Impact characterization on thin structures using machine learning approaches  

作　　者：Flavio DIPIETRANGELO Francesco NICASSIO Gennaro SCARSELLI 

机构地区：[1]Department of Engineering for Innovation,University of Salento,Lecce 73100,Italy

出　　处：《Chinese Journal of Aeronautics》2024年第2期30-44,共15页中国航空学报（英文版）

摘　　要：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.

关 键 词：Artificial neural network Impact localisation Machine learning Polynomial regression Structural health monitoring 

分 类 号：V214[航空宇航科学与技术—航空宇航推进理论与工程] V414