| Determining plastic slips in rate-independent crystal plasticity models through machine learning algorithms | |
Wang ZW(王志文)1,2; Chen XJ(陈贤佳)1 ; Wen JC(温济慈)1,2; Wei YJ(魏宇杰)1,2
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| Corresponding Author | Wei, Yujie([email protected]) |
| Source Publication | EXTREME MECHANICS LETTERS
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| 2024-09-01 | |
| Volume | 71Pages:13 |
| ISSN | 2352-4316 |
| Abstract | Dislocation slip-based crystal plasticity models have been a great success in connecting the fundamental physics with the macroscopic deformation of crystalline materials. Pioneered by Taylor in his work on "plastic strain in metals" (Taylor, 1938), and further advanced by Bishop and Hill (1951a, 1951b), the Taylor-Bishop-Hill theory laid the foundation of today's constitutive models on crystal plasticity. An intriguing part of those modeling is to determine the active slip systems-which system to be involved in and how much it contributes to the deformation. In this paper, we developed a machine learning-based algorithm to determine accurately and efficiently the active slip systems in crystal plasticity constitutive models. Applications to the common three polycrystalline metals, face-centered cubic (FCC) copper, body-centered cubic (BCC) alpha-iron, and hexagonal close-packed (HCP) AZ31B, demonstrate that even a simple neural network could give rise to accurate and efficient results in comparing with traditional routines. There seems to be plenty of space for further reducing the computation time and hence scaling up the simulating samples. |
| Keyword | Machine learning Crystal plasticity Slip system Taylor criterion Maximum dissipation Finite element method |
| DOI | 10.1016/j.eml.2024.102216 |
| Indexed By | SCI ; EI |
| Language | 英语 |
| WOS ID | WOS:001286480400001 |
| WOS Keyword | CRYSTALLOGRAPHIC TEXTURE ; NEURAL-NETWORKS ; DEFORMATION ; STRAIN ; EVOLUTION ; MICROMECHANICS ; METALS |
| WOS Research Area | Engineering ; Materials Science ; Mechanics |
| WOS Subject | Engineering, Mechanical ; Materials Science, Multidisciplinary ; Mechanics |
| Funding Project | National Natural Sci-ence Foundation of China (NSFC) Basic Science Center for 'Multiscale Problems in Nonlinear Mechanics', China[11988102] |
| Funding Organization | National Natural Sci-ence Foundation of China (NSFC) Basic Science Center for 'Multiscale Problems in Nonlinear Mechanics', China |
| Classification | 二类/Q1 |
| Ranking | 1 |
| Contributor | Wei, Yujie |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://dspace.imech.ac.cn/handle/311007/96296 |
| Collection | 非线性力学国家重点实验室 |
| Affiliation | 1.Chinese Acad Sci, State Key Lab Nonlinear Mech LNM, Inst Mech, Beijing 100190, Peoples R China; 2.Univ Chinese Acad Sci, Sch Engn Sci, Beijing 100049, Peoples R China |
| Recommended Citation GB/T 7714 | Wang ZW,Chen XJ,Wen JC,et al. Determining plastic slips in rate-independent crystal plasticity models through machine learning algorithms[J]. EXTREME MECHANICS LETTERS,2024,71:13.Rp_Au:Wei, Yujie |
| APA | 王志文,陈贤佳,温济慈,&魏宇杰.(2024).Determining plastic slips in rate-independent crystal plasticity models through machine learning algorithms.EXTREME MECHANICS LETTERS,71,13. |
| MLA | 王志文,et al."Determining plastic slips in rate-independent crystal plasticity models through machine learning algorithms".EXTREME MECHANICS LETTERS 71(2024):13. |
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