Finite element and machine learning-assisted analysis of fiber–matrix interphase in single-fiber pull-out tests

Experimental and simulative single-fiber pull-outs were carried out to determine the interphase parameters and to investigate the damage process in the pull-out. For each experiment, a related simulation was performed, not only taking into account the corresponding fiber diameter and the individual embedded length, but also the stiffness gradient in the fiber–matrix interphase. Using a generalized traction separation law it was shown that interphase damage is probably a process of plastic material deformation. Additionally, the determination of mechanical interphase parameters was achieved through the utilization of supervised machine learning algorithms.

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Publication Details

Journal
Composites Part B Engineering
Published
2025-06-22
DOI
https://doi.org/10.1016/j.compositesb.2025.112702
Citations
4
Primary Topic
Advanced Fiber Optic Sensors
Type
article
Field-Weighted Citation Impact
2.92
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article

Finite element and machine learning-assisted analysis of fiber–matrix interphase in single-fiber pull-out tests

Christina Scheffler, Karl Roetsch, Lars Bittrich, Markus Stommel et al.
4 citations
Composites Part B Engineering
Advanced Fiber Optic Sensors
2.92
article

Finite element and machine learning-assisted analysis of fiber–matrix interphase in single-fiber pull-out tests

Christina Scheffler, Karl Roetsch, Lars Bittrich, Markus Stommel, Axel Spickenheuer, Andrea Bercini
article en
4 citations

Abstract

Experimental and simulative single-fiber pull-outs were carried out to determine the interphase parameters and to investigate the damage process in the pull-out. For each experiment, a related simulation was performed, not only taking into account the corresponding fiber diameter and the individual embedded length, but also the stiffness gradient in the fiber–matrix interphase. Using a generalized traction separation law it was shown that interphase damage is probably a process of plastic material deformation. Additionally, the determination of mechanical interphase parameters was achieved through the utilization of supervised machine learning algorithms.

Composites Part B EngineeringVol. 305
Leibniz Institute for Solid State and Materials Research (DE), Leibniz Institute of Polymer Research (DE), Hochschule für Technik und Wirtschaft Dresden – University of Applied Sciences (DE), Technische Universität Dresden (DE)
Openalex Percentile: Top 9%
Advanced Fiber Optic Sensors
2.92
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Finite element and machine learning-assisted analysis of fiber–matrix interphase in single-fiber pull-out tests — Christina Scheffler, Karl Roetsch, et al. · Composites Part B Engineering (2025) | TGRS Research Map | TGRS