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

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

101
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
101

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

Self-Propagating High-Temperature Synthesis as an Enabling Route for High-Entropy MAX Phases.

Materials (Basel, Switzerland)·2026
Same author

Patient-Specific Lattice Implants for Segmental Femoral and Tibial Reconstruction (Part 1): Defect Patterns, Fixation Strategies and Reconstruction Options-A Review.

Biomimetics (Basel, Switzerland)·2026
Same author

Patient-Specific Lattice Implants for Segmental Femoral and Tibial Reconstruction (Part 2): CT-Based Personalization, Design Workflows and Validation-A Review.

Biomimetics (Basel, Switzerland)·2026
Same author

Wear Performance of a Physical Vapour Deposition-Coated, Spark Plasma Sintered TiB<sub>2</sub>/Ti Composite Lubricated with Externally Introduced hBN at Temperatures up to 900 °C.

Materials (Basel, Switzerland)·2025
Same author

Effect of Laser Surface Texturing and Fabrication Methods on Tribological Properties of Ti6Al4V/HAp Biocomposites.

Materials (Basel, Switzerland)·2025
Same author

Bioinspired and Multifunctional Tribological Materials for Sliding, Erosive, Machining, and Energy-Absorbing Conditions: A Review.

Biomimetics (Basel, Switzerland)·2024

相关实验视频

Updated: Sep 13, 2025

Longitudinal Micro-Computed Tomography Image Analysis for User-Defined Region of Interest in Critical-Sized Bone Defects
08:39

Longitudinal Micro-Computed Tomography Image Analysis for User-Defined Region of Interest in Critical-Sized Bone Defects

Published on: June 24, 2025

176

多目标机器学习优化用于骨植入物的圆柱形TPMS格子

Mansoureh Rezapourian1, Ali Cheloee Darabi2, Mohammadreza Khoshbin3

  • 1Department of Mechanical and Industrial Engineering, Tallinn University of Technology, 19086 Tallinn, Estonia.

Biomimetics (Basel, Switzerland)
|July 25, 2025
PubMed
概括
此摘要是机器生成的。

这项研究优化了使用人工神经网络和多目标优化进行骨植入的三次周期性最小表面 (TPMS) 格子. 开发了尺寸特定的设计,平衡机械性能和表面效率,以实现个性化医疗应用.

关键词:
约翰逊库克故障模型帕雷托前线分析分析人工神经网络 (ANN) 是一个人工神经网络.骨植入物是一种植入物.有限元素分析 (FEA) 是一种分析方法.机器学习是机器学习.机械属性预测和预测多目标优化多目标优化特定尺寸的植入物设计.三重周期性最小表面 (TPMS) 的使用.

更多相关视频

Precision Measurements and Parametric Models of Vertebral Endplates
10:35

Precision Measurements and Parametric Models of Vertebral Endplates

Published on: September 17, 2019

6.6K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.9K

相关实验视频

Last Updated: Sep 13, 2025

Longitudinal Micro-Computed Tomography Image Analysis for User-Defined Region of Interest in Critical-Sized Bone Defects
08:39

Longitudinal Micro-Computed Tomography Image Analysis for User-Defined Region of Interest in Critical-Sized Bone Defects

Published on: June 24, 2025

176
Precision Measurements and Parametric Models of Vertebral Endplates
10:35

Precision Measurements and Parametric Models of Vertebral Endplates

Published on: September 17, 2019

6.6K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.9K

科学领域:

  • 生物材料工程 生物材料工程
  • 计算生物学 计算生物学
  • 机械工程 机械工程

背景情况:

  • 三次周期最小表面 (TPMS) 格子为骨植入物提供了有前途的机械性能.
  • 优化TPMS格子设计需要平衡多个性能目标,并考虑解剖学变化.
  • 现有的设计框架往往缺乏针对不同植入物大小的具体性.

研究的目的:

  • 开发一个多目标优化框架,用于设计特定尺寸的TPMS晶格,用于骨植入物.
  • 使用人工神经网络 (ANN) 替代模型预测关键的机械和表面性能.
  • 根据小型,中型和大型解剖学变异量身定制格子设计.

主要方法:

  • 使用了使用NSGA-II算法的多目标优化框架.
  • 人工神经网络 (ANN) 被训练来预测最终的压力,能量吸收,表面积与体积的比率和相对密度.
  • 设计被分类并优化为小型,中型和大型植入物大小,相对密度过在20-40%之间.

主要成果:

  • 确定了105个帕雷托最佳设计,其中75个在过后保留了生物学相关的相对密度.
  • 沙普利添加式扩展 (SHAP) 分析表明格子厚度和单元细胞大小是主要的设计参数.
  • 在不同植入物大小组中观察到明显的性能趋势.

结论:

  • 开发的框架有效地平衡了TPMS网格的竞争性设计目标.
  • 特定尺寸的优化使得能够为骨植入物应用选择量身定制的格子.
  • 这种方法为优化生物架构提供了一条可重复的途径,在植入物开发中推进个性化医学.