中国塑料2026，Vol.40Issue(5)：42-47,6.DOI:10.19491/j.issn.1001-9278.2026.05.008
基于机器学习的CF残留长度预测和影响因素特征重要性分析
Machine Learning-based prediction of carbon fiber residual length and feature importance analysis of influencing factors
杨文明 1谢林生 1周胜荣 1杨伟光 1徐成龙1
作者信息
- 1. 华东理工大学机械与动力工程学院,上海 200237
- 折叠
摘要
Abstract
This study investigated the preparation of PA6/carbon fiber(CF)composites in an internal mixer,focusing on the effects of rotor structure,rotational speed,rotor flight clearance,and PA6 melt viscosity on the residual length of car-bon fibers.Machine learning algorithms were employed to predict and evaluate the feature weights and importance of these influencing factors.The results indicate that the linear regression model effectively predicts the logarithmic mean of CF residual length,while nonlinear models,specifically decision tree and gradient boosting regression,provide more ac-curate assessments of feature importance.Among the factors analyzed,melt viscosity exhibits the greatest influence on the average CF residual length,with a feature importance exceeding 0.4;rotational speed follows with a value close to 0.32;the number of screw threads contributes approximately 0.25;and rotor flight clearance shows the least impact on CF residual length.关键词
碳纤维增强复合材料/纤维断裂/纤维长度/机器学习Key words
carbon fiber reinforced polymer/fracture of fiber/length of fiber/machine learning分类
化学化工引用本文复制引用
杨文明,谢林生,周胜荣,杨伟光,徐成龙..基于机器学习的CF残留长度预测和影响因素特征重要性分析[J].中国塑料,2026,40(5):42-47,6.
