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Fatigue occurs when materials rupture under repeated or fluctuating loads, even at stress levels far below their static breaking strength. It typically results in brittle failure, even for ductile materials. It is a critical consideration in designing machines and structural components subjected to repetitive or varying loads. The nature of these loadings can range from fluctuating loads like unbalanced pump impellers causing vibrations to repeatedly bending a thin steel rod wire back and forth...
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Brittle materials, including glass, cast iron, and stone, exhibit unique characteristics. They fracture without considerable change in their elongation rate, indicating that their breaking and ultimate strength are equivalent. Such materials also show lower strain levels at the point of rupture. The failure in brittle materials predominantly results from normal stresses, as evidenced by the rupture created along a surface perpendicular to the applied load. These materials do not display...
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Simulación de Fracturación y Fallo de Materiales

Ryley G Hill1, Kai Gao2, Aleksandra Pachalieva2

  • 1National Security Earth Science Group, Earth and Environmental Sciences Division, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA. rghill@lanl.gov.

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|December 13, 2025
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Resumen
Este resumen es generado por máquina.

Este estudio presenta un gran conjunto de datos de evolución de fracturas simuladas en cinco materiales, generado mediante métodos de campo de fase y elementos finitos-discretos. Estos datos apoyan el desarrollo de modelos de aprendizaje automático para predecir fallos en materiales.

Palabras clave:
fracturafallosimulaciónaprendizaje automáticoconjunto de datos

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Área de la Ciencia:

  • Física
  • Ciencia de Materiales
  • Mecánica Computacional

Sus antecedentes:

  • Los fenómenos de fractura son críticos en diversos campos como la infraestructura, la aeroespacial y la geofísica.
  • La predicción de fallos en materiales requiere datos de simulación robustos.
  • Los métodos de simulación existentes varían en fidelidad y eficiencia.

Objetivo del estudio:

  • Presentar un conjunto de datos completo de la evolución de fracturas simuladas y fallos de materiales.
  • Apoyar el desarrollo de modelos de aprendizaje automático para la predicción de fallos.
  • Proporcionar una base para futuras investigaciones en mecánica computacional de fracturas.

Principales métodos:

  • Se generaron datos de fractura utilizando dos solucionadores numéricos distintos: el método de campo de fase y el método de elementos finitos-discretos (FDEM).
  • Se simularon cinco materiales: PBX, esquisto anisotrópico, tungsteno, aluminio y acero.
  • Se realizaron simulaciones exhaustivas bajo diversas condiciones de carga (tensión uniaxial y biaxial) con fracturas iniciales aleatorias.

Principales resultados:

  • Se creó un conjunto de datos diverso que abarca 490.000 casos de simulación.
  • Se capturaron las dinámicas temporales de propagación de fracturas para cada simulación.
  • El conjunto de datos incluye información detallada sobre la evolución de las fracturas bajo diferentes propiedades del material y estados de tensión.

Conclusiones:

  • El conjunto de datos presentado es un recurso valioso para avanzar en el análisis computacional de fallos de materiales.
  • Permite el desarrollo y la validación de enfoques de aprendizaje automático para predecir fracturas.
  • Facilita la investigación futura en física fundamental y aplicaciones de ingeniería de la mecánica de fracturas.