Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Relation between Poisson's ratio, Modulus of Elasticity and Modulus of Rigidity01:15

Relation between Poisson's ratio, Modulus of Elasticity and Modulus of Rigidity

229
Deformation occurs in axial and transverse directions when an axial load is applied to a slender bar. This deformation impacts the cubic element within the bar, transforming it into either a rectangular parallelepiped or a rhombus, contingent on its orientation. This transformation process induces shearing strain. Axial loading elicits both shearing and normal strains. Applying an axial load instigates equal normal and shearing stresses on elements oriented at a 45° angle to the load axis.
229
Euler's Formula for Pin-Ended Columns01:21

Euler's Formula for Pin-Ended Columns

269
In structural engineering, the stability of columns under compressive axial loads is a critical consideration, described as buckling. A typical example involves a column PQ, which is pin-connected at both ends and subjected to a centric axial load F applied at one end, with a reaction force of F' = -F at the other end. Here, it is crucial to understand that when an applied load exceeds the critical load, buckling occurs as the system becomes unstable.
To calculate the critical load,...
269
Bending of Members Made of Several Materials01:08

Bending of Members Made of Several Materials

132
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...
132
Plastic Deformations of Members with a Single Plane of Symmetry01:21

Plastic Deformations of Members with a Single Plane of Symmetry

85
When a structural member undergoes plastic deformation due to bending, it is crucial to understand the position of the neutral axis and the stress distribution. This member, characterized by a single plane of symmetry, exhibits a uniform stress distribution, with negative stress above the neutral axis and positive stress below. Notably, the neutral axis does not align with the centroid of the cross-section. This misalignment is typical in cases where the cross-section is not rectangular or...
85
Elastic Curve from the Load Distribution01:16

Elastic Curve from the Load Distribution

146
The structural behavior of beams under distributed loads is critical for engineering analysis, which focuses on predicting how beams bend and react under such conditions. Different types of beams (e.g., cantilever, supported, or overhanging) behave differently under distributed load conditions.
For all beams, the analysis of the beam's reaction to distributed loads begins by understanding the relationship between a beam's load and the resulting shear forces and bending moments.
146
Members Made of Elastoplastic Material01:19

Members Made of Elastoplastic Material

92
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...
92

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Orbital magnetoresistance in the antiferromagnet CoO driven by dynamic orbital angular momentum.

Science (New York, N.Y.)·2026
Same author

Identifying grain boundary and intragranular pinning centres in Sm<sub>2</sub>(Co,Fe,Cu,Zr)<sub>17</sub> permanent magnets to guide performance optimisation.

Nature communications·2025
Same author

Model-Based Iterative Reconstruction of Three-Dimensional Magnetization in a Nanowire Structure Using Electron Holographic Vector Field Tomography.

Microscopy and microanalysis : the official journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canada·2025
Same author

Automotive Application of Chemically Foamed rPET.

Polymers·2025
Same author

Curved Nanomagnets: An Archetype for the Skyrmionic States at Ambient Conditions.

Nano letters·2025
Same author

Magnetic and mechanical hardening of nano-lamellar magnets using thermo-magnetic fields.

Nature communications·2025

Related Experiment Video

Updated: May 15, 2025

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

12.4K

Parametric cushioning lattice insole based on finite element method and machine learning: A preliminary computational

Zhenghui Lu1, Xin Li1, Dong Sun2

  • 1Faculty of Sports Science, Ningbo University, Ningbo, China; Faculty of Engineering, University of Pannonia, Veszprém, Hungary; Department of Material Science and Technology, Audi Hungaria Faculty of Automotive Engineering, Széchenyi István University, Hungary.

Journal of Biomechanics
|April 7, 2025
PubMed
Summary

This study introduces a controllable parameterized lattice cushioning insole (PLI) using finite element (FE) and machine learning (ML) methods. Optimized PLI designs significantly reduce plantar pressure, enhancing insole cushioning performance.

Keywords:
Additive manufacturingData-driven designFinite elementInsoleLattice structuresMachine learningParameter optimizationPlantar pressure

More Related Videos

Bioelectric Analyses of an Osseointegrated Intelligent Implant Design System for Amputees
14:31

Bioelectric Analyses of an Osseointegrated Intelligent Implant Design System for Amputees

Published on: July 15, 2009

13.9K
Finite Element Modeling for the Simulation of the Quasi-Static Compression of Corrugated Tapered Tubes
06:34

Finite Element Modeling for the Simulation of the Quasi-Static Compression of Corrugated Tapered Tubes

Published on: January 6, 2023

1.6K

Related Experiment Videos

Last Updated: May 15, 2025

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

12.4K
Bioelectric Analyses of an Osseointegrated Intelligent Implant Design System for Amputees
14:31

Bioelectric Analyses of an Osseointegrated Intelligent Implant Design System for Amputees

Published on: July 15, 2009

13.9K
Finite Element Modeling for the Simulation of the Quasi-Static Compression of Corrugated Tapered Tubes
06:34

Finite Element Modeling for the Simulation of the Quasi-Static Compression of Corrugated Tapered Tubes

Published on: January 6, 2023

1.6K

Area of Science:

  • Biomechanics
  • Materials Science
  • Computational Engineering

Background:

  • Insole cushioning is vital for design, but lacks standardized methods for material and structure selection.
  • Additive manufacturing (AM) and intelligent methods offer design freedom but require optimization guidance.
  • Current approaches lack a systematic framework for tailoring insole cushioning to specific needs.

Purpose of the Study:

  • To propose a controllable parameterized lattice cushioning insole (PLI) integrating finite element (FE) and machine learning (ML).
  • To establish a data-driven method for optimizing insole structures and materials for enhanced cushioning.
  • To provide a systematic and efficient solution for insole design challenges.

Main Methods:

  • Integration of finite element (FE) analysis with machine learning (ML) algorithms.
  • Development of a parameterized lattice structure with adjustable parameters (a, b) and strut thickness (t).
  • Optimization of the PLI design through computational modeling and data analysis.

Main Results:

  • The optimized PLI demonstrated a reduction in plantar pressure by up to 44.45%.
  • Optimal parameters (a=2.54, b=3.56, t=3.15) were identified for maximum cushioning effect.
  • The method simplifies the selection of insole structures and materials, enhancing cushioning performance.

Conclusions:

  • The data-driven PLI optimization method significantly improves insole cushioning.
  • This approach offers a systematic and efficient solution for insole design, reducing reliance on repeated physical testing.
  • The findings provide a foundational reference for clinical applications in insole structure design.