Deep Learning-based Mechanical Characterization of Synthetically Generated Plant Fibers under Transverse Compression

Nikolaos Kegkeroglou; Bilal Sabillah; Myrto Inglezou; Vincent Placet; Céndric Clevy; Antonios Porichis

cs.CVOct 12, 2026

Deep Learning-based Mechanical Characterization of Synthetically Generated Plant Fibers under Transverse Compression

Authors: Nikolaos Kegkeroglou, Bilal Sabillah, Myrto Inglezou, Vincent Placet, Céndric Clevy, Antonios Porichis

Organizations: Tech Hive Labs, Iliados 7, Halandri, 152 32, Greece. · Université Marie et Louis Pasteur, SUPMICROTECH, CNRS, Institut FEMTO-ST, Besançon, F-25000, France. · AI Innovation Centre, University of Essex, Little Abington, Cambridge, CB21 6AL, UK.

Abstract

Plant fibers are increasingly used as sustainable reinforcements in composites, but the intrinsic variability of their microstructure causes substantial scatter in transverse mechanical behavior, complicating composite design. Characterizing this behavior relies on finite element (FE) simulations requiring several minutes of high-performance computing per fiber, making high-throughput characterization across realistic fiber populations impractical. In this study, we propose a deep learning framework replacing this FE bottleneck with a fast pipeline. It includes two coupled recursive U-Nets predicting the step-by-step evolution of fiber deformation and internal stress fields under transverse compression, followed by a regression CNN estimating the transverse Young's modulus from the final stress field. Unlike prior approaches predicting stress fields for fixed configurations or idealized geometries, our framework recursively resolves the full temporal stress evolution for synthetic fibers derived from real hemp cross-sections, with or without a lumen. Trained on 1,000 synthetic fiber geometries with FE ground truth, it reproduces the stress field sequence with a Structural Similarity Index Measure (SSIM) of $0.9574 \pm 0.0074$ and a Root Mean Squared Error (RMSE) of $0.0345 \pm 0.0079$ . It estimates transverse Young's modulus with an $R^2$ of $0.938 \pm 0.024$ , a Mean Absolute Error (MAE) of $24.31 \pm 1.23$ MPa, and a Mean Absolute Percentage Error (MAPE) of $5.25 \pm 0.91\%$ . Inference requires under one second per fiber versus five minutes per FE simulation, drastically reducing computational cost. This offers a fast, reliable tool for mechanical characterization of synthetic plant fibers, with promising applications in designing natural fiber-reinforced composites.

Figures & tables

Figure 1: Extracted and smoothed hemp fiber cross-sectional geometries used for finite element analysis, showing examples of fibers with and without lumen. Reprinted from Composites Part A: Applied Science and Manufacturing, Vol 175, Jason Govilas, Cédric Clévy, Johnny Beaugrand, Vincent Placet, Investigating the influence of plant fiber geometry on apparent transverse elastic properties through finite element analysis, 107789, Copyright (2023), with permission from Elsevier Govilas et al. (2023) .

Figure 2: The progressive evolution of the internal stress field output from the FE transverse compression simulations at selected loading intervals.

Figure 3: The pre-processing steps applied to the FE output sequences.

Figure 4: Schematic representation of the deep learning pipeline. Starting from the initial fiber mask, the Deformation U-Net predicts the evolving fiber geometry step by step. At each step, the Stress U-Net predicts the corresponding physical stress field, conditioned on the predicted deformation. The final predicted stress field is then passed to the Modulus CNN, which estimates the transverse Young’s modulus ( $E_{T}$ ).

Hyperparameter	Regression	Image-to-Image
Input size	$(256,256)$	$(256,256)$
Number of layers	4	4
Number of initial filters	64	32
Convolution kernel	$3\times 3$	$3\times 3$
Pooling size	4	2
Bottleneck dimension	32	128

Table 1: Hyperparameters of the deep learning models

Panel of Figure 5 — Figure 5: The $E_{\mathrm{T}}$ distribution of the training-evaluation split with different experiment seeds.

Model	SSIM (0–1) $\uparrow$	RMSE (0–1) $\downarrow$
Stress U-Net (ours)	0.9574 $\pm$ 0.0074	0.0345 $\pm$ 0.0079
StressNet Nie et al. (2019)	0.9372 $\pm$ 0.0048	0.0438 $\pm$ 0.0022
StressNet-mod Shokrollahi et al. (2023) ; Sun et al. (2024)	0.9172 $\pm$ 0.0240	0.0479 $\pm$ 0.0094
U-Net-mod Bhaduri et al. (2022)	0.9170 $\pm$ 0.0218	0.0563 $\pm$ 0.0090
SCSNet Nie et al. (2019)	0.8076 $\pm$ 0.0290	0.0894 $\pm$ 0.0023

Table 2: Comparative evaluation of stress field prediction models

Figure 6: Qualitative evaluation of the proposed framework for stress field prediction of individual synthetic plant fibers under transverse compression.

Model	R 2 (0–1) $\uparrow$	MAE (MPa) $\downarrow$	MAPE (%) $\downarrow$
Modulus CNN (ours)	0.938 $\pm$ 0.024	24.31 $\pm$ 1.23	5.25 $\pm$ 0.91
ResNet-34 He et al. (2015)	0.895 $\pm$ 0.059	30.48 $\pm$ 6.84	6.89 $\pm$ 1.70
AlexNet Krizhevsky et al. (2012)	0.881 $\pm$ 0.088	28.42 $\pm$ 3.91	6.67 $\pm$ 0.49
TI-CNN Wang et al. (2021)	0.837 $\pm$ 0.079	35.94 $\pm$ 7.07	8.10 $\pm$ 0.98
CNN-custom Raaghav et al. (2025)	0.754 $\pm$ 0.014	48.04 $\pm$ 8.89	10.49 $\pm$ 2.11

Table 3: Comparative evaluation of regression models for transverse Young’s modulus prediction

Group	$n$	MAPE (%)	Bias (%)	MAE (MPa)	R 2
Overall	70	$5.25\pm 0.91$	$-0.60\pm 1.70$	$24.31\pm 1.23$	$0.938\pm 0.024$
Low-modulus ( $E_{\mathrm{T}}\leq$ median)	35	$5.52\pm 2.14$	$+0.15\pm 2.26$	$19.28\pm 4.82$	$0.917\pm 0.033$
High-modulus ( $E_{\mathrm{T}}>$ median)	35	$5.04\pm 0.36$	$-1.66\pm 2.01$	$29.93\pm 4.38$	$0.595\pm 0.297$

Table 4: Prediction performance of the Modulus CNN, stratified by true modulus, averaged over 3 seeds (

\pm

std across seeds). Groups are split at the pooled median true

E_{\mathrm{T}}

n

is the number of held-out test samples per group, pooled over the 3 seeds.

Group	$n$	MAPE (%)	Bias (%)	MAE (MPa)	R 2
Overall	70	$5.25\pm 0.91$	$-0.60\pm 1.70$	$24.31\pm 1.23$	$0.938\pm 0.024$
No lumen	46	$5.11\pm 0.11$	$-0.59\pm 1.53$	$28.09\pm 2.40$	$0.797\pm 0.104$
With lumen	24	$5.24\pm 2.60$	$-0.88\pm 3.03$	$16.48\pm 6.61$	$0.930\pm 0.038$

Table 5: Prediction performance of the Modulus CNN, stratified by lumen presence, averaged over 3 seeds (

\pm

std across seeds).

n

is the number of held-out test samples per group, pooled over the 3 seeds.

Model	Target	SSIM (0–1) $\uparrow$	RMSE (0–1) $\downarrow$
Full	Deformation	0.9822 $\pm$ 0.0015	0.0761 $\pm$ 0.0037
Stride-1	Deformation	$0.9805\pm 0.0024$	$0.0820\pm 0.0053$
Full	Stress	0.9574 $\pm$ 0.0074	0.0345 $\pm$ 0.0079
Stride-1	Stress	$0.8923\pm 0.0083$	$0.0734\pm 0.0102$
No-def	Stress	$0.8235\pm 0.0290$	$0.0903\pm 0.0137$
No-rec	Stress	$0.5162\pm 0.0316$	$0.1333\pm 0.0014$

Table 6: Ablation study of the image-to-image model

Figure 7: Qualitative comparison of rollout predictions for stride values $\Delta=1$ and $\Delta=4$ across sequential evaluation steps.

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Short-fiber thermoplastic (SFT) composites are increasingly employed in lightweight aerospace and automotive structures owing to their favorable strength-to-weight ratio, high production rates, and recyclability. Unlike continuous-fiber systems, the mechanical response of SFTs is governed by mesoscale interactions among fiber orientation, spatial clustering, and manufacturing-induced porosity. These features exhibit significant spatial variability in manufactured components and influence stiffness, damage initiation, and nonlinear deformation. Although mesoscale finite element (FE) models can resolve such heterogeneity, their application to realistic three-dimensional microstructures remains computationally intractable. A data-driven surrogate framework is proposed to predict the mechanical behavior of additively manufactured, compression-molded (AM-CM) SFTs. Microstructures reconstructed from micro-computed tomography data were discretized into Voronoi-based cells representing distinct fiber-interaction neighborhoods. Each cell was homogenized via nonlinear FE simulations incorporating matrix damage, and the resulting stress-strain responses trained a hybrid Graph Neural Network-Long Short-Term Memory (GNN-LSTM) architecture encoding microstructural topology and history-dependent mechanical evolution. The surrogate accurately predicts stiffness and stress-strain behavior of unseen microstructures, achieving

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Abstract

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