Machine learning based constitutive modelling on craze yielding in polymeric materials
文献类型：期刊论文
| 作者 | Jiang KY(江科杙)1,2; Wen JC(温济慈)1,2; Wei YJ(魏宇杰)1,2 |
| 刊名 | ACTA MECHANICA SINICA
 |
| 出版日期 | 2025-07-03 |
| 卷号 | 41期号:7页码:13 |
| 关键词 | Polymers Craze yielding Constitutive model Machine learning Finite element method |
| ISSN号 | 0567-7718 |
| DOI | 10.1007/s10409-025-25370-x |
| 通讯作者 | Wen, Jici(wenjici@lnm.imech.ac.cn) ; Wei, Yujie(yujie_wei@lnm.imech.ac.cn) |
| 英文摘要 | The inelastic behavior of thermoplastic polymers may involve shearing and crazing, and both depend on temperature and strain rate. Traditional constitutive models account for temperature and strain rate through phenomenological or empirical formulas. In this study, we present a physics-guided machine learning (ML) framework to model shear and craze in polymeric materials. The effects of all three principal stresses for the craze initiation are considered other than the maximum tensile principal stress solely in previous works. We implemented a finite element framework through a user-defined material subroutine and applied the constitutive model to the deformation in three polymers (PLA 4060D, PLA 3051D, and HIPS). The result shows that our ML-based model can predict the stress-strain and volume-strain responses at different strain rates with high accuracy. Notably, the ML-based approach needs no assumptions about yield criteria or hardening laws. This work highlights the potential of hybrid physics-ML paradigms to overcome the trade-offs between model complexity and accuracy in polymer mechanics, paving the way for computationally efficient and generalizable constitutive models for thermoplastic materials. |
| 分类号 | 一类 |
| WOS关键词 | LARGE DEFORMATIONS ; GLASSY ; POLYCARBONATE |
| 资助项目 | National Natural Science Foundation of China (NSFC) Excellent Research Group Program for Multiscale Problems in Nonlinear Mechanics[12588201] |
| WOS研究方向 | Engineering ; Mechanics |
| 语种 | 英语 |
| WOS记录号 | WOS:001531602900002 |
| 资助机构 | National Natural Science Foundation of China (NSFC) Excellent Research Group Program for Multiscale Problems in Nonlinear Mechanics |
| 其他责任者 | 温济慈，魏宇杰 |
| 源URL | [http://dspace.imech.ac.cn/handle/311007/102089]  |
| 专题 | 中国科学院力学研究所 |
| 作者单位 | 1.Univ Chinese Acad Sci, Sch Engn Sci, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Inst Mech, State Key Lab Nonlinear Mech, Beijing 100190, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Jiang KY,Wen JC,Wei YJ. Machine learning based constitutive modelling on craze yielding in polymeric materials[J]. ACTA MECHANICA SINICA,2025,41(7):13. |
| APA | 江科杙,温济慈,&魏宇杰.(2025).Machine learning based constitutive modelling on craze yielding in polymeric materials.ACTA MECHANICA SINICA,41(7),13. |
| MLA | 江科杙,et al."Machine learning based constitutive modelling on craze yielding in polymeric materials".ACTA MECHANICA SINICA 41.7(2025):13. |
入库方式： OAI收割
来源：力学研究所
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