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Related Concept Videos

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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Plastic Deformations01:19

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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...
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Plastic Deformation in Circular Shafts01:20

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When materials are subjected to forces that surpass their yield strength, they undergo a process known as plastic deformation. This results in a permanent alteration or strain in their structure. This concept can be specifically applied to circular shafts, where the deformation leads to a change in its shape. The precise evaluation of this plastic deformation requires understanding the stress distribution within the circular shaft, which is achieved by calculating the maximum shearing stress in...
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Plastic Deformations of Members with a Single Plane of Symmetry01:21

Plastic Deformations of Members with a Single Plane of Symmetry

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When a structural member undergoes plastic deformation due to bending, it is crucial to understand the position of the neutral axis and the stress distribution. This member, characterized by a single plane of symmetry, exhibits a uniform stress distribution, with negative stress above the neutral axis and positive stress below. Notably, the neutral axis does not align with the centroid of the cross-section. This misalignment is typical in cases where the cross-section is not rectangular or...
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Plasticity

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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...
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Water-reducers, or plasticizers, are chemical admixtures used in concrete to improve strength and workability. These additives reduce the water-cement ratio without compromising workability, lower the cement content while maintaining the same workability, or increase workability to assist concrete placement in inaccessible areas.
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Machine learning plastic deformation of crystals.

Henri Salmenjoki1, Mikko J Alava1, Lasse Laurson2,3

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Predicting plastic deformation in crystalline solids is possible. Machine learning reveals that deformation predictability changes with strain and crystal size, with larger crystals being more predictable.

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Area of Science:

  • Materials Science
  • Solid Mechanics
  • Computational Materials Science

Background:

  • Plastic deformation in micron-scale crystalline solids shows significant variability in stress-strain curves.
  • Understanding the predictability of this variability is crucial for materials design and application.

Purpose of the Study:

  • To investigate whether the sample-to-sample variations in plastic deformation are random or predictable.
  • To explore how deformation predictability evolves with strain and crystal size.

Main Methods:

  • Employed machine learning techniques, including regression neural networks and support vector machines.
  • Utilized data from discrete dislocation dynamics (DDD) simulations.
  • Trained machine learning models to map pre-existing dislocation configurations to stress-strain curves.

Main Results:

  • Deformation predictability evolves non-monotonically with strain.
  • Predictability is influenced by crystal size, with larger systems showing higher predictability.
  • Stochastic deformation avalanches impose fundamental limits on predictability at intermediate strains.
  • Large-strain deformation dynamics are surprisingly predictable.

Conclusions:

  • The variability in plastic deformation is not purely random and exhibits predictable patterns.
  • Machine learning effectively predicts deformation behavior based on initial dislocation structures.
  • Crystal size and strain are key factors governing deformation predictability in micron-scale solids.