Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Plasticity00:58

Plasticity

2.7K
Plasticity is the property where an object loses its elasticity and undergoes irreversible deformation, even after the deformation forces are eliminated. If a material deforms irreversibly without increasing stress or load, then this is called ideal plasticity. For example, when a force is applied to an aluminum rod, it changes its shape, but it does not return to its original shape once the force is removed. Plastic deformation or ductility is thus a permanent deformation or change in the...
2.7K
Plastic Deformations01:19

Plastic Deformations

357
Plastic deformation represents a fundamental concept in materials science, which explains the irreversible change in the shape of a material when it experiences stress beyond its elastic capability. This phenomenon is important in structural engineering, especially in designing and analyzing cantilever beams—structures that are securely fixed at one end and bear loads at the opposite end. When these beams are subjected to loads within their elastic range, they will return to their...
357
Plastic Deformations01:14

Plastic Deformations

345
It is essential to understand how structural members behave under plastic deformation when the bending stress exceeds the material's yield strength. This state of deformation permanently alters the shape of the member, in contrast to the linear elastic behavior observed before yielding. The strain at any point in the member is expressed in terms of maximum strain. Notably, the neutral axis, which coincides with the centroid during elastic bending, shifts away from the centroid under plastic...
345
Plastic Behavior01:21

Plastic Behavior

459
A material's elastic behavior is characterized by the disappearance of stress once the load is removed, allowing the material to return to its original state. However, when stress surpasses the yield point, yielding commences, marking the onset of plastic deformation or permanent set. This change from elastic to plastic behavior is influenced by the peak stress value and the duration before the load is removed. An intriguing observation occurs when a specimen is loaded, unloaded, and...
459
Members Made of Elastoplastic Material01:19

Members Made of Elastoplastic Material

317
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...
317
Hooke's Law01:26

Hooke's Law

1.3K
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.
1.3K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Machine Learning-Based Surrogate Modelling for Efficient Inverse Analysis of Micro-Indentation Response to Determine Material Parameters.

Materials (Basel, Switzerland)·2026
Same author

Inverse Method to Determine Parameters for Time-Dependent and Cyclic Plastic Material Behavior from Instrumented Indentation Tests.

Materials (Basel, Switzerland)·2024
Same author

Mechanical Behavior of Austenitic Steel under Multi-Axial Cyclic Loading.

Materials (Basel, Switzerland)·2023
Same author

Micromechanical Modeling of AlSi10Mg Processed by Laser-Based Additive Manufacturing: From as-Built to Heat-Treated Microstructures.

Materials (Basel, Switzerland)·2022
Same author

Influence of Temperature on Void Collapse in Single Crystal Nickel under Hydrostatic Compression.

Materials (Basel, Switzerland)·2021
Same author

Hydrogen Embrittlement at Cleavage Planes and Grain Boundaries in Bcc Iron-Revisiting the First-Principles Cohesive Zone Model.

Materials (Basel, Switzerland)·2020

Related Experiment Video

Updated: Dec 25, 2025

Experimental Methods for Investigation of Shape Memory Based Elastocaloric Cooling Processes and Model Validation
11:11

Experimental Methods for Investigation of Shape Memory Based Elastocaloric Cooling Processes and Model Validation

Published on: May 2, 2016

11.5K

Data-Oriented Constitutive Modeling of Plasticity in Metals.

Alexander Hartmaier1

  • 1ICAMS, Ruhr-Universität Bochum, 44801 Bochum, Germany.

Materials (Basel, Switzerland)
|April 5, 2020
PubMed
Summary

This study introduces a novel machine learning approach for modeling metal plasticity, offering a flexible alternative to traditional yield functions. This data-oriented method efficiently captures material behavior under various loads and anisotropies.

Keywords:
constitutive modelingmachine learningplasticity

More Related Videos

Characterizing Dissipative Elastic Metamaterials Produced by Additive Manufacturing
09:39

Characterizing Dissipative Elastic Metamaterials Produced by Additive Manufacturing

Published on: June 28, 2024

1.4K
Subject-specific Musculoskeletal Model for Studying Bone Strain During Dynamic Motion
09:32

Subject-specific Musculoskeletal Model for Studying Bone Strain During Dynamic Motion

Published on: April 11, 2018

10.1K

Related Experiment Videos

Last Updated: Dec 25, 2025

Experimental Methods for Investigation of Shape Memory Based Elastocaloric Cooling Processes and Model Validation
11:11

Experimental Methods for Investigation of Shape Memory Based Elastocaloric Cooling Processes and Model Validation

Published on: May 2, 2016

11.5K
Characterizing Dissipative Elastic Metamaterials Produced by Additive Manufacturing
09:39

Characterizing Dissipative Elastic Metamaterials Produced by Additive Manufacturing

Published on: June 28, 2024

1.4K
Subject-specific Musculoskeletal Model for Studying Bone Strain During Dynamic Motion
09:32

Subject-specific Musculoskeletal Model for Studying Bone Strain During Dynamic Motion

Published on: April 11, 2018

10.1K

Area of Science:

  • Materials Science
  • Computational Mechanics
  • Machine Learning

Background:

  • Constitutive models for plastic deformation in metals rely on flow rules and yield functions.
  • Existing models often require explicit calculation of numerous parameters, especially for anisotropic materials.

Purpose of the Study:

  • To develop a novel mathematical formulation for elastic-plastic deformation using machine learning.
  • To create a flexible, data-oriented approach that can replace traditional yield functions.

Main Methods:

  • Developed a new mathematical formulation leveraging machine learning algorithms.
  • Reduced problem dimensionality by applying physical principles of elastic-plastic deformation.
  • Integrated the formulation into finite element analysis.

Main Results:

  • The data-oriented approach demonstrated flexibility in handling material anisotropy without extensive parameterization.
  • The new formulation showed applicability in finite element analysis, with results comparable to established models like Hill-like anisotropic plasticity.
  • The method allows for efficient use of machine learning for complex material behaviors.

Conclusions:

  • The proposed machine learning formulation offers a flexible and efficient alternative to conventional yield functions for modeling metal plasticity.
  • This data-driven approach simplifies the handling of material anisotropy and shows promise for integration with experimental and simulation data.
  • Future work can involve training the machine learning model with hybrid data for advanced scale-bridging homogenization.