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

181
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...
181

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

Multiscale Investigation of CO<sub>2</sub>/Oil Competitive Adsorption and Interfacial Evolution in Shale Reservoirs.

ACS omega·2026
Same author

Nano-Delivery System for the Prevention and Control of the Disease.

Molecules (Basel, Switzerland)·2026
Same author

Targeting ATP11B-YAP axis repairs mitochondrial function and inhibits neuronal ferroptosis to attenuate age-related cognitive decline.

Signal transduction and targeted therapy·2026
Same author

Establishment and effectiveness of a community self-management intervention model for patients with chronic pulmonary heart disease: A prospective observational study.

Digital health·2026
Same author

Comment on "Neuron-specific transcriptomic dysregulation in methotrexate-induced cognitive impairment revealed by snRNA-seq".

International journal of surgery (London, England)·2026
Same author

Single-cell transcriptome analysis reveals mechanisms by which hippocampal deep brain stimulation promotes neurorepair and microglial subpopulation remodeling in ischemic stroke.

Journal of translational medicine·2026

相关实验视频

Updated: Jun 25, 2025

Surrogate Model Development for Digital Experiments in Welding
09:17

Surrogate Model Development for Digital Experiments in Welding

Published on: March 28, 2025

789

用神经网络对合金外进行数据物理融合驱动的缺陷预测.

Peng Yu1, Xiaoyuan Ji1, Tao Sun1

  • 1School of Materials Science and Engineering, State Key Laboratory of Materials Processing and Die & Mould Technology, Huazhong University of Science and Technology, Wuhan 430074, China.

Materials (Basel, Switzerland)
|May 25, 2024
PubMed
概括

这项研究开发了对合金外的孔隙缺陷的准确预测模型,这对航空发动机安全至关重要. 该研究确定了影响缺陷形成的关键过程参数,提供了提高造质量的策略.

关键词:
一个Ti合金Ti合金.投资造公司投资造多重回归多重回归神经网络的神经网络的神经网络收缩缺陷是因为收缩缺陷.

更多相关视频

Knowledge Based Cloud FE Simulation of Sheet Metal Forming Processes
11:05

Knowledge Based Cloud FE Simulation of Sheet Metal Forming Processes

Published on: December 13, 2016

12.2K
A Coupled Experiment-finite Element Modeling Methodology for Assessing High Strain Rate Mechanical Response of Soft Biomaterials
11:28

A Coupled Experiment-finite Element Modeling Methodology for Assessing High Strain Rate Mechanical Response of Soft Biomaterials

Published on: May 18, 2015

12.5K

相关实验视频

Last Updated: Jun 25, 2025

Surrogate Model Development for Digital Experiments in Welding
09:17

Surrogate Model Development for Digital Experiments in Welding

Published on: March 28, 2025

789
Knowledge Based Cloud FE Simulation of Sheet Metal Forming Processes
11:05

Knowledge Based Cloud FE Simulation of Sheet Metal Forming Processes

Published on: December 13, 2016

12.2K
A Coupled Experiment-finite Element Modeling Methodology for Assessing High Strain Rate Mechanical Response of Soft Biomaterials
11:28

A Coupled Experiment-finite Element Modeling Methodology for Assessing High Strain Rate Mechanical Response of Soft Biomaterials

Published on: May 18, 2015

12.5K

科学领域:

  • 材料科学 材料科学 材料科学
  • 制造业 工程 制造工程
  • 航空航天工程 航空航天工程

背景情况:

  • 合金外是航空发动机的关键部件,需要高度完整性.
  • 投资造合金外的孔隙缺陷显著影响运行安全和稳定性.
  • 目前的方法很难有效地控制影响缺陷形成的过程参数波动.

研究的目的:

  • 提出一种策略,以控制工艺参数对合金外收缩量和数量的影响.
  • 开发和比较ZTC4合金外中孔隙缺陷的预测模型.
  • 为了确定外缺陷对关键过程参数的敏感性.

主要方法:

  • 模拟ZTC4合金外的重力投资造.
  • 构建多重回归预测模型,用于孔隙体积和数量.
  • 开发神经网络预测模型,用于孔隙体积和数量.
  • 分析倒温度,倒时间和模具外温度对缺陷的影响.

主要成果:

  • 多重回归和神经网络模型在预测收缩腔总体量方面都达到了99%以上的准确性.
  • 神经网络模型表现出比多重回归模型更高的准确性.
  • 神经网络模型根据倒温度,倒时间和模具外温度准确预测了缺陷体积和数量.
  • 确定倒温度是最敏感的参数,其次是倒时间和模具温度.

结论:

  • 神经网络模型提供了一个强大的方法来预测和控制合金外的孔隙缺陷.
  • 优化过程参数窗口可以有效地减轻参数波动对缺陷形成的影响.
  • 这些发现为改善实际生产控制流程以减少造缺陷提供了宝贵的见解.