Machine Learning Predictive Models for the Tensile Properties of Polyethylene and Polypropylene

Le-kang Zhang; Xiao-yu Sun; Jing-qing Li; Lun-yang Liu; Tao Liao; Ying Lu; Hong-fei Li; Yong-feng Men; Shi-chun Jiang

doi:10.11777/j.issn1000-3304.2026.26005

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Machine Learning Predictive Models for the Tensile Properties of Polyethylene and Polypropylene

更新时间：2026-03-25

- Machine Learning Predictive Models for the Tensile Properties of Polyethylene and Polypropylene
- Acta Polymerica Sinica Pages: 1-14(2026)
- 作者机构：
  
  1.天津大学材料科学与工程学院天津 300072
  2.中国科学院长春应用化学研究所高分子科学与技术全国重点实验室长春 130022
- 作者简介：
  
  Hong-fei Li, E-mail: hfli@ciac.ac.cn
- 基金信息：
- DOI：10.11777/j.issn1000-3304.2026.26005
  CLC：
- CSTR：32057.14.GFZXB.2026.7563
- Received：05 January 2026，
  
  Accepted：09 February 2026，
  
  Online First：25 March 2026，
- 稿件说明：
移动端阅览
张乐康, 孙晓宇, 李景庆, 刘伦洋, 廖涛, 卢影, 李宏飞, 门永锋, 蒋世春. 基于应力-应变曲线的机器学习预测聚乙烯和聚丙烯力学性能. 高分子学报, doi: 10.11777/j.issn1000-3304.2026.26005.

Zhang, L. K.; Sun, X. Y.; Li, J. Q.; Liu, L. Y.; Liao, T.; Lu, Y.; Li, H. F.; Men, Y. F.; Jiang, S. C. Machine learning predictive models for the tensile properties of polyethylene and polypropylene. Acta Polymerica Sinica (in Chinese), doi: 10.11777/j.issn1000-3304.2026.26005.
张乐康, 孙晓宇, 李景庆, 刘伦洋, 廖涛, 卢影, 李宏飞, 门永锋, 蒋世春. 基于应力-应变曲线的机器学习预测聚乙烯和聚丙烯力学性能. 高分子学报, doi: 10.11777/j.issn1000-3304.2026.26005. DOI： CSTR： 32057.14.GFZXB.2026.7563.

Zhang, L. K.; Sun, X. Y.; Li, J. Q.; Liu, L. Y.; Liao, T.; Lu, Y.; Li, H. F.; Men, Y. F.; Jiang, S. C. Machine learning predictive models for the tensile properties of polyethylene and polypropylene. Acta Polymerica Sinica (in Chinese), doi: 10.11777/j.issn1000-3304.2026.26005. DOI： CSTR： 32057.14.GFZXB.2026.7563.

摘要

根据聚乙烯(PE)和聚丙烯(PP)的工程应力-应变曲线以及杨氏模量(YM)、断裂伸长率(EB)和拉伸强度(TS)构建了多特征机器学习预测模型，探究了材料属性、制样工艺、测试参数对力学性能的影响. 通过最小绝对值收敛和选择算子(Lasso)、决策树回归(DTR)、随机森林回归(RF)等7种算法，结合5折交叉验证和超参数优化，实现了力学性能的定量预测；基于极端梯度提升树回归(XGB)算法完成了应力-应变曲线的高保真度拟合. 结果表明：PE力学性能的预测受数据量限制虽然存在精确度局限，但预测趋势表明预测方法有效；YM在PP数据集上预测性能最优(测试集

≥0.80，部分模型超过0.90)，无明显过拟合；EB和TS预测受数据量所限，仅RF、XGB、KNN模型在PP数据集中表现出有效预测能力；控制变量条件下，PP的应力-应变曲线预测

大于0.90，可精准再现温度依赖的力学性能. 证明了机器学习对聚烯烃力学性能预测的可行性，可为高分子材料力学性能快速预测提供技术支撑.

Abstract

Polyeth

ylene (PE) and polypropylene (PP) are among the most productive and extensively applied polymer materials worldwide

and precise regulation of their properties is a core prerequisite for attaining high-performance characteristics. In this study

a multi-feature machine learning prediction model was established based on the engineering stress-strain curves and three key mechanical indicators (Young's modulus (YM)

elongation at break (EB)

and tensile strength (TS)) of PE and PP. The model was employed to investigate the influence mechanisms of material attributes

sample preparation processes

and testing parameters on the mechanical properties of target polymers. Seven distinct algorithms

including Lasso

Decision Tree Regression (DTR)

and Random Forest Regression (RF)

were integrated with five-fold cross-validation and hyperparameter optimization strategies to realize the quantitative prediction of the mechanical properties. Furthermore

Extreme Gradient Boosting Regression (XGBoost

XGB) was adopted to achieve a high-fidelity fitting of the stress-strain curves. Due to the limitation of the amount of data

the prediction accuracy of the mechanical properties of PE has certain limitations. However

from the perspective of the overall trend of the prediction

this prediction method still has a good effect. The results demonstrate that the prediction performance for YM was optimal on the PP dataset

with the coefficient of determination (

) on the test set reaching no less than 0.80 and even exceeding 0.90 for certain models

without any obvious overfitting. In contrast

the predictive efficacy for EB and TS was constrained by the limited dataset size

where only the RF

XGBoost

and K-Nearest Neighbor (KNN) models exhibited reliable predictive capabilities on the PP dataset. Under variable control

the

value for the prediction of the PP stress-strain curves surpassed 0.90

enabling the accurate reproduction of the temperature-dependent mechanical pro

perties. This study verifies the feasibility of applying machine learning techniques to predict the mechanical properties of polyolefins

thereby providing promising technical support for the rapid evaluation of the mechanical performance of polymer materials.

关键词

Keywords

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