Assessment of the viscoelastic mechanical properties of polycarbonate urethane for medical devices

Beckmann, Agnes; Heider, Yousef; Stoffel, Marcus; Markert, Bernd

doi:10.1016/j.jmbbm.2018.02.015

Cited by 10 publications

(4 citation statements)

References 30 publications

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“…27,29 Flexibility and Viscoelasticity The viscoelastic mechanical properties of medical grade polycarbonate urethane were assessed by Beckmann et al 2018. 30 Unfortunately, there is no comparative data reported between PCU and the human disc, meaning that PCU behaves viscoelastically but the degree to which this behavior resembles the natural viscoelasticity of human tissue remains open.…”

Section: Low Frictionmentioning

confidence: 99%

<p>The MOVE-C Cervical Artificial Disc – Design, Materials, Mechanical Safety</p>

Kienle¹,

Graf²,

Krais³

et al. 2020

MDER

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Purpose: There are various cervical disc prostheses on the market today. They can be subdivided into implants with a ball-and-socket design and implants with a flexible core, which is captured between the implant endplates and sealed using various sheaths. Implants with an articulating surface are mostly metal-on-metal or metal-on-UHMWPE designs and, thus, do not allow for axial damping. The aim of this study is to provide mechanical safety and performance data of the MOVE-C cervical disc prosthesis which combines both an articulating surface and a flexible core. Materials and Methods: MOVE-C consists of a cranial and caudal metal plate made of TiAl6V4. The cranial plate is TiNbN coated on its articulating surface. The caudal plate has a fixed polycarbonate-urethane (PCU) core. The TiNbN coating is meant to optimize the wear behavior of the titanium endplate, whereas the PCU core is meant to allow for a reversible axial deformation, a pre-defined neutral zone and a progressive load-deformation curve in all planes. Results: Various standard testing procedures (for example, ISO 18192-1 and ASTM F2364) and non-standard mechanical tests were carried out to prove the implant's mechanical safety. Due to the new implant design, wear and creep testing was deemed most important. The wear rate for the PCU was in maximum 1.54 mg per million cycles. This value was within the range of the UHMWPE wear rates reported for other cervical disc prostheses (0.53 to 2.59 mg/million cycles). Also in the creep-relaxation test, a qualitatively physiological behavior was shown with a certain amount of remaining deformation but no failure. Conclusion: The mechanical safety of the MOVE-C cervical disc prosthesis was shown to be comparable to other cervical disc prostheses. Since PCU wear particles were elsewhere shown to be less bioactive than cross-linked UHMWPE particles, wear-related failure in vivo may be less frequent compared to other prostheses. This, however, will have to be shown in further studies.

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Section: Low Frictionmentioning

confidence: 99%

<p>The MOVE-C Cervical Artificial Disc – Design, Materials, Mechanical Safety</p>

Kienle¹,

Graf²,

Krais³

et al. 2020

MDER

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“…In conventional approaches in the mechanics of materials, constitutive laws as, for example, the relations among the state variables, the strain, and the stress tensor or that related to fluid retention behavior description, are proposed in form of mathematical expressions, which are based on phenomenological observations or experimental data 12,18,22–28 . These expressions can be subjected to hard constraints such as balance equations or thermodynamics restrictions 29–33 .…”

Section: Introductionmentioning

confidence: 99%

An offline multi‐scale unsaturated poromechanics model enabled by self‐designed/self‐improved neural networks

Heider

Suh

Sun

2021

Num Anal Meth Geomechanics

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Supervised machine learning via artificial neural network (ANN) has gained significant popularity for many geomechanics applications that involves multi‐phase flow and poromechanics. For unsaturated poromechanics problems, the multi‐physics nature and the complexity of the hydraulic laws make it difficult to design the optimal setup, architecture, and hyper‐parameters of the deep neural networks. This paper presents a meta‐modeling approach that utilizes deep reinforcement learning (DRL) to automatically discover optimal neural network settings that maximize a pre‐defined performance metric for the machine learning constitutive laws. This meta‐modeling framework is cast as a Markov Decision Process (MDP) with well‐defined states (subsets of states representing the proposed neural network (NN) settings), actions, and rewards. Following the selection rules, the artificial intelligence (AI) agent, represented in DRL via NN, self‐learns from taking a sequence of actions and receiving feedback signals (rewards) within the selection environment. By utilizing the Monte Carlo Tree Search (MCTS) to update the policy/value networks, the AI agent replaces the human modeler to handle the otherwise time‐consuming trial‐and‐error process that leads to the optimized choices of setup from a high‐dimensional parametric space. This approach is applied to generate two key constitutive laws for the unsaturated poromechanics problems: (1) the path‐dependent retention curve with distinctive wetting and drying paths. (2) The flow in the micropores, governed by an anisotropic permeability tensor. Numerical experiments have shown that the resultant ML‐generated material models can be integrated into a finite element (FE) solver to solve initial‐boundary‐value problems as replacements of the hand‐craft constitutive laws.

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“…The dynamic behavior of a PDSS is obtained by either incorporating a rubber-like material, e.g., silicone or polycarbonate urethane (PCU), or by reducing the structural stiffness, e.g., by using mechanical springs. Numerous spinal implants incorporate PCU for its mechanical properties and biocompatibility (Beckmann et al, 2018;St. John, 2014).…”

Section: Introductionmentioning

confidence: 99%

“…They found that the PCU materials reached the water absorption equilibrium after two weeks of soaking time in a saline bath. Beckmann et al (2018) performed material tests on Bionate® II 80A and 90A under physiological conditions to calibrate nonlinear viscoelastic finite element material models. Although the testing at physiological conditions promise a higher accuracy regarding the implant's stiffness, tests conducted in a closed volume implicate several difficulties.…”

Section: Introductionmentioning

confidence: 99%

Biomechanical testing of a polycarbonate-urethane-based dynamic instrumentation system under physiological conditions

Beckmann

Herren

Nicolini

et al. 2019

Clinical Biomechanics

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Assessment of the viscoelastic mechanical properties of polycarbonate urethane for medical devices

Cited by 10 publications

References 30 publications

<p>The MOVE-C Cervical Artificial Disc – Design, Materials, Mechanical Safety</p>

<p>The MOVE-C Cervical Artificial Disc – Design, Materials, Mechanical Safety</p>

An offline multi‐scale unsaturated poromechanics model enabled by self‐designed/self‐improved neural networks

Biomechanical testing of a polycarbonate-urethane-based dynamic instrumentation system under physiological conditions

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