| contributor author | Yang, Hang | |
| contributor author | Qiu, Hai | |
| contributor author | Xiang, Qian | |
| contributor author | Tang, Shan | |
| contributor author | Guo, Xu | |
| date accessioned | 2022-02-04T22:06:39Z | |
| date available | 2022-02-04T22:06:39Z | |
| date copyright | 6/4/2020 12:00:00 AM | |
| date issued | 2020 | |
| identifier issn | 0021-8936 | |
| identifier other | jam_87_9_091005.pdf | |
| identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4274891 | |
| description abstract | In this paper, a data-driven approach for constructing elastoplastic constitutive law of microstructured materials is proposed by combining the insights from plasticity theory and the tools of artificial intelligence (i.e., constructing yielding function through ANN) to reduce the required amount of data for machine learning. Illustrative examples show that the constitutive laws constructed by the present approach can be used to solve the boundary value problems (BVPs) involving elastoplastic materials with microstructures under complex loading paths (e.g., cyclic/reverse loading) effectively. The limitation of the proposed approach is also discussed. | |
| publisher | The American Society of Mechanical Engineers (ASME) | |
| title | Exploring Elastoplastic Constitutive Law of Microstructured Materials Through Artificial Neural Network—A Mechanistic-Based Data-Driven Approach | |
| type | Journal Paper | |
| journal volume | 87 | |
| journal issue | 9 | |
| journal title | Journal of Applied Mechanics | |
| identifier doi | 10.1115/1.4047208 | |
| journal fristpage | 091005-1 | |
| journal lastpage | 091005-9 | |
| page | 9 | |
| tree | Journal of Applied Mechanics:;2020:;volume( 087 ):;issue: 009 | |
| contenttype | Fulltext | |
