Impact characterization on thin structures using machine learning approaches

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 mini-computer, 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.