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Prediction of the flexural properties of poplar wood based on microstructure through machine learning

基于微观结构和机器学习的杨木抗弯性能预测

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Abstract

Understanding the fracture mechanism and establishing predictive model for the flexural properties are critical issues in composite materials research. However, accurately developing the nonlinear microstructure-based model remains inherently challenging, particularly for poplar wood, a typically natural fiber-reinforced composite. In this study, the fracture analysis and the predictive model of the flexural properties were evaluated by combining a multi-layered structural analysis encompassing the cell wall ultrastructure, the cells morphology, and the tissue proportion. As a result, key microstructural drivers of flexural properties were identified. The flexural properties were positively correlated with crystallinity, wall-to-lumen ratio, fiber proportion, and negatively correlated with microfibril angle and vessel lumen diameter. Post-failure fracture morphology revealed the presence of “weak points”, where stress mainly concentrated at the vessel wall pits and interface between multi-layered cell walls. The principal component analysis (PCA) algorithm effectively condensed nine microstructural features into four interpretable principal components. Although multiple linear regression (PCA-ML) and support vector machine regression models (PCA-SVR) both had thoroughly predictive ability, the performance of the PCA-SVR model was comprehensively improved. Specifically, the coefficients of determination of PCA-SVR of modulus of elasticity and modulus of rupture were 0.831 and 0.799. This study provides a scientific foundation for the design of wood-based composites with tailored flexural behavior.

摘要

在复合材料领域中明确弯曲性能的失效机制并建立相应的预测模型是关键科学问题. 然而, 对于杨木这类天然纤维复合材料, 建立基于微观结构的非线性预测模型仍面临挑战. 本研究综合分析了细胞壁超微结构、 细胞形态及组织比量等微观结构对弯曲性能失效分析的影响, 并构建了弯曲性能的预测模型. 结果表明, 弯曲性能与结晶度、 壁腔比和纤维组织比量呈正相关, 与微纤丝角及导管直径呈负相关. 断裂面微观结构分析揭示了“弱相结构”的存在, 应力主要集中于导管壁上的纹孔及纤维细胞的细胞壁壁层界面. 主成分分析算法将九个结构因子降至四个可解释的主成分, 从而构建了线性回归模型(PCA-ML)与支持向量机回归模型(PCA-SVR). 尽管两类模型均具备较优的预测能力, 但PCA-SVR模型的整体性能更优. 其中, 弹性模量PCA-SVR模型与抗弯强度PCA-SVR模型的决定系数分别为0.831和0.799. 本文为设计具有可调控弯曲性能的木基复合材料奠定科学基础.

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Acknowledgements

This work was supported by the National Key Research and Development Program of China (Grant No. 2023YFD2200501) and the National Natural Science Foundation of China (Grant No. 32471799).

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Authors and Affiliations

  1. Research Institute of Wood Industry, Chinese Academy of Forestry, Beijing, 100091, China

    Jiaqi He  (贺嘉琪), Changdong Jia  (贾昌东) & Yurong Wang  (王玉荣)

  2. Experimental Center of Desert Forestry, Chinese Academy of Forestry, Dengkou, 015200, China

    Yingming Zhao  (赵英铭)

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  1. Jiaqi He  (贺嘉琪)
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  2. Changdong Jia  (贾昌东)
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  3. Yurong Wang  (王玉荣)
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Contributions

Author contributions Jiaqi He: Conceptualization, Formal analysis, Methodology, Software, Writing – original draft. Changdong Jia: Validation, Visualization, Writing – review & editing. Yurong Wang: Funding acquisition, Project administration, Supervision, Writing – review & editing. Yingming Zhao: Resources, Writing – review & editing.

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Correspondence to Yurong Wang  (王玉荣).

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He, J., Jia, C., Wang, Y. et al. Prediction of the flexural properties of poplar wood based on microstructure through machine learning. Acta Mech. Sin. 42, 651109 (2026). https://doi.org/10.1007/s10409-026-51109-x

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  • DOI: https://doi.org/10.1007/s10409-026-51109-x

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