Inverse finite element characterization of soft tissues using genetic algorithm

Abstract

Karthikeyan B, Chawla A, Mukherjee S Department of Mechanical Engineering, Indian Institute of Technology, New Delhi, India Human body finite element (FE) models for use in impact simulations require soft tissue characterization at high strain rates. The objective of the current work is to extract viscoelastic properties of passive muscle tissues at high strain rates and study their rate dependency. A procedure to identify the dynamic properties of passive muscle tissue under impact has been proposed using isolated-tissue experiments, FE simulations and Genetic Algorithm (GA) based optimization. Data from nineteen impact tests on unconfined isolated human muscles for strain rate ranging from 132/s to 262/s were used [1]. Tissues were compressed up to approximately 50 % strain and the force-time response was recorded. FE simulations of these impact tests have been performed in the present study by modeling the muscle as linear viscoelastic. RMS of the deviation between the experimental and FE force data, sampled at 10 kHz, was then minimized to predict the material parameters, bulk modulus, short-term shear modulus and long-term shear modulus. This parameter identification process was automated using PAM-CRASH