Impact Localisation in Composite Plates of Different Stiffness Impactors under Simulated Environmental and Operational Conditions

Abstract

A parametric investigation of the effect of impactor stiffness as well as environmental and operational conditions on impact contact behaviour and the subsequently generated lamb waves in composite structures is presented. It is shown that differing impactor stiffness generates the most significant changes in contact area and lamb wave characteristics (waveform, frequency, and amplitude). A novel impact localisation method was developed based on the above observations that allows for variations due to differences in impactor stiffness based on modifications of the reference database method and the Akaike Information Criterion (AIC) time of arrival (ToA) picker. The proposed method was compared against a benchmark method based on artificial neural networks (ANNS) and the normalised smoothed envelope threshold (NSET) ToA extraction method. The results indicate that the proposed method had comparable accuracy to the benchmark method for hard impacts under various environmental and operational conditions when trained only using a single hard impact case. However, when tested with soft impacts, the benchmark method had very low accuracy, whilst the proposed method was able to maintain its accuracy at an acceptable level. Thus, the proposed method is capable of detecting the location of impacts of varying stiffness under various environmental and operational conditions using data from only a single impact case, which brings it closer to the application of data driven impact detection systems in real life structures.

Ultrasonic Guided-Waves Sensors and Integrated Structural Health Monitoring Systems for Impact Detection and Localization: A Review

“…The Akaike Information Criterion (AIC) criterion for the accurate estimation of DToA has been demonstrated by De Simone et al [ 49 ]. Further research work has consolidated the investigation of the advantages of AIC, and a modified version for impact monitoring has been recently proposed by Seno et al [ 50 ]. In the latter work, an ANN was trained for automatic classification of defects in composite materials that were tested in laboratory and simulated operational conditions.…”

A Convolutional Neural Network for Impact Detection and Characterization of Complex Composite Structures

“…The PZT sensors are connected to the NI-PXIe-1073, to record the sensor response. The impactor is a steel hemispherical impactor as described in [17]. The dropping height was varied in steps of 20 mm from 20 to 80 mm.…”

“…Sensor data should not be input as a discrete signal as it contains too much information and it requires extraction of specific features such as Time of Arrival (ToA) of signals for impact detection on a plate, which is problem-dependant and cannot be generalized [16]. ANNs are generally accurate for the scope of a given training data, so for a real life impact identification and characterization, a large range of training data is required [17]. Another machine-learning method successfully employed for simple applications is the Support Vector Machine (SVM) which adapts its architecture automatically and requires less training data compared to ANN [16].…”

Impact diagnosis in stiffened structural panels using a deep learning approach

“…Artificial neural networks (ANNs), which are biologically inspired computational models that can be trained to model complex nonlinear relationships between inputs and outputs, have been used not only to predict the peak impact force [14][15][16][17] but also to reconstruct the impact force time-history 18 using different features extracted from the sensor signals. Different impact scenarios on flat and curved plates were investigated with regard to impact localization by Seno and colleagues in their studies 19,20 while also considering environmental and operating conditions.…”