Quantification of hyperelastic material parameters for a 3D-Printed thermoplastic elastomer with different infill percentages

3D printable, flexible, and conductive composites are prepared by incorporating a thermoplastic elastomer and electrically conductive carbon fillers. The advantageous printability, workability, chemical resistance, electrical conductivity, and biocompatibility components allowed for an enabling of 3D-printed electronics, electromagnetic interference (EMI) shielding, static elimination, and biomedical sensors. Carbon-infused thermoplastic polyurethane (C/TPU) composites have been demonstrated to possess right-strained sensing abilities and are the candidate in fields such as smart textiles and biomedical sensing. Flexible and conductive composites were prepared by a mechanical blending of biocompatible TPU and carbons. 3D structures that exhibit mechanical flexibility and electric conductivity were successfully printed. Three different types of C/TPU composites, carbon nanotube (CNT), carbon black (CCB), and graphite (G) were prepared with differentiating sizes and composition of filaments. The conductivity of TPU/CNT and TPU/CCB composite filaments increased rapidly when the loading amount of carbon fillers exceeded the filtration threshold of 8%–10% weight. Biocompatible G did not form a conductive pathway in the TPU; resistance to indentation deformation of the TPU matrix was maintained by weight by 40%. Adding a carbon material to the TPU improved the mechanical properties of the composites, and carbon fillers could improve electrical conductivity without losing biocompatibility. For the practical use of the manufactured filaments, optimal printing parameters were determined, and an FDM printing condition was adjusted. Through this process, a variety of soft 3D-printed C/TPU structures exhibiting flexible and robust features were built and tested to investigate the performance of the possible application of 3D-printed electronics and medical scaffolds.

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A Novel Constitutive Parameters Identification Procedure for Hyperelastic Skeletal Muscles Using Two-Way Neural Networks

International Journal of Computational Methods ◽

10.1142/s0219876221500602 ◽

2021 ◽

pp. 2150060

Author(s):

Yang Li ◽

Jianbing Sang ◽

Xinyu Wei ◽

Zijian Wan ◽

G. R. Liu

Keyword(s):

Neural Networks ◽

Skeletal Muscles ◽

Work Load ◽

Hyperelastic Material ◽

Load Prediction ◽

Material Parameters ◽

Penalty Factor ◽

Comparison Results ◽

Predicted Values ◽

Constitutive Parameters

Muscle soreness can occur after working beyond the habitual load, especially for people engaged in high-intensity work load. Prediction of hyperelastic material parameters is essentially an inverse process, which possesses challenges. This work presents a novel procedure that combines nonlinear finite element method (FEM), two-way neural networks (NNs) together with experiments, to predict the hyperelastic material parameters of skeletal muscles. FEM models are first established to simulate nonlinear deformation of skeletal muscles subject to compressions. A dataset of nonlinear relationship between nominal stress and principal stretch of skeletal muscles is created using our FEM models. The dataset is then used to establish two-way NNs, in which a forward NN is trained and it is in turn used to train the inverse NN. The inverse NN is used to predict the hyperelastic material parameters of skeletal muscles. Finally, experiments are carried out using fresh skeletal muscles to validate the predictions in great detail. In order to examine the accuracy of the two-way NNs predicted values against the experimental ones, a decision coefficient [Formula: see text] with penalty factor is introduced to evaluate the performance. Studies have also been conducted to compare the present two-way NNs approach with the other existing methods, including the directly (one-way) inverse problem NN, and improved niche genetic algorithm (INGA). The comparison results show that two-way NNs model is an accurate approach to identify the hyperelastic parameters of skeletal muscles. The present two-way NNs method can be further expanded to the predictions of constitutive parameters of other type of nonlinear materials.

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Preparation, morphology and superior performances of biobased thermoplastic elastomer by in situ dynamical vulcanization for 3D-printed materials

Polymer ◽

10.1016/j.polymer.2016.11.045 ◽

2017 ◽

Vol 108 ◽

pp. 11-20 ◽

Cited By ~ 23

Author(s):

Xiaoran Hu ◽

Hailan Kang ◽

Yan Li ◽

Yiting Geng ◽

Runguo Wang ◽

...

Keyword(s):

Thermoplastic Elastomer ◽

3D Printed ◽

Printed Materials

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Can hyperelastic material parameters be uniquely determined from indentation experiments?

RSC Advances ◽

10.1039/c6ra15747e ◽

2016 ◽

Vol 6 (85) ◽

pp. 81958-81964 ◽

Cited By ~ 7

Author(s):

Yihui Pan ◽

Yuexing Zhan ◽

Huanyun Ji ◽

Xinrui Niu ◽

Zheng Zhong

Keyword(s):

Hyperelastic Material ◽

Simple Criterion ◽

Material Parameters

Uniqueness of hyperelastic parameters depends on a simple criterion: whether dimensionless material parameters are coupled with indentation displacement.

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3D printed phantom to mimic dynamic softening of cervix during pre-labour

10.32920/ryerson.14655213 ◽

2021 ◽

Author(s):

Jung S. Kim

Keyword(s):

Maximum Stress ◽

Thermoplastic Elastomer ◽

True Stress ◽

Spring Constant ◽

Labour Induction ◽

Training Models ◽

Dynamic Softening ◽

3D Printed ◽

Minimum Stress ◽

Future Work

It is thought that through the development of more realistic training models for midwives and obstetricians it may be possible to reduce the overuse of labour induction. To this end we demonstrate a method for creating pneumatically-controlled phantom cervixes using thermoplastic elastomer, filled with a granular material. The maximum spring constant of the phantom cervix was measured to be 10.5 N/m at -20 kPa deflated air (vacuum) and the minimum spring constant measured was 5.3 N/m at 20 kPa inflated air. The true stress measured on these elastomeric phantom cervixes indicated a maximum stress of 133 kPa and a minimum stress of 94 kPa at 0.15 strain. Discrimination and threshold tests demonstrated that people can distinguish between the hard and soft states of the phantom. Future work will focus on increasing the softness of these devices.

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Robustly printable freeform thermal metamaterials

Nature Communications ◽

10.1038/s41467-021-27543-7 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Wei Sha ◽

Mi Xiao ◽

Jinhao Zhang ◽

Xuecheng Ren ◽

Zhan Zhu ◽

...

Keyword(s):

A Priori ◽

Transformation Optics ◽

A Priori Knowledge ◽

Flexible Design ◽

Material Parameters ◽

Fast Prototyping ◽

Complex Shapes ◽

Design Paradigm ◽

Background Temperature ◽

3D Printed

AbstractThermal metamaterials have exhibited great potential on manipulating, controlling and processing the flow of heat, and enabled many promising thermal metadevices, including thermal concentrator, rotator, cloak, etc. However, three long-standing challenges remain formidable, i.e., transformation optics-induced anisotropic material parameters, the limited shape adaptability of experimental thermal metadevices, and a priori knowledge of background temperatures and thermal functionalities. Here, we present robustly printable freeform thermal metamaterials to address these long-standing difficulties. This recipe, taking the local thermal conductivity tensors as the input, resorts to topology optimization for the freeform designs of topological functional cells (TFCs), and then directly assembles and prints them. Three freeform thermal metadevices (concentrator, rotator, and cloak) are specifically designed and 3D-printed, and their omnidirectional concentrating, rotating, and cloaking functionalities are demonstrated both numerically and experimentally. Our study paves a powerful and flexible design paradigm toward advanced thermal metamaterials with complex shapes, omnidirectional functionality, background temperature independence, and fast-prototyping capability.

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Validation of the Hyperelastic Material Parameters of Healthy Human Brain Arteries and Cerebral Saccular Aneurysms

IFMBE Proceedings - 5th European Conference of the International Federation for Medical and Biological Engineering ◽

10.1007/978-3-642-23508-5_228 ◽

2011 ◽

pp. 876-879

Author(s):

B. K. Tóth ◽

I. Bojtár

Keyword(s):

Human Brain ◽

Hyperelastic Material ◽

Healthy Human ◽

Material Parameters ◽

Brain Arteries ◽

Saccular Aneurysms

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The Nonlinear Material Properties of Liver Tissue Determined From No-Slip Uniaxial Compression Experiments

Journal of Biomechanical Engineering ◽

10.1115/1.2720928 ◽

2006 ◽

Vol 129 (3) ◽

pp. 450-456 ◽

Cited By ~ 52

Author(s):

Esra Roan ◽

Kumar Vemaganti

Keyword(s):

Soft Tissue ◽

Uniaxial Compression ◽

Material Properties ◽

Liver Tissue ◽

Mechanical Response ◽

Strain Energy Function ◽

Bovine Liver ◽

Hyperelastic Material ◽

Nonlinear Material ◽

Material Parameters

The mechanical response of soft tissue is commonly characterized from unconfined uniaxial compression experiments on cylindrical samples. However, friction between the sample and the compression platens is inevitable and hard to quantify. One alternative is to adhere the sample to the platens, which leads to a known no-slip boundary condition, but the resulting nonuniform state of stress in the sample makes it difficult to determine its material parameters. This paper presents an approach to extract the nonlinear material properties of soft tissue (such as liver) directly from no-slip experiments using a set of computationally determined correction factors. We assume that liver tissue is an isotropic, incompressible hyperelastic material characterized by the exponential form of strain energy function. The proposed approach is applied to data from experiments on bovine liver tissue. Results show that the apparent material properties, i.e., those determined from no-slip experiments ignoring the no-slip conditions, can differ from the true material properties by as much as 50% for the exponential material model. The proposed correction approach allows one to determine the true material parameters directly from no-slip experiments and can be easily extended to other forms of hyperelastic material models.

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