Parametric study of patient-specific femoral locking plates based on a combined musculoskeletal multibody dynamics and finite element modeling

2017 ◽  
Vol 232 (2) ◽  
pp. 114-126 ◽  
Author(s):  
Xunjian Fan ◽  
Zhenxian Chen ◽  
Zhongmin Jin ◽  
Qida Zhang ◽  
Xuan Zhang ◽  
...  
Keyword(s):  
Finite Element ◽  
Finite Element Modeling ◽  
Fracture Healing ◽  
Multibody Dynamics ◽  
Locking Plate ◽  
Plate Thickness ◽  
Design Parameters ◽  
Patient Specific ◽  
Plate Interface ◽  
Element Modeling

A combined musculoskeletal multibody dynamics and finite element modeling was performed to investigate the effects of design parameters on the fracture-healing efficiency and the mechanical property of a patient-specific anatomically adjusted femoral locking plate. Specifically, the screw type, the thickness and material of the locking plate, the gap between two femoral fragments (fracture gap) and the distance between bone and plate (interface gap) were evaluated during a human walking. We found that the patient-specific locking plate possessed greater mechanical strength and more efficient fracture healing than the corresponding traditional plate. An optimal patient-specific femoral locking plate would consist of bicortical locking screws, Ti-6Al-4V material and 4.75-mm plate thickness with a fracture gap of 2 mm and an interface gap of 1 mm. The developed patient-specific femoral locking plate based on the patient-specific musculoskeletal mechanical environment was more beneficial to fracture rehabilitation and healing. The patient-specific design method provides an effective research platform for designing and optimizing the patient-specific femoral locking plate under realistic in vivo walking conditions, which can be extended to the design of other implants as well as to other physiological loading conditions related to various daily activities.

scholarly journals Method for patient-specific finite element modeling and simulation of deep brain stimulation

2008 ◽  
Vol 47 (1) ◽  
pp. 21-28 ◽  
Author(s):  
Mattias Åström ◽  
Ludvic U. Zrinzo ◽  
Stephen Tisch ◽  
Elina Tripoliti ◽  
Marwan I. Hariz ◽  
...  
Keyword(s):  
Finite Element ◽  
Deep Brain Stimulation ◽  
Finite Element Modeling ◽  
Modeling And Simulation ◽  
Brain Stimulation ◽  
Patient Specific ◽  
Element Modeling ◽  
Deep Brain

Full-scale finite element modeling and nonlinear bending analysis of sandwich plates with functionally graded auxetic 3D lattice core

2020 ◽  
pp. 109963622092465 ◽  
Author(s):  
Chong Li ◽  
Hui-Shen Shen ◽  
Hai Wang
Keyword(s):  
Finite Element ◽  
Finite Element Modeling ◽  
Plate Thickness ◽  
Functionally Graded ◽  
Full Scale ◽  
Sandwich Plates ◽  
Nonlinear Bending ◽  
Element Modeling ◽  
Bending Load ◽  
Lattice Core

This paper investigates the nonlinear bending behavior of sandwich plates with functionally graded auxetic 3D lattice core. First and foremost, an auxetic 3D lattice metamaterial with negative effective Poisson’s ratio (EPR) is designed and examined via theoretical and finite element methods with experimental verifications using specimens fabricated by 3D printing. Furthermore, three functionally graded configurations of the auxetic 3D lattice core through the plate thickness direction are proposed and compared with the uniform distribution case. Full-scale finite element modeling and nonlinear thermal-mechanical analysis are performed for the sandwich plates, with the temperature-dependent material properties of both core and face sheets taken into account. Numerical results revealed that the auxetic core can remarkably reduce the lateral deflections, with comparison to their non-auxetic counterpart with positive EPR. Parametric studies are further carried out to demonstrate the effects of functionally graded configurations, temperature rises, facesheet-to-core thickness ratios, boundary conditions, and strut radii on the nonlinear bending load-deflection curves, along with EPR-deflection curves in the large deflection region.

Corrugated structures reinforced by shape memory alloy sheets: Analytical modeling and finite element modeling

2018 ◽  
Vol 233 (7) ◽  
pp. 2445-2454 ◽  
Author(s):  
Maryam Koudzari ◽  
Mohammad-Reza Zakerzadeh ◽  
Mostafa Baghani
Keyword(s):  
Finite Element ◽  
Shape Memory Alloy ◽  
Shape Memory Alloys ◽  
Finite Element Modeling ◽  
Shape Memory ◽  
Smart Structure ◽  
Design Parameters ◽  
Asymmetric Mode ◽  
Element Modeling ◽  
Structure Changes

In this study, an analytical solution is presented for a trapezoidal corrugated beam, which is reinforced by shape memory alloy sheets on both sides. Formulas are presented for shape memory alloys in states of compression and tension. According to the modified Brinson model, shape memory alloys have different thermomechanical behavior in compression and tension, and also these alloys would behave differently in different temperatures. The developed formulation is based on Euler–Bernoulli theory. Deflection of the smart structure and the effect of asymmetric response in shape memory alloys are studied. Results found from the semi-analytic modeling are compared to and validated through a finite element modeling, and there is more than [Formula: see text] agreement between two solutions. With regard to the results, the neutral axis of the smart structure changes in each section. The maximum deflection ratio of asymmetric mode to symmetric one mode is 1.7. Additionally, the effect of design parameters on deflection is studied in detail.

A Predictive Analysis of Wall Stress in Abdominal Aortic Aneurysms Using a Neural Network Model

2021 ◽  
Author(s):  
Balaji Rengarajan ◽  
Sourav Patnaik ◽  
Ender A. Finol
Keyword(s):  
Neural Network ◽  
Finite Element ◽  
Finite Element Modeling ◽  
Wall Stress ◽  
Aortic Aneurysms ◽  
Multivariate Adaptive Regression Splines ◽  
Patient Specific ◽  
Clinical Images ◽  
Element Modeling ◽  
Abdominal Aortic

Abstract In the present work, we investigated the use of geometric indices to predict patient-specific abdominal aortic aneurysm (AAA) wall stress by means of a novel neural network (NN) modeling approach. We conducted a retrospective review of existing clinical images of two patient groups: 98 asymptomatic and 50 symptomatic AAA. The images were subject to a protocol consisting of image segmentation, processing, volume meshing, finite element modeling, and geometry quantification, from which 53 geometric indices and the spatially averaged wall stress (SAWS) were calculated. We developed feed-forward NN models composed of an input layer, two dense layers, and an output layer using Keras, a deep learning library in Python. The NN models were trained, tested, and validated independently for both AAA groups using all geometric indices, as well as a reduced set of indices resulting from a variable reduction procedure. We compared the performance of the NN models with two standard machine learning algorithms (MARS: multivariate adaptive regression splines and GAM: generalized additive model) and a linear regression model (GLM: generalized linear model). The NN-based approach exhibited the highest overall mean goodness-of-fit and lowest overall relative error compared to MARS, GAM, and GLM, when using the reduced sets of indices to predict SAWS for both AAA groups. The use of NN modeling represents a promising alternative methodology for the estimation of AAA wall stress using geometric indices as surrogates, in lieu of finite element modeling.

Female patient-specific finite element modeling of pelvic organ prolapse (POP)

2015 ◽  
Vol 48 (2) ◽  
pp. 238-245 ◽  
Author(s):  
Zhuo-Wei Chen ◽  
Pierre Joli ◽  
Zhi-Qiang Feng ◽  
Mehdi Rahim ◽  
Nicolas Pirró ◽  
...  
Keyword(s):  
Finite Element ◽  
Pelvic Organ Prolapse ◽  
Finite Element Modeling ◽  
Female Patient ◽  
Pelvic Organ ◽  
Patient Specific ◽  
Element Modeling
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