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相关概念视频

Bending of Members Made of Several Materials01:08

Bending of Members Made of Several Materials

147
In analyzing a structural member composed of two different materials with identical cross-sectional areas, it is crucial to understand how their distinct elastic properties affect the member's response under load. The analysis involves assessing stress and strain distributions using the transformed section concept, which accounts for variations in material properties.
Hooke's Law determines stress in each material, stating that stress is proportional to strain but varies due to each...
147
Generalized Hooke's Law01:22

Generalized Hooke's Law

892
The generalized Hooke's Law is a broadened version of Hooke's Law, which extends to all types of stress and in every direction. Consider an isotropic material shaped into a cube subjected to multiaxial loading. In this scenario, normal stresses are exerted along the three coordinate axes. As a result of these stresses, the cubic shape deforms into a rectangular parallelepiped. Despite this deformation, the new shape maintains equal sides, and there is a normal strain in the direction of the...
892
Hooke's Law01:26

Hooke's Law

375
Hooke's law, a pivotal principle in material science, establishes that the strain a material undergoes is directly proportional to the applied stress, defined by a factor called the modulus of elasticity or Young's modulus.
375
Members Made of Elastoplastic Material01:19

Members Made of Elastoplastic Material

94
The behavior of elastoplastic materials under bending stresses, particularly in structural members with rectangular cross-sections, is crucial for predicting material responses and understanding failure modes. Initially, when a bending moment is applied, the stress distribution across the section follows Hooke's Law and is linear and elastic. This distribution means the stress increases from the neutral axis to the maximum at the outer fibers, up to the elastic limit.
As the bending moment...
94
Relation between Poisson's ratio, Modulus of Elasticity and Modulus of Rigidity01:15

Relation between Poisson's ratio, Modulus of Elasticity and Modulus of Rigidity

263
Deformation occurs in axial and transverse directions when an axial load is applied to a slender bar. This deformation impacts the cubic element within the bar, transforming it into either a rectangular parallelepiped or a rhombus, contingent on its orientation. This transformation process induces shearing strain. Axial loading elicits both shearing and normal strains. Applying an axial load instigates equal normal and shearing stresses on elements oriented at a 45° angle to the load axis.
263
Residual Stresses in Bending01:18

Residual Stresses in Bending

158
In the study of elastoplastic members subjected to bending moments, understanding the loading and unloading phases is crucial for assessing material behavior and structural integrity. During the loading phase, as the bending moment increases, the material initially responds elastically, adhering to Hooke's Law, where stress is directly proportional to strain. When the load exceeds the yield strength, plastic deformation occurs, resulting in permanent strain and deformation that remains even...
158

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相关实验视频

Updated: Jun 22, 2025

Characterizing Dissipative Elastic Metamaterials Produced by Additive Manufacturing
09:39

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使用基于物理学的神经网络,确定复杂的超弹性材料的构成参数.

Siyuan Song1, Hanxun Jin1

  • 1School of Engineering, Brown University, Providence, RI 02912, USA. hanxun_jin@alumni.brown.edu.

Soft matter
|July 2, 2024
PubMed
概括

这项研究引入了物理信息神经网络 (PINN) 框架,以准确识别复杂软材料中的材料参数. 强大的模型即使在杂的实验数据和复杂的几何形状下也能工作.

科学领域:

  • 计算力学是计算力学.
  • 材料科学是一种材料科学.
  • 机器学习 机器学习

背景情况:

  • 在复杂材料中确定构成性参数是具有挑战性的.
  • 现有的物理信息神经网络 (PINNs) 具有复杂的行为和实验数据的局限性.
  • 具有复杂几何形状的软材料需要先进的建模技术.

研究的目的:

  • 开发一个基于PINN的强大框架,用于识别软材料中的材料参数.
  • 解决当前PINN框架对复杂的宪法法和大变形的局限性.
  • 为了使准确的参数识别使用多模式合成实验数据.

主要方法:

  • 开发了一种新的PINN框架,用于软材料中的参数识别.
  • 用于训练的多模式合成数据集,包括全场变形和负载历史.
  • 在平面应力条件下训练PINN模型以确定无压缩Arruda-Boyce模型的参数.
  • 确保了算法对实验噪声的稳定性.

主要成果:

  • 该PINN框架准确地确定了不可压缩的Arruda-Boyce模型的组成参数.
  • 即使在5%的实验噪声下,也实现了低于5%的识别错误.

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  • 对于具有复杂几何形状和复杂构成性行为的样品,证明了强度.
  • 成功处理大变形和平面应力条件.
  • 结论:

    • 拟议的PINN框架为复杂固体中模量识别提供了一个强大的方法.
    • 这种方法对于具有几何和构成复杂性的材料特别有效.
    • 该框架促进了PINNs在材料科学和工程中的应用.
    • 从实验数据中实现更准确的材料表征.