costosa. Basado en resultados previos, este proyecto estudiantil se basará en la hipótesis de que los métodos de aprendizaje automático predicen las características de los contactos con gran exactitud y más rápidamente que los complejos modelos de simulación. Los datos de las simulaciones de contacto se utilizarán para entrenar enfoques de aprendizaje automático, como Artificial Neural Networks.
Prerequisitos:
no tiene.
Tiene un método de evaluación Nota 1-7, con 10 créditos y tiene 2/2 vacantes disponibles |
Mentor(es): Ver en la plataforma |
| 13-12-2023 |
Keywords:
Materiales
mecánica
Prerequisites:
None.
Evaluation method: Nota 1-7, with 0/1 available vacants |
Mentor(s): Open in the plataform |
| 09-12-2023 |
Keywords:
Prerequisites:
None.
Evaluation method: Nota 1-7, with 0/1 available vacants |
Mentor(s): Open in the plataform |
| 09-12-2023 |
Keywords:
Prerequisites:
None.
Evaluation method: Nota 1-7, with 0/1 available vacants |
Mentor(s): Open in the plataform |
| 06-12-2023 |
Prerequisites:
None.
Evaluation method: Nota 1-7, with 0/1 available vacants |
Mentor(s): Open in the plataform |
| 04-12-2023 |
Keywords:
Energía
Prerequisites:
None.
Evaluation method: Nota 1-7, with 0/1 available vacants |
Mentor(s): Open in the plataform |
| 30-11-2022 |
Additive manufacturing of 2D material-reinforced polymer cages for rolling bearings
One of the major challenges for mechanical systems, such as rolling bearings, is associated with friction and wear when conventional lubricants cannot or shall not be employed. Despite substantial advances in the solid lubrication ability of 2D materials due to their weakly bonded multi-layer structure and self-lubricating characteristics, the combination with additive manufacturing of bearing components remains unexplored. Undoubtedly, combining 3D printing techniques, such as fused deposition modeling (FDM) or selective laser melting (SLM), with 2D material-reinforced multifunctional composite structures has the potential to offer new opportunities for developing novel rolling bearing cage designs. The objective therefore is to develop and investigate the fabrication of polyamide matrix composites reinforced by molybdenum disulfide (MoS2) and MXene 2D nanomaterials by means of additive manufacturing towards tailor-made, lightweight, and high durability cages.
Prerequisites:
None.
Evaluation method: Nota 1-7, with -1/1 available vacants |
Mentor(s): Open in the plataform |
| 05-06-2022 |
Fabrication of 2D material-reinforced metal matrix composites by additive manufacturing.
Numerous medical devices are employed to treat or alleviate various diseases and injuries as well as anatomy and physiological process support. Their failures or malfunctions often relate to processes/problems occurring rubbing interfaces. Therefore, the biotribological behavior of such systems plays a crucial role in prolonging their safe and reliable operation. In this sense, there is a need to further enhance the mechanical properties and biotribological behavior of the materials. This can for example be done by reinforcing the materials with fillers. 2D materials such as graphene feature great potential for biological/biomedical applications. Moreover, the fabrication of multifunctional composite structures using additive manufacturing (AM) techniques like selective laser melting (SLM) for biomedical/biotribological applications remains relatively underexplored. Undoubtedly, combining additive manufacturing with 2D material-based metal matrix composites (MMCs) has the potential to offer up new opportunities for patient-specific (tailor-made) and wear-resistant implants.
Prerequisites:
None.
Evaluation method: Nota 1-7, with 0/3 available vacants |
Mentor(s): Open in the plataform |
| 01-06-2022 |
Prediction of Thermo-elastohydrodynamic contacts by machine learning approaches
Reducing friction and wear losses in the lubricated tribo-contacts of heavily loaded machine elements or mechanical components is essential for developing energy-efficient and reliable systems. In particular, the modeling of concentrated and thermo-elasto-hydrodynamically lubricated (TEHL) contacts, in which local elastic deformation of friction surfaces and the formation of a hydrodynamic fluid film are superimposed, is comparatively complex and computationally expensive. Based on previous results, this student project will be based on the hypothesis that machine learning methods predict contact characteristics with high accuracy and faster than complex simulation models. Data from contact simulations will be used to train machine learning approaches, such as Artificial Neural Networks.
Prerequisites:
None.
Evaluation method: Nota 1-7, with 2/2 available vacants |
Mentor(s): Open in the plataform |