Designing the Geometry of Compact Tension Specimens for Easy Fracture Toughness Measurement Using Reinforcement Learning
文献类型：期刊论文
| 作者 | Qiu, Cheng3; Lin, Yuxia2; Shen, Yan2 ; Song, Hongwei3; Yang, Jinglei1,2; Song HW(宋宏伟)
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| 刊名 | JOURNAL OF APPLIED MECHANICS-TRANSACTIONS OF THE ASME
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| 出版日期 | 2024-09-01 |
| 卷号 | 91期号:9页码:14 |
| 关键词 | energy release rate laminates stress analysis |
| ISSN号 | 0021-8936 |
| DOI | 10.1115/1.4065624 |
| 通讯作者 | Qiu, Cheng(qiucheng@imech.ac.cn) ; Yang, Jinglei(maeyang@ust.hk) |
| 英文摘要 | For composite laminates, a rising R-curve is observed for their fracture toughness under Mode I stress, which is important for a comprehensive failure analysis of the materials. Since it is laborious to measure the R-curve due to its dependence on both the load and the crack extension, we put forward a novel compact tension specimen by modifying its geometry to eliminate the relation between fracture toughness and crack extension, so as to simplify the experimental process of the R-curve measurement by only recording the load history. Two machine-learning models were developed for the optimum sample design based on the finite element analysis of the effect of sample geometries on the R-curve. A simple neural network model was built for designing tapered specimen and a reinforcement learning model was created for further finding the best design from a broader design space. The results showed that, in contrast to the specimens with a tapered shape, which only ensure the independence between the R-curve and crack extension in the case of a small extension, the design provided by the reinforcement learning provides such independence across a wider range of crack length and an improved accuracy. |
| WOS关键词 | MODE-I ; R-CURVE ; FAILURE CRITERION ; PART I ; COMPOSITES ; PREDICTION ; STRENGTH ; BEAM |
| 资助项目 | Project of Hetao Shenzhen-Hong Kong Science and Technology Innovation Cooperation Zone[HZQB-KCZYB-2020083] ; Department of Science and Technology of Guangdong Province[2022A0505030023] ; Chinese Academy of Sciences[025GJHZ2022103FN] |
| WOS研究方向 | Mechanics |
| 语种 | 英语 |
| WOS记录号 | WOS:001308115600006 |
| 资助机构 | Project of Hetao Shenzhen-Hong Kong Science and Technology Innovation Cooperation Zone ; Department of Science and Technology of Guangdong Province ; Chinese Academy of Sciences |
| 源URL | [http://dspace.imech.ac.cn/handle/311007/96578]  |
| 专题 | 力学研究所_流固耦合系统力学重点实验室(2012-) |
| 通讯作者 | Qiu, Cheng; Yang, Jinglei |
| 作者单位 | 1.HKUST Shenzhen Hong Kong Collaborat Innovat Res In, Shenzhen 518048, Peoples R China 2.Hong Kong Univ Sci & Technol, Dept Mech & Aerosp Engn, Hong Kong 999077, Peoples R China 3.Chinese Acad Sci, Inst Mech, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Qiu, Cheng,Lin, Yuxia,Shen, Yan,et al. Designing the Geometry of Compact Tension Specimens for Easy Fracture Toughness Measurement Using Reinforcement Learning[J]. JOURNAL OF APPLIED MECHANICS-TRANSACTIONS OF THE ASME,2024,91(9):14. |
| APA | Qiu, Cheng,Lin, Yuxia,Shen, Yan,Song, Hongwei,Yang, Jinglei,&宋宏伟.(2024).Designing the Geometry of Compact Tension Specimens for Easy Fracture Toughness Measurement Using Reinforcement Learning.JOURNAL OF APPLIED MECHANICS-TRANSACTIONS OF THE ASME,91(9),14. |
| MLA | Qiu, Cheng,et al."Designing the Geometry of Compact Tension Specimens for Easy Fracture Toughness Measurement Using Reinforcement Learning".JOURNAL OF APPLIED MECHANICS-TRANSACTIONS OF THE ASME 91.9(2024):14. |
入库方式： OAI收割
来源：力学研究所
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