迁移学习在短纤维增强聚合物弹塑性响应预测中的应用研究

doi:10.19936/j.cnki.2096-8000.20260228.002

复合材料科学与工程 ›› 2026, Vol. 0 ›› Issue (2): 10-19.DOI: 10.19936/j.cnki.2096-8000.20260228.002

迁移学习在短纤维增强聚合物弹塑性响应预测中的应用研究

邢文奇^1,2, 朱水文^1,2*, 武顺心^1,2

1.湖北汽车工业学院汽车工程学院,十堰 442002;
2.汽车动力传动与电子控制湖北省重点实验室,十堰 442002

收稿日期:2025-01-06 出版日期:2026-02-28 发布日期:2026-03-12
通讯作者: 朱水文(1978—),男,博士,讲师,硕士生导师,研究方向为复合材料及汽车轻量化,swzhu@huat.edu.cn。
作者简介:邢文奇(2000—),男,硕士研究生,研究方向为汽车轻量化及复合材料应用。
基金资助:
湖北汽车工业学院博士基金(BK202213);湖北省汽车传动与电子控制重点实验室开放基金(ZDK12023B09);湖北省教育厅项目(B2023076)

Application of transfer learning in the prediction of elastoplastic response of short fiber reinforced polymers

XING Wenqi^1,2, ZHU Shuiwen^1,2*, WU Shunxin^1,2

1. School of Automotive Engineering, Hubei University of Automotive Technology, Shiyan 442002, China;
2. Hubei Key Laboratory of Automotive Power Train and Electronic Control, Shiyan 442002, China

Received:2025-01-06 Online:2026-02-28 Published:2026-03-12

摘要/Abstract

摘要： 随着复合材料在航空航天、汽车等多个领域的广泛应用,准确预测其力学性能变得愈发重要。本研究提出了一种基于迁移学习的短纤维增强聚合物应力-应变曲线预测方法。首先,通过DIGIMAT构建数据库,并采用拉丁超立方抽样技术选取样本,以提高模型训练的效率。然后,利用人工神经网络作为代理模型,并采用迁移学习方法,快速获取新材料的应力-应变预测模型。研究结果表明,迁移学习模型能有效捕捉材料应力-应变行为的关键特征,特别是在预测五阶多项式系数方面表现出色。进一步分析了纤维体积分数和长径比对材料力学性能的影响,发现较大长径比的纤维能更有效地传递应力。本研究为短纤维增强聚合物的性能预测提供了一种有效的工具,尤其在数据获取困难或成本较高时,展现了迁移学习在复合材料性能预测中的潜力和应用前景。

关键词: 短纤维增强聚合物, 迁移学习, 应力-应变曲线, 人工神经网络

Abstract: With the wide application of composite materials in aerospace and automotive fields, it has become more and more important to accurately predict their mechanical properties. In this study, a transfer learning-based stress-strain curve prediction method for short fiber reinforced polymers was proposed. Firstly, the database was constructed by DIGIMAT and the Latin hypercube sampling technique was used to select samples to improve the efficiency of model training. Then, using artificial neural network (ANN) as a surrogate model, and through the transfer learning method, the stress-strain prediction model of the new material can be quickly obtained. The results show that the transfer learning model can effectively capture the key features of the stress-strain behavior of materials, especially in predicting the fifth-order polynomial coefficients. The effects of fiber volume fraction and aspect ratio on the mechanical properties of materials were further analyzed, and it was found that the fibers with larger length-diameter ratios could transmit stress more effectively. This study provides an effective tool for the performance prediction of short fiber reinforced polymers, especially when data acquisition is difficult or the cost is high, the potential and application prospects of transfer learning in the performance prediction of composite materials are demonstrated.

Key words: short fiber reinforced polymers, transfer learning, stress-strain curves, artificial neural networks

中图分类号:

TB332

邢文奇, 朱水文, 武顺心. 迁移学习在短纤维增强聚合物弹塑性响应预测中的应用研究[J]. 复合材料科学与工程, 2026, 0(2): 10-19.

XING Wenqi, ZHU Shuiwen, WU Shunxin. Application of transfer learning in the prediction of elastoplastic response of short fiber reinforced polymers[J]. COMPOSITES SCIENCE AND ENGINEERING, 2026, 0(2): 10-19.

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迁移学习在短纤维增强聚合物弹塑性响应预测中的应用研究

Application of transfer learning in the prediction of elastoplastic response of short fiber reinforced polymers

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