Micropillar compression using discrete dislocation dynamics and machine learning
文献类型：期刊论文
| 作者 | Tao, Jin; Wei DA(魏德安) ; Yu, Junshi; Kan, Qianhua; Kang, Guozheng; Zhang, Xu
|
| 刊名 | THEORETICAL AND APPLIED MECHANICS LETTERS
 |
| 出版日期 | 2024-01 |
| 卷号 | 14期号:1页码:100484 |
| ISSN号 | 2095-0349 |
| 关键词 | Discrete dislocation dynamics simulations Machine learning Size effects Orientation effects Microstructural features |
| DOI | 10.1016/j.taml.2023.100484 |
| 英文摘要 | Discrete dislocation dynamics (DDD) simulations reveal the evolution of dislocation structures and the interaction of dislocations. This study investigated the compression behavior of single-crystal copper micropillars using few-shot machine learning with data provided by DDD simulations. Two types of features are considered: external features comprising specimen size and loading orientation and internal features involving dislocation source length, Schmid factor, the orientation of the most easily activated dislocations and their distance from the free boundary. The yielding stress and stress-strain curves of single-crystal copper micropillar are predicted well by incorporating both external and internal features of the sample as separate or combined inputs. It is found that the Machine learning accuracy predictions for single-crystal micropillar compression can be improved by incorporating easily activated dislocation features with external features. However, the effect of easily activated dislocation on yielding is less important compared to the effects of specimen size and Schmid factor which includes information of orientation but becomes more evident in small-sized micropillars. Overall, incorporating internal features, especially the information of most easily activated dislocations, improves predictive capabilities across diverse sample sizes and orientations. |
| 分类号 | 二类 |
| WOS研究方向 | Mechanics |
| 语种 | 英语 |
| 资助机构 | National Natural Science Foundation of China [12192214, 12222209] |
| 其他责任者 | Zhang, X (corresponding author), Southwest Jiaotong Univ, Sch Mech & Aerosp Engn, Chengdu 610031, Peoples R China. |
| 源URL | [http://dspace.imech.ac.cn/handle/311007/93684]  |
| 专题 | 力学研究所_非线性力学国家重点实验室 |
| 作者单位 | 1.Chinese Acad Sci, Inst Mech, State Key Lab Nonlinear Mech, Beijing 100190, Peoples R China 2.Southwest Jiaotong Univ, Sch Mech & Aerosp Engn, Chengdu 610031, Peoples R China |
| 推荐引用方式 GB/T 7714 | Tao, Jin,Wei DA,Yu, Junshi,et al. Micropillar compression using discrete dislocation dynamics and machine learning[J]. THEORETICAL AND APPLIED MECHANICS LETTERS,2024,14(1):100484. |
| APA | Tao, Jin,魏德安,Yu, Junshi,Kan, Qianhua,Kang, Guozheng,&Zhang, Xu.(2024).Micropillar compression using discrete dislocation dynamics and machine learning.THEORETICAL AND APPLIED MECHANICS LETTERS,14(1),100484. |
| MLA | Tao, Jin,et al."Micropillar compression using discrete dislocation dynamics and machine learning".THEORETICAL AND APPLIED MECHANICS LETTERS 14.1(2024):100484. |
入库方式： OAI收割
来源：力学研究所
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