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| Impact working condition identification of composite laminate based on deep learning and peridynamics |
| Received:August 20, 2021 Revised:November 23, 2021 |
| View Full Text View/Add Comment Download reader |
| DOI：10.7511/jslx20210820002 |
| KeyWord:deep learning peridynamics composite laminate working condition identification impact damage |
| Author | Institution |
| 唐和生 |
同济大学 土木工程学院, 上海, |
| 谢雅娟 |
同济大学 土木工程学院, 上海, |
| 陈豪 |
同济大学 土木工程学院, 上海, |
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| Abstract: |
| Under the action of impact load, damage such as fracture and delamination often occurs in composite materials.The identification of impact working conditions based on damage data is of great significance for improving the design of composite materials and ensuring their safety in use.For this purpose, a method for identifying impact working conditions of laminates based on deep learning and peridynamics (PD) theory is proposed in this paper.First, the improved "surface correction factor" PD theory was used to establish a PD model for the damage evolution analysis of composite laminates under the action of rigid body impact, then the numerical simulation results of the PD model and data enhancement technology were adopted to build the impact working condition datasets of the laminates.Deep learning-convolutional neural network (CNN) was adopted, which was trained using impact damage evolution data in different working conditions, and the identification of unknown impact working conditions was realized.The results show that the identification accuracy of initial falling speeds and angles of the steel balls in each working condition is higher than 90%. |
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