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Related Concept Videos

Plastic Behavior01:21

Plastic Behavior

192
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...
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Stress-Strain Diagram - Ductile Materials01:24

Stress-Strain Diagram - Ductile Materials

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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...
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Yield Criteria for Ductile Materials under Plane Stress01:25

Yield Criteria for Ductile Materials under Plane Stress

156
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...
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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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Plastic Deformations01:14

Plastic Deformations

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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...
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Members Made of Elastoplastic Material01:19

Members Made of Elastoplastic Material

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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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Predicting Yield Strength and Plastic Elongation in Body-Centered Cubic High-Entropy Alloys.

Diego Ibarra Hoyos1, Quentin Simmons1, Joseph Poon1,2

  • 1Department of Physics, University of Virginia, Charlottesville, VA 22904, USA.

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Summary

Machine learning accurately predicts mechanical properties of body-centered cubic (BCC) high-entropy alloys (HEAs). This approach aids in designing stronger, more ductile alloys for structural applications.

Keywords:
d parameterfeature interpretationhigh-entropy alloysmachine learningplastic strain predictionyield strength prediction

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Area of Science:

  • Materials Science
  • Computational Materials Science
  • Alloy Design

Background:

  • High-entropy alloys (HEAs) offer tunable mechanical properties.
  • Predicting yield stress and plastic strain in BCC HEAs is crucial for structural applications.
  • Existing models may not fully capture the complex relationships governing HEA behavior.

Purpose of the Study:

  • To develop and validate a machine learning model for predicting yield stress and plastic strain in BCC HEAs.
  • To identify key features influencing mechanical properties of BCC HEAs.
  • To provide a predictive tool for accelerated design of novel HEAs.

Main Methods:

  • Machine learning (ML) models, specifically Random Forest Regression (RFR), were employed.
  • Feature engineering incorporated electronic factors, atomic ordering (mixing enthalpy), and the D parameter (stacking fault energy).
  • Genetic Algorithms were used for feature selection, and 10-fold cross-validation ensured model robustness.

Main Results:

  • The ML model achieved low Root Mean Square Errors (RMSE) in predicting yield stress and plastic strain.
  • Feature importance analysis revealed key predictors influencing mechanical properties.
  • The model demonstrated strong predictive accuracy and interpretability.

Conclusions:

  • Machine learning provides a powerful and accurate method for predicting mechanical properties of BCC HEAs.
  • This predictive capability facilitates the design of high-performance structural HEAs with enhanced strength and ductility.
  • The study offers a valuable tool for accelerating the discovery of new HEA compositions.