Abstract Finding the stiffness map of biological tissues is of great importance in evaluating their healthy or pathological conditions. However, due to the heterogeneity and anisotropy of biological fibrous tissues, this task presents challenges and significant uncertainty when characterized only by single-mode loading experiments. In this study, we propose a new theoretical framework to map the stiffness landscape of fibrous tissues, specifically focusing on brain white matter tissue. Initially, a finite element (FE) model of the fibrous tissue was subjected to six loading cases, and their corresponding stress–strain curves were characterized. By employing multiobjective optimization, the material constants of an equivalent anisotropic material model were inversely extracted to best fit all six loading modes simultaneously. Subsequently, large-scale FE simulations were conducted, incorporating various fiber volume fractions and orientations, to train a convolutional neural network capable of predicting the equivalent anisotropic material properties solely based on the fibrous architecture of any given tissue. The proposed method, leveraging brain fiber tractography, was applied to a localized volume of white matter, demonstrating its effectiveness in precisely mapping the anisotropic behavior of fibrous tissue. In the long-term, the proposed method may find applications in traumatic brain injury, brain folding studies, and neurodegenerative diseases, where accurately capturing the material behavior of the tissue is crucial for simulations and experiments.
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Validation of a finite element method for simulation of components produced by continuous carbon fiber reinforced additive manufacturing
n this research, a method is examined by which the behavior of continuous carbon fiber rein-forced additive manufacturing may be simulated using Finite Element Analysis. This technique is used in a simulated tensile test experiment in which the findings are compared to results determined from theoretical calculations according to the Rule of Mixtures method and from existing mechanical testing results. Four different fiber reinforcement configurations are examined with fiber volume fractions ranging from 4% to 32%. It was found that for fiber vol-ume fractions of 11%, the simulation results closely match those predicted theoretically by the Rule of Mixtures as well as the mechanical testing results published in existing research. Lower fiber volume fractions near 4% yield less accurate results, with a 20% error due to the fact that the anisotropic behavior of the polymer matrix is the dominant material trait. Simulation of higher volume fractions near 32% closely approximate theoretical predictions, however neither the theoretical results nor the simulation results accurately reflect real world mechanical testing, indicating that nonideal condition factors such as the effect of micro-voids between the start and end of the fiber reinforcements play a significant role in the overall strength of the material. Thus, for fiber volume fractions near 11%, this simulation method can accurately be used to predict the behavior of end-use components, but more study must be done to increase simulation accuracy in low and high fiber volume fractions.
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- Award ID(s):
- 1950207
- PAR ID:
- 10326049
- Editor(s):
- Taylor & Francis
- Date Published:
- Journal Name:
- International journal for computational methods in engineering science and mechanics
- ISSN:
- 1550-2287
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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