Impact characterization on thin structures using machine learning approaches

作     者：Flavio DIPIETRANGELO Francesco NICASSIO Gennaro SCARSELLI Flavio DIPIETRANGELO;Francesco NICASSIO;Gennaro SCARSELLI

作者机构：Department of Engineering for InnovationUniversity of SalentoLecce 73100Italy 

出 版 物：《Chinese Journal of Aeronautics》 (中国航空学报（英文版）)

年 卷 期：2024年第37卷第2期

页      面：30-44页

核心收录：

学科分类：08[工学] 0825[工学-航空宇航科学与技术] 

主　　题：Artificial neural network Impact localisation Machine learning Polynomial regression Structural health monitoring 

摘      要：Machine learning algorithms are trained and compared to identify and to characterise the impact on typical aerospace panels of different *** activities are conducted to build a proper impacts’*** regression algorithm and artificial neural network are applied and optimised to panels without stringer to test their capability to identify the ***,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 *** 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.