2Dept. of Mechanical Engineering, College of Engineering, Guindy, Anna University, Chennai-600 025, Tamilnadu, India
The objective of this experimental work was to assess the drop impact damage on Woven Glass Fibre Reinforced Polymer composite laminate through online method and offline method. Online monitoring of drop impact damage was carried out by Acoustic Emission (AE) technique and AE signals during the drop impact test were captured. From the analysis of AE signals, it was observed that as the impact energy increases the AE parameters such as counts, counts to peak, signal strength and root mean square (RMS) values also increase. Offline assessment of impact damage on composite laminate was also observed by ultrasonic technique and it was inferred that ultrasonic parameters, namely amplitude and attenuation ratio were decreased with increase in impact energy of test. But attenuation coefficient had an indirect relationship with impact energy. During online/offline monitoring of composite laminate the AE/UT parameters which were obtained from real time monitoring are used to predict Impact Damage Tolerance (IDT) using a separate trained artificial neural network model. Based on the IDT value of composite, the component should be continued in-service or replaced.