Machine Learning-Based Prediction of Forming-Induced Defects in Flax Woven Fabrics Using 3D Scanning and Haralick Texture Features

Natural fiber-reinforced composites offer lightweight and sustainable alternatives for automotive and aerospace applications. However, similar to synthetic composite options, forming-induced defects such as wrinkles can reduce targeted performance. Predicting these defects is particularly challenging for natural fiber reinforcements due to the inherent variability in the fiber geometry and ensuing fabric properties. This study applies a machine learning approach to predict formability in flax woven fabrics (2×2 twill and biaxial non-crimp) using a glass fabric as a synthetic fabric reference. The fabrics’ shear, bending, tensile, and friction behaviors were experimentally characterized to capture forming-relevant mechanical properties. The fabrics were subsequently formed in single-, dual-, and triple-layer configurations over a square tool, followed by 3D scanning to quantify wrinkle distributions. Forming-induced surface deformations were transformed into grayscale maps, from which Haralick texture features were extracted. Combined with the fabric design parameters such as weave, orientation, number of layers, grammage, and thickness, the mechanical properties features were used to train linear regression models, reliably predicting select Haralick features, cross-validated using Monte Carlo simulations. Results showed that flax twill reinforcements exhibited the lowest formability, while the glass fabric formed smoothly, and biaxial non-woven fabric showed primarily localized folds. Increasing the fabric orientation from 0° to 45° improved forming performance for most woven reinforcements; but not the biaxial non-woven alternative. Linear regression models accurately predicted the defect severity via homogeneity (R² = 0.81) and dissimilarity (R² = 0.73) texture features, demonstrating that integrating texture-based image analysis with fabric parameters and mechanical properties provides a promising machine-learning-based framework for predicting surface quality upon forming of fabric-reinforced composites.

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Info:

Periodical:

Materials Science Forum (Volume 1182)

Pages:

59-71

DOI:

https://doi.org/10.4028/p-zhZXT4

Citation:

Cite this paper

Online since:

April 2026

Authors:

Olivia H. Margoto*, Mohammad Amin Batouei, Victor Yang, Yasmine Abdin, Abbas S. Milani

Keywords:

3D Scanning, Flax Woven Fabrics, Forming-Induced Defect Prediction, Haralick Features Extraction, Monte Carlo Cross-Validation, Prediction

Funder:

The publication of this article was funded by the University of British Columbia 10.13039/501100005247

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Creative Commons CC BY 4.0

Citation:

* - Corresponding Author

References

[1] W. Lee, M.K. Um, J.H. Byun, P. Boisse, J. Cao, Numerical study on thermo-stamping of woven fabric composites based on double-dome stretch forming, International Journal of Material Forming 3 (2010) 1217–1227.