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Bending of Members Made of Several Materials01:08

Bending of Members Made of Several Materials

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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...
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Generalized Hooke's Law01:22

Generalized Hooke's Law

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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...
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Hooke's Law01:26

Hooke's Law

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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.
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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...
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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.
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Residual Stresses in Bending01:18

Residual Stresses in Bending

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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...
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Related Experiment Video

Updated: Jun 22, 2025

Characterizing Dissipative Elastic Metamaterials Produced by Additive Manufacturing
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Characterizing Dissipative Elastic Metamaterials Produced by Additive Manufacturing

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Identifying constitutive parameters for complex hyperelastic materials using physics-informed neural networks.

Siyuan Song1, Hanxun Jin1

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

Soft Matter
|July 2, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a physics-informed neural network (PINN) framework to accurately identify material parameters in complex soft materials. The robust model works even with noisy experimental data and intricate geometries.

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Area of Science:

  • Computational mechanics
  • Materials science
  • Machine learning

Background:

  • Identifying constitutive parameters in complex materials is challenging.
  • Existing physics-informed neural networks (PINNs) have limitations with complex behaviors and experimental data.
  • Soft materials with intricate geometries require advanced modeling techniques.

Purpose of the Study:

  • To develop a robust PINN-based framework for identifying material parameters in soft materials.
  • To address limitations of current PINN frameworks for complex constitutive laws and large deformations.
  • To enable accurate parameter identification using multi-modal synthetic experimental data.

Main Methods:

  • Developed a novel PINN framework for parameter identification in soft materials.
  • Utilized multi-modal synthetic datasets including full-field deformation and loading history for training.
  • Trained the PINN model to identify parameters for the incompressible Arruda-Boyce model under plane stress conditions.
  • Ensured algorithm robustness against experimental noise.

Main Results:

  • The PINN framework accurately identified constitutive parameters for the incompressible Arruda-Boyce model.
  • Achieved identification errors below 5% even with 5% experimental noise.
  • Demonstrated robustness for samples with intricate geometries and complex constitutive behaviors.
  • Successfully handled large deformation and plane stress conditions.

Conclusions:

  • The proposed PINN framework offers a robust approach for modulus identification in complex solids.
  • This method is particularly effective for materials with geometrical and constitutive complexity.
  • The framework advances the application of PINNs in materials science and engineering.
  • Enables more accurate material characterization from experimental data.