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

Members Made of Elastoplastic Material01:19

Members Made of Elastoplastic Material

460
The behavior of elastoplastic materials under bending stresses, particularly in structural members with rectangular cross-sections, is crucial for predicting material responses and understanding failure modes. Initially, when a bending moment is applied, the stress distribution across the section follows Hooke's Law and is linear and elastic. This distribution means the stress increases from the neutral axis to the maximum at the outer fibers, up to the elastic limit.
As the bending moment...
460
Bending of Members Made of Several Materials01:11

Bending of Members Made of Several Materials

664
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.
Hooke's Law determines stress in each material, stating that stress is proportional to strain but varies due to each material's...
664

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

Bimodal Interphase Architecture in Filled Elastomers: Molecular Dynamics Evidence and Experimental Signatures.

Molecules (Basel, Switzerland)·2026
Same author

Numerical Investigation of Die Swell Behavior in EPDM Rubber Extrusion: Effects of Compound Formulation and Processing Conditions.

Polymers·2026
Same author

Bound Rubber as a Transferable Structural Descriptor: Connecting MD-Derived Interfacial Scaling to Continuum Reinforcement Models.

Polymers·2026
Same author

Correction: Path planning of industrial robots based on the adaptive field cooperative sampling algorithm.

Frontiers in neurorobotics·2026
Same author

Path planning of industrial robots based on the adaptive field cooperative sampling algorithm.

Frontiers in neurorobotics·2025
Same author

New Perspective of Additive Manufacturing: From Materials to Processes and Algorithms of Continuous Carbon Fiber-Reinforced Composites.

3D printing and additive manufacturing·2024

相关实验视频

Updated: Mar 15, 2026

Characterizing Dissipative Elastic Metamaterials Produced by Additive Manufacturing
09:39

Characterizing Dissipative Elastic Metamaterials Produced by Additive Manufacturing

Published on: June 28, 2024

1.7K

通过多频动态机械分析对PC/ABS混合物进行机器学习辅助粘弹性表征.

Yancai Sun1,2,3,4, Wenzhong Deng2,3, Haoran Wang5

  • 1College of Electromechanical Engineering, Qingdao University of Science and Technology, Qingdao 266061, China.

Polymers
|March 14, 2026
PubMed
概括

本研究使用动态机械分析 (DMA) 和机器学习 (ML) 来预测聚合物粘弹性特性. 与数据驱动模型相比,基于物理的NeuralWLF模型提供了更好的概括性和可解释性.

关键词:
这是PC/ABS混合物.动态机械分析机械分析机器学习是机器学习.基于物理学的神经网络.时间温度叠加.粘性弹性 粘性弹性

更多相关视频

Sample Preparation in Quartz Crystal Microbalance Measurements of Protein Adsorption and Polymer Mechanics
08:21

Sample Preparation in Quartz Crystal Microbalance Measurements of Protein Adsorption and Polymer Mechanics

Published on: January 22, 2020

14.2K
Environmental Dynamic Mechanical Analysis to Predict the Softening Behavior of Neural Implants
06:59

Environmental Dynamic Mechanical Analysis to Predict the Softening Behavior of Neural Implants

Published on: March 1, 2019

8.4K

相关实验视频

Last Updated: Mar 15, 2026

Characterizing Dissipative Elastic Metamaterials Produced by Additive Manufacturing
09:39

Characterizing Dissipative Elastic Metamaterials Produced by Additive Manufacturing

Published on: June 28, 2024

1.7K
Sample Preparation in Quartz Crystal Microbalance Measurements of Protein Adsorption and Polymer Mechanics
08:21

Sample Preparation in Quartz Crystal Microbalance Measurements of Protein Adsorption and Polymer Mechanics

Published on: January 22, 2020

14.2K
Environmental Dynamic Mechanical Analysis to Predict the Softening Behavior of Neural Implants
06:59

Environmental Dynamic Mechanical Analysis to Predict the Softening Behavior of Neural Implants

Published on: March 1, 2019

8.4K

科学领域:

  • 材料科学 材料科学 材料科学
  • 聚合物物理 聚合物物理
  • 计算材料科学科学 计算材料科学

背景情况:

  • 描述聚合物混合物如PC/ABS的粘弹性特性对于材料设计至关重要.
  • 像DMA这样的传统方法可能很耗时,需要专家的解释.
  • 机器学习为加速和准确预测材料行为提供了潜力.

研究的目的:

  • 将多频动态机械分析 (DMA) 与机器学习 (ML) 结合起来,以表征和预测PC/ABS混合物粘弹性特性.
  • 评估和比较各种数据驱动的ML模型的性能与物理知情的NeuralWLF模型.
  • 建立DMA-ML模型评估中验证严格性的定量标准.

主要方法:

  • 在PC/ABS混合物上进行了多频DMA温度扫描.
  • 数据驱动模型 (RF,XGB,SVR,MLP) 和基于物理的NeuralWLF模型进行了训练和验证.
  • 采用了分层验证框架,包括温度阻塞交叉验证和离开一个功能 (LOFO).
  • 进行了系统的区块大小扫描,以调查验证通货膨胀,并确定差距与FWHM比率标准.

主要成果:

  • DMA的玻璃过渡范围为115.8-123.2°C,频率灵敏度为7.18°C/十年.
  • 基于物理学的NeuralWLF模型证明了具有可解释的威廉姆斯-兰德尔-费里 (WLF) 参数的优异交叉频率概括 (R2>0.92).
  • 在大约2的差距/FWHM比率下确定了物理数据交叉,超出这个比率,NeuralWLF的表现优于数据驱动模型.
  • 在严格的验证条件下,课程学习改善了NeuralWLF的性能 (30°C验证,R2=0.731).

结论:

  • 对DMA-ML模型的诚实评估需要超过特征特征宽度的验证差距.
  • 拟议的差距/FWHM比率作为评估验证严格性的定量标准.
  • 像NeuralWLF这样的物理知情模型在DMA数据的概括性和解释性方面具有优势,特别是在已识别的物理数据交叉点之外.