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相关概念视频

Plasticity00:58

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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Electrodeposition01:08

Electrodeposition

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Electrodeposition is a technique used to separate an analyte from interferents by electrochemical processes. Here, the analyte is a metal ion that can be deposited on an electrode immersed in the sample solution. The electrochemical setup consists of an anode and a cathode. When an electric current is applied to the setup, oxidation occurs at the anode. At the cathode, which consists of a large metal surface, metal ions undergo reduction and deposit onto the surface.
Electrodeposition can...
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相关实验视频

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Experimental Methods for Investigation of Shape Memory Based Elastocaloric Cooling Processes and Model Validation
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晶体可塑性参数优化在循环变形电沉积铜-A机器学习方法.

Karol Frydrych1,2, Maciej Tomczak1, Stefanos Papanikolaou1

  • 1NOMATEN Centre of Excellence, National Centre for Nuclear Research, Sołtana 7, 05-400 Otwock, Poland.

Materials (Basel, Switzerland)
|July 27, 2024
PubMed
概括

本研究介绍了一种机器学习方法,使用长期短期记忆网络来优化材料参数. 这种方法有效地预测了循环变形下的材料行为,而不需要为每个材料进行新的优化.

关键词:
埃舍尔比溶液是什么意思晶体的可塑性 晶体的可塑性循环变形的周期性变形长期短期记忆网络 长期短期记忆网络低循环疲劳的低循环疲劳机器学习是机器学习.优化的优化优化优化.自我一致的建模模型.

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A Novel Method for In Situ Electromechanical Characterization of Nanoscale Specimens
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Applying Dynamic Strain on Thin Oxide Films Immobilized on a Pseudoelastic Nickel-Titanium Alloy
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相关实验视频

Last Updated: Jun 18, 2025

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科学领域:

  • 计算材料科学科学 计算材料科学
  • 机器学习应用 机器学习应用
  • 材料工程 材料工程 材料工程

背景情况:

  • 参数优化对于准确的材料建模至关重要.
  • 传统方法可能耗时,需要大量的实验数据.
  • 具有异型和动力硬化的elasto-viscoplastic模型存在复杂的优化挑战.

研究的目的:

  • 在材料模型中应用机器学习方法以实现高效的参数优化.
  • 为了验证循环变形下的elasto-viscoplastic模型的方法.
  • 为了证明在没有进一步优化的情况下获得材料参数的能力,运行.

主要方法:

  • 使用基于长期短期记忆 (LSTM) 网络的机器学习方法.
  • 将该方法应用于一个包含异型和动力硬化的elasto-viscoplastic模型.
  • 在低周期疲劳状态下对循环变形的评估性能.

主要成果:

  • 实现了循环变形应力-张力曲线的合理一致.
  • 证明了获得新材料参数的能力,而无需重新优化.
  • 在复杂的问题上验证了该方法的稳定性,例如晶体可塑性和自我一致的模型.

结论:

  • 提出的基于LSTM的机器学习方法为材料参数优化提供了有效的替代方案.
  • 这种方法显著减少了对新材料的重复优化过程的需求.
  • 该方法的证明能力和稳定性使其广泛适用于各种实验和材料模型.