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相关概念视频

Fatigue01:21

Fatigue

208
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...
208
Fatigue Strength of Concrete01:22

Fatigue Strength of Concrete

217
Fatigue, in the context of materials science and engineering, refers to the weakening or failure of a material caused by repeatedly applied loads, even if these loads are below the strength limit of the material. Fatigue strength in concrete is a critical property that influences its durability and longevity. Concrete can fail in two ways due to fatigue. Static fatigue or creep rupture occurs under a constant load or one that increases slowly. The other failure mode is due to cyclical or...
217
Microcracking in Concrete01:20

Microcracking in Concrete

149
Microcracking in concrete refers to the tiny cracks that can form within the material even before any external load is applied. These microcracks typically occur at the interface between the coarse aggregate and the hydrated cement paste, often as a result of differential volume changes prompted by variations in stress-strain behavior, as well as thermal and moisture movement. Initially, these microcracks remain stable and do not grow substantially until the concrete is stressed to about 30...
149
Stress-Strain Diagram - Ductile Materials01:24

Stress-Strain Diagram - Ductile Materials

840
The stress-strain relationship in ductile materials such as structural steel or aluminium is intricate and progresses through several stages. When a specimen is loaded, it initially exhibits a linear length increase, depicted by a steep straight line on the stress-strain diagram. It indicates the material is elastically deforming and will return to its original shape once unloaded. However, when a critical stress value is reached, plastic deformation begins. This stage sees substantial...
840
Yield Criteria for Ductile Materials under Plane Stress01:25

Yield Criteria for Ductile Materials under Plane Stress

186
In designing structural elements and machine parts using ductile materials, it is crucial to ensure that these components withstand applied stresses without yielding. Yielding is initially determined through a tensile test, which evaluates the material's response to uniaxial stress. However, tensile stress is insufficient when components face biaxial or plane stress conditions This condition requires advanced criteria to predict failure.
The Maximum Shearing Stress Criterion, also known as...
186
Stress-Strain Diagram - Brittle Materials01:24

Stress-Strain Diagram - Brittle Materials

2.6K
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...
2.6K

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在微观结构表示上使用图形神经网络进行材料疲劳预测.

Akhil Thomas1,2, Ali Riza Durmaz3,4, Mehwish Alam5

  • 1Fraunhofer Institute for Mechanics of Materials, Freiburg, Germany. akhil.thomas@iwm.fraunhofer.de.

Scientific reports
|August 2, 2023
PubMed
概括

在多晶体中预测疲劳损伤是具有挑战性的. 图形神经网络有效地识别铁钢中易受损坏的颗粒,优于其他模型,并揭示疲劳失败的微观结构驱动因素.

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科学领域:

  • 材料科学 材料科学 材料科学
  • 机械工程 机械工程
  • 计算科学 计算科学

背景情况:

  • 预测高循环疲劳下的多晶体疲劳损伤是一个持续的挑战.
  • 识别在循环负荷下容易发生塑性变形的颗粒对于理解材料故障至关重要.
  • 现有的方法难以准确地捕捉复杂的微观结构中的局部损伤启动.

研究的目的:

  • 开发一种用于预测多晶体局部疲劳损伤的新方法.
  • 利用图形神经网络 (GNN) 进行铁钢的粒度损伤分类.
  • 确定有效的数据表示和GNN模型,以了解疲劳损伤机制.

主要方法:

  • 来自实验数据的微纹理和损伤地图被转录为微结构图形表示.
  • 谷物被表示为节点,边缘连接相邻的谷物.
  • 图形卷积网络 (GCNs) 应用于二进制粒度损伤分类.

主要成果:

  • 图形卷积网络实现了0.72的平衡精度和0.34.1的F1得分.
  • GCN显著优于现象学晶体可塑性 (+68%) 和传统机器学习 (+17%) 模型.
  • 解释性分析突出了影响疲劳损伤开始的关键粒度和特征.

结论:

  • 在多晶体中,GNN为预测疲劳损伤的启动提供了一个强大的工具.
  • 这种方法可以揭示底层的微观结构驱动力和疲劳失败的机制.
  • 微结构图框架有助于将先进的机器学习技术应用于材料科学问题.