.png){kind=icon}
返回
Impact characterization on thin structures using machine learning approaches
.png){kind=icon}
OA
AI
DOI：10.1016/j.cja.2023.11.022.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Machine learning algorithms are trained and compared to identify and to characterise the impact on typical aerospace panels of different geometry. Experimental activities are conducted to build a proper impacts' dataset. Polynomial regression algorithm and artificial neural network are applied and optimised to panels without stringer to test their capability to identify the impacts. Subsequently, the algorithms are applied to panels reinforced with stringers that represent a significant increase of complexity in terms of dynamic features of the system to test: the focus is not only on the impact position's detection but also on the event's severity. After the identification of the best algorithm, the corresponding machine learning model is deployed on an ARM processor minicomputer, implementing an impact detection system, able to be installed on board an aerial vehicle, making it a smart aircraft equipped with an artificial intelligence decision -making system. (c) 2023 Production and hosting by Elsevier Ltd. on behalf of Chinese Society of Aeronautics and Astronautics. This is an open access article under the CC BY -NC -ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/).
Keyword:
Artificial neural network
Impact localisation
Machine learning
Polynomial regression
Structural health monitoring
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
5.7
论文数:
4.7K
被引数:
1.4W
机构
引用论文
Improving accuracy of damage quantification based on two-level consistency control of PZT layersOAAI
OAAI分享收藏Guided waves for structural health monitoring in composites: A review and implementation strategies
PREAI
分享收藏Guided wave scattering at a delamination in a quasi-isotropic composite laminate: Experiment and simulation.png)
COMPOSITE STRUCTURESOAAI
COMPOSITE STRUCTURESIF7.1
OAAI分享收藏
查看更多内容