Machine learning based inverse modeling of full-field strain distribution for mechanical characterization of a linear elastic and heterogeneous membrane

2021 ◽  
pp. 104134
Author(s):  
Yuan Zhang ◽  
Lin Guo ◽  
Clement J.A. Brousse ◽  
Chung-Hao Lee ◽  
Aurelie Azoug ◽  
...  
Keyword(s):  
Machine Learning ◽  
Inverse Modeling ◽  
Strain Distribution ◽  
Mechanical Characterization ◽  
Field Strain ◽  
Full Field ◽  
Linear Elastic ◽  
Heterogeneous Membrane

An Innovative Tool to Measure Human Skin Strain Distribution in Vivo using Motion Capture and Delaunay Mesh

2012 ◽  
Vol 28 (2) ◽  
pp. 309-317 ◽  
Author(s):  
J. Mahmud ◽  
S. L. Evans ◽  
C. A. Holt
Keyword(s):  
Finite Elements ◽  
Human Skin ◽  
Motion Capture ◽  
Strain Distribution ◽  
Field Strain ◽  
Full Field ◽  
Skin Deformation ◽  
Deformation Mechanics ◽  
Delaunay Mesh

AbstractSkin has a complex structure and its deformation mechanics is still not well defined. In the study of skin biomechanics, the stretch ratio, λ, is an important property, which is determined using strain data. This paper attempts to develop a novel tool by integrating experimental-numerical approach to measure full-field strain distribution of human skin in vivo. Skin deformation in vivo was measured using motion capture system, (which is not a full-field measuring tool) and then by constructing finite elements, its full-field strain contour is produced. The experimental procedure starts by attaching a set of reflective markers onto the skin at the forearm of healthy volunteers. Skin deformation is induced by pulling a nylon filament attached with a loading tab. Three infrared cameras are used to capture the movement of markers during load application. QTM (Qualisys, Sweden) software is used to track markers trajectories and generate data consisting of 3-dimensional markers coordinate. The initial capture is set as the reference marker positions (undeformed skin) and the subsequent images represent the deformed skin relative to the initial. Representing markers as nodes, finite elements are constructed by adjoining three adjacent markers using Delaunay mesh. Strains were deduced from the strain displacement matrix and measured for three subjects at three loading directions. The results are in fair agreement with those obtained by others. The method and output provide a useful addition to understanding skin deformation.

scholarly journals An inverse method for mechanical characterization of heterogeneous diseased arteries using intravascular imaging

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Bharath Narayanan ◽  
Max L. Olender ◽  
David Marlevi ◽  
Elazer R. Edelman ◽  
Farhad R. Nezami
Keyword(s):  
In Silico ◽  
Mechanical Characterization ◽  
Imaging Data ◽  
New Approach ◽  
Parameter Recovery ◽  
Material Parameters ◽  
Linear Elastic ◽  
Novel Method

AbstractThe increasing prevalence of finite element (FE) simulations in the study of atherosclerosis has spawned numerous inverse FE methods for the mechanical characterization of diseased tissue in vivo. Current approaches are however limited to either homogenized or simplified material representations. This paper presents a novel method to account for tissue heterogeneity and material nonlinearity in the recovery of constitutive behavior using imaging data acquired at differing intravascular pressures by incorporating interfaces between various intra-plaque tissue types into the objective function definition. Method verification was performed in silico by recovering assigned material parameters from a pair of vessel geometries: one derived from coronary optical coherence tomography (OCT); one generated from in silico-based simulation. In repeated tests, the method consistently recovered 4 linear elastic (0.1 ± 0.1% error) and 8 nonlinear hyperelastic (3.3 ± 3.0% error) material parameters. Method robustness was also highlighted in noise sensitivity analysis, where linear elastic parameters were recovered with average errors of 1.3 ± 1.6% and 8.3 ± 10.5%, at 5% and 20% noise, respectively. Reproducibility was substantiated through the recovery of 9 material parameters in two more models, with mean errors of 3.0 ± 4.7%. The results highlight the potential of this new approach, enabling high-fidelity material parameter recovery for use in complex cardiovascular computational studies.

Mechanical Characterization of Embedded Serpentine Conductors in Wearable Electronics

2020 ◽  
Vol 142 (2) ◽  
Author(s):  
Chong Ye ◽  
Charles I. Ume ◽  
Suresh K. Sitaraman
Keyword(s):  
Experimental Data ◽  
Digital Image Correlation ◽  
Strain Distribution ◽  
Mechanical Characterization ◽  
Neutral Line ◽  
Image Correlation ◽  
Wearable Electronics ◽  
Two Dimensional ◽  
Supporting Materials

Abstract Wearable electronics undergo stretching, flexing, bending, and twisting during the process of being put on and while being worn. In addition, wearable textile electronics also need to survive under cyclic washing. During such processes, it is necessary to ensure that the electronics as well as the conductors and various other supporting materials remain reliable. In this work, mechanical characterization of various materials in a commercially available smart shirt is presented. The serpentine conductor used in the smart shirt has been carefully examined to understand the strain distribution at various locations under stretching. Both analytical formulations and numerical simulations have been carried out to determine the strain distribution in the serpentine structure, and the results from the simulations have been compared against experimental data obtained through two-dimensional digital image correlation (2D DIC). Various design configurations of the semicircular serpentine structure have been studied in this work, and a relationship between width and the neutral line radius of the semicircular serpentine structure has been obtained to reduce maximum strains in the serpentine structure under stretching.

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