Visco-hyperelastic model with damage for simulating cyclic thermoplastic elastomers behavior applied to an industrial component

In this work a nonlinear phenomenological visco-hyperelastic model including damage consideration is developed to simulate the behavior of Santoprene 101-73 material. This type of elastomeric material is widely used in the automotive and aeronautic sectors, as it has multiple advantages. However, there are still challenges in properly analyzing the mechanical phenomena that these materials exhibit. To simulate this kind of material a lot of theories have been exposed, but none of them have been endorsed unanimously. In this paper, a new model is presented based on the literature, and on experimental data. The test samples were extracted from an air intake duct component of an automotive engine. Inelastic phenomena such as hyperelasticity, viscoelasticity and damage are considered singularly in this model, thus modifying and improving some relevant models found in the literature. Optimization algorithms were used to find out the model parameter values that lead to the best fit of the experimental curves from the tests. An adequate fitting was obtained for the experimental results of a cyclic uniaxial loading of Santoprene 101-73

Finite element model set-up of colorectal tissue for analyzing surgical scenarios

Finite Element Analysis (FEA) has gained an extensive application in the medical field, such as soft tissues simulations. In particular, colorectal simulations can be used to understand the interaction with the surrounding tissues, or with instruments used in surgical procedures. Although several works have been introduced considering small displacements, as a result of the forces exerted on adjacent tissues, FEA applied to colorectal surgical scenarios is still a challenge. Therefore, this work aims to provide a sensitivity analysis on three geometric models, taking in mind different bioengineering tasks. In this way, a set of simulations has been performed using three mechanical models named Linear Elastic, Hyper-Elastic with a Mooney-Rivlin material model, and Hyper-Elastic with a YEOH material model

Experimental, Analytical, and Numerical Evaluation of the Mechanical Properties of the Brain Tissue

A true understanding of the mechanisms behind most of the brain diseases is still out of reach. For several years, the interest of scientists has been focused on the genetic and biological causes, however, recent studies unraveled the importance of the biomechanics of the brain growth, folding, impact resistance, and deformation on its pathological conditions. While, a wide range of different methods have been used for characterization of the mechanical properties of the brain at the tissue level, the obtained results from different studies are extremely scattered and sometimes in contrast to one another. Since the brain tissue is extremely soft, its mechanical properties are quite a challenge to be obtained. In this study, the accurate analysis of the mechanical heterogeneity of the brain tissue is performed through dynamic and pseudo-static indentation techniques to evaluate the viscoelastic response of the brain and presenting its anisotropy, inhomogeneity, and rate dependence. In addition, this research provides a detailed reference for modeling the nonlinear mechanical behavior of soft tissues, in general, and the brain tissue, in particular, with addressing important considerations for mechanical modeling in uniaxial loading conditions. With thoroughly presenting the physical basis of the modeling procedure, it is shown that if such considerations are neglected, a considerable inaccurate evaluation of the mechanical properties of the tissue can be expected, although the results might mathematically be correct. Moreover, a new model is developed for the mechanical behavior of the brain tissue that addresses the tension-compression asymmetry with taking into account the compressibility of the tissue in different loading conditions. This model is implemented by utilizing a combined analytical and numerical scheme. The results of this research could be used as input variables for computer simulations of the brain tissue in studying the traumatic brain injury, malformation of the brain folds, and other pathobiological conditions associated with the mechanical behavior of the brain

In vivo measurement of human brain elasticity using a light aspiration device

The brain deformation that occurs during neurosurgery is a serious issue impacting the patient "safety" as well as the invasiveness of the brain surgery. Model-driven compensation is a realistic and efficient solution to solve this problem. However, a vital issue is the lack of reliable and easily obtainable patient-specific mechanical characteristics of the brain which, according to clinicians' experience, can vary considerably. We designed an aspiration device that is able to meet the very rigorous sterilization and handling process imposed during surgery, and especially neurosurgery. The device, which has no electronic component, is simple, light and can be considered as an ancillary instrument. The deformation of the aspirated tissue is imaged via a mirror using an external camera. This paper describes the experimental setup as well as its use during a specific neurosurgery. The experimental data was used to calibrate a continuous model. We show that we were able to extract an in vivo constitutive law of the brain elasticity: thus for the first time, measurements are carried out per-operatively on the patient, just before the resection of the brain parenchyma. This paper discloses the results of a difficult experiment and provide for the first time in-vivo data on human brain elasticity. The results point out the softness as well as the highly non-linear behavior of the brain tissue.Comment: Medical Image Analysis (2009) accept\'

Recursive Least Squares Filtering Algorithms for On-Line Viscoelastic Characterization of Biosamples

The mechanical characterization of biological samples is a fundamental issue in biology and related fields, such as tissue and cell mechanics, regenerative medicine and diagnosis of diseases. In this paper, a novel approach for the identification of the stiffness and damping coefficients of biosamples is introduced. According to the proposed method, a MEMS-based microgripper in operational condition is used as a measurement tool. The mechanical model describing the dynamics of the gripper-sample system considers the pseudo-rigid body model for the microgripper, and the Kelvin–Voigt constitutive law of viscoelasticity for the sample. Then, two algorithms based on recursive least square (RLS) methods are implemented for the estimation of the mechanical coefficients, that are the forgetting factor based RLS and the normalised gradient based RLS algorithms. Numerical simulations are performed to verify the effectiveness of the proposed approach. Results confirm the feasibility of the method that enables the ability to perform simultaneously two tasks: sample manipulation and parameters identification