Abstract
Monitoring of drop impact damage is necessary because it produces invisible damage in composite materials without any visible mark on the surface. Monitoring of drop impact damage was carried out on Woven Glass Fibre Reinforced Polymer (WGFRP) composite laminate through Acoustic Emission (AE) technique. The significant AE parameters like signal strength, root means square value, counts and counts to peak were determined for drop impact damage. Impact damage tolerance was predicted using Artificial Neural Network (ANN) trained with AE parameters as input and impact damage tolerance as output. The predicated impact damage tolerance was with average error tolerance of 3.35%. The proposed network finds very good agreement for prediction of impact damage tolerance of impact damaged WGFRP composite laminate in real time application.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
