Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Fatigue01:21

Fatigue

166
Fatigue occurs when materials rupture under repeated or fluctuating loads, even at stress levels far below their static breaking strength. It typically results in brittle failure, even for ductile materials. It is a critical consideration in designing machines and structural components subjected to repetitive or varying loads. The nature of these loadings can range from fluctuating loads like unbalanced pump impellers causing vibrations to repeatedly bending a thin steel rod wire back and forth...
166

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Advances in Fatigue Analysis and Numerical Simulation in Engineering Materials.

Materials (Basel, Switzerland)·2026
Same author

Atomistic-Based Fatigue Property Normalization Through Maximum A Posteriori Optimization in Additive Manufacturing.

Materials (Basel, Switzerland)·2025
Same author

Methodology for Hydrogen-Assisted Fatigue Testing Using In Situ Cathodic Charging.

Materials (Basel, Switzerland)·2025
Same author

Separation of Damage Mechanisms in Full Forward Rod Extruded Case-Hardening Steel 16MnCrS5 Using 3D Image Segmentation.

Materials (Basel, Switzerland)·2024
Same author

Characterization of Interfacial Corrosion Behavior of Hybrid Laminate EN AW-6082 ∪ CFRP.

Materials (Basel, Switzerland)·2024
Same author

Comparison of Various Intrinsic Defect Criteria to Plot Kitagawa-Takahashi Diagrams in Additively Manufactured AlSi10Mg.

Materials (Basel, Switzerland)·2023

相关实验视频

Updated: May 10, 2025

Production of Single Tracks of Ti-6Al-4V by Directed Energy Deposition to Determine the Layer Thickness for Multilayer Deposition
09:12

Production of Single Tracks of Ti-6Al-4V by Directed Energy Deposition to Determine the Layer Thickness for Multilayer Deposition

Published on: March 13, 2018

9.2K

人工智能驱动的非常高周期疲劳控制:优化微观结构设计用于选择性激光化Ti-6Al-4V.

Mustafa Awd1,2, Frank Walther3

  • 1Institute for Informatics and Automation (IIA), Bremen City University of Applied Sciences (HSB), Flughafenallee 10, D-28199 Bremen, Germany.

Materials (Basel, Switzerland)
|April 24, 2025
PubMed
概括

机器学习优化了耐用,耐疲劳的组件的增材制造. 这种方法提高了材料性能,并将设计周期缩短了50%以上,用于关键的航空航天和生物医学应用.

关键词:
添加剂制造 添加剂制造 添加剂制造机器学习是机器学习.微结构优化微结构优化过程参数优化过程参数优化非常高循环疲劳 (VHCF) 阻力

更多相关视频

Fabrication of Mechanically Tunable and Bioactive Metal Scaffolds for Biomedical Applications
09:56

Fabrication of Mechanically Tunable and Bioactive Metal Scaffolds for Biomedical Applications

Published on: December 8, 2015

10.6K
Indirect Fabrication of Lattice Metals with Thin Sections Using Centrifugal Casting
08:32

Indirect Fabrication of Lattice Metals with Thin Sections Using Centrifugal Casting

Published on: May 14, 2016

12.4K

相关实验视频

Last Updated: May 10, 2025

Production of Single Tracks of Ti-6Al-4V by Directed Energy Deposition to Determine the Layer Thickness for Multilayer Deposition
09:12

Production of Single Tracks of Ti-6Al-4V by Directed Energy Deposition to Determine the Layer Thickness for Multilayer Deposition

Published on: March 13, 2018

9.2K
Fabrication of Mechanically Tunable and Bioactive Metal Scaffolds for Biomedical Applications
09:56

Fabrication of Mechanically Tunable and Bioactive Metal Scaffolds for Biomedical Applications

Published on: December 8, 2015

10.6K
Indirect Fabrication of Lattice Metals with Thin Sections Using Centrifugal Casting
08:32

Indirect Fabrication of Lattice Metals with Thin Sections Using Centrifugal Casting

Published on: May 14, 2016

12.4K

科学领域:

  • 材料科学与工程 材料科学与工程
  • 机械工程 机械工程
  • 计算科学 计算科学

背景情况:

  • 增材制造 (AM) 为先进的材料设计提供了机会.
  • 在关键应用中,优化材料性能以提高耐疲劳性至关重要.
  • 高循环疲劳 (HCF) 测试对于评估材料耐用性至关重要.

研究的目的:

  • 将机器学习 (ML) 集成到AM中,以提高材料性能和耐疲劳性.
  • 探索机械 ML 技术来定制微结构.
  • 为了加速可靠,高性能元件的设计和生产.

主要方法:

  • 使用超声波疲劳测试收集非常高周期疲劳数据 (最多为1x10^10周期).
  • 应用机械机器学习模型来预测疲劳值和优化过程参数.
  • 分析了微观结构特征,如粒度方向和相位均性.

主要成果:

  • 机器学习模型将设计代周期减少了50%以上.
  • 通过微观结构改进,疲劳裂传播阻力提高了20-30%.
  • 在ML设计的元材料中,实现了15%的重量减轻和提高收益强度.
  • 确定了影响和合金微结构演变的关键过程参数 (温度梯度,冷却速率).

结论:

  • 在AM中集成ML显著提高了关键组件的疲劳寿命和可靠性.
  • 机械制造使轻质,高强度的元材料可用于各种应用的设计.
  • 这项研究为智能,适应性制造系统铺平了道路,提高了性能和效率.