A mesoscale model of non‐crimp fabrics based on a deep learning framework. Issue 1 (24th March 2023)

• Record Type: Journal Article
• Authors: Zhou, Shuang; Hillgärtner, Markus; Abdusalamov, Rasul; X. Duong, Thang; Itskov, Mikhail
• Other Names: Böhm Ch. guestEditor.; Mang K. guestEditor.; Markert B. guestEditor.; Reese S. guestEditor.; Schmidtchen M. guestEditor.; Waimann J. guestEditor.; Kaliske M. editorInChief.
• Is Part Of: Proceedings in applied mathematics and mechanics. Volume 22:Issue 1(2023)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2023-03-24
• Subjects: Applied mathematics -- Periodicals; Engineering mathematics -- Periodicals; Mathematical physics -- Periodicals; 519
• DOI: 10.1002/pamm.202200298
• Languages: English
• ISSNs: 1617-7061

Abstract

Abstract: Non‐crimp fabrics (NCFs) are a type of textile characterized by straight and long fibers. Due to their lightweight structures, they are widely used for fiber‐reinforced composites in automotive, aerospace, and other fields. NCFs consist of several differently oriented layers of unidirectional fibers, stacked on top of each other, and stitched together by stitching yarns. Due to this structure, constitutive modeling of the anisotropic properties of NCFs is still challenging requiring an accurate description, especially for multi‐axial loading. In this work, a deep learning framework constructed by artificial neural networks (ANNs) is presented that describes the constitutive behavior of NCFs. Such a framework is able to learn not only from the provided load‐displacement relation by experimental data of existing materials, but also from additional relevant information, such as geometric characteristics. The framework allows predicting the constitutive behavior of new fabric materials, whose experimental data are unavailable yet. This contribution aims to establish a constitutive model of dry NCFs based on a deep learning framework trained from virtual experimental data provided by finite element simulations of representative volume elements (RVEs) at the mesoscale. NCFs with only two families of fibers perpendicular to each other and stitched together are considered. The proposed constitutive model is capable of predicting the in‐plane material response of NCFs under arbitrary multi‐axial loading conditions. To this end, a procedure of training ANN for reaction forces responding to displacements of RVEs is investigated. In order to guarantee the predictive capability for various NCFs under multi‐axial loading conditions, the artificial neural network is used to learn from simulations of the same RVE provided by changing its fiber bundle properties (tensile moduli and shear moduli) and loading directions at the mesoscale. Finally, the proposed constitutive model is verified by comparing the predictive results with reference data sets for various materials in arbitrary axial loading.