Inverse methodology as applied to reconstruct local textile features from measured pressure field

One can compute the final deformation of a known geometry under specific boundary conditions using the constitutive laws of mechanics that describe their stress strain behavior. In such cases the initial geometry is known, and all operators mapping the deformation are defined on the reference domain. However, there are situations in which the final configuration of a deformation might be known but not the initial. The inverse formulation allows one to determine the initial geometry of a domain, given its final deformation state, the material behavior law and a set of boundary conditions. In the present work we propose a method to reconstruct the mesoscale geometry of a textile based on its mechanical response during compaction. To do so, stress boundary conditions are acquired by means of a pressure-sensitive film. By adopting an appropriate material law, the thickness and width information of the yarns are deduced from the pressure field experienced by the compacted textile. Unlike 3D scanning techniques such as mu-CT, the proposed method can be applied on any domain size, allowing long-range variability to be captured. To the best of the authors' knowledge, there are no previous works that use a pressure-sensitive film on a large domain to capture the input data for a shape reconstruction. This example application serves as a demonstration of a methodology which could be applied to other classes of materials. (C) 2021 Published by Elsevier Ltd on behalf of The editorial office of Journal of Materials Science & Technology.

Automated summary

The paper proposes a method to reconstruct the mesoscale geometry of a textile based on its mechanical response during compaction by using stress boundary conditions acquired through a pressure-sensitive film. By adopting an appropriate material law, the thickness and width information of the yarns can be deduced. This method can be applied on any domain size and capture long-range variability, providing a new approach for shape reconstruction.