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関連する概念動画

Yield Criteria for Ductile Materials under Plane Stress01:25

Yield Criteria for Ductile Materials under Plane Stress

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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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Stress-Strain Diagram - Ductile Materials01:24

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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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Hooke's Law01:26

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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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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.
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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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関連する実験動画

Updated: Jan 13, 2026

Determining the Mechanical Strength of Ultra-Fine-Grained Metals
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高エントロピー合金の組成からの降伏強度の予測：機械学習による加速

Seungtae Lee1, Seok Su Sohn1, Hae-Seok Lee2,3

  • 1Department of Materials Science and Engineering, Korea University, Seoul 02841, Republic of Korea.

Materials (Basel, Switzerland)
|January 10, 2026
PubMed
まとめ
この要約は機械生成です。

本研究では、高エントロピー合金（HEA）の降伏強度を予測する機械学習モデルを導入し、コストのかかる試行錯誤法を削減します。AIアプローチは、持続可能な開発のための新規HEA組成物の発見を加速します。

キーワード:
合金設計データ駆動型モデリング高エントロピー合金機械学習降伏強度予測

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関連する実験動画

Last Updated: Jan 13, 2026

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科学分野:

  • 材料科学
  • 冶金学
  • 計算材料科学

背景:

  • 高エントロピー合金（HEA）は例外的な特性を提供しますが、試行錯誤による非効率的な開発が行われています。
  • これは探索を妨げ、コストを増加させ、持続可能な生産に影響を与えます。

研究 の 目的:

  • HEAの降伏強度を予測するための機械学習（ML）方法論を開発すること。
  • 新規HEA組成物の設計と最適化を加速すること。

主な方法:

  • 181個のHEA組成データポイントに基づいてMLモデルをトレーニングしました。
  • 降伏強度予測においてR2乗（R²）スコア0.85を達成しました。
  • モデルの汎化性能を多様なHEAカテゴリ（カントール、耐火物、共晶）で検証しました。

主要な成果:

  • MLモデルは、様々なHEAタイプにわたる降伏強度の傾向を正確に予測しました。
  • 検証により、外部データセットに対する堅牢なパフォーマンスと信頼性が確認されました。
  • 予測された降伏強度データと実験的な降伏強度データの整合性が実証されました。

結論:

  • MLアプローチは、HEAの効率的な組み合わせ設計を促進します。
  • 望ましい特性のための合金組成物の迅速な最適化を可能にします。
  • この方法論は、持続可能な合金設計と環境に配慮した生産のためのガイドラインとして機能します。