Open Accesscript Received: 02 January 2022, Received in Revised form: 01 February 2022, Accepted: 05 February 2022 DOI: 10.46338/ijetae0222_04 29 Design of an Intelligent System for Defect Recognition in Composite Materials using Lock-In Thermography
Author(s) -
Roberto Marani,
AUTHOR_ID,
Anna Gina Perri
Publication year - 2022
Publication title -
international journal emerging technology and advanced engineering
Language(s) - English
Resource type - Journals
ISSN - 2250-2459
DOI - 10.46338/ijetae0222_04
Subject(s) - lock (firearm) , composite number , convolutional neural network , thermography , artificial intelligence , computer science , materials science , mechanical engineering , composite material , engineering , infrared , optics , physics
This paper examines the lock-in thermographic technique for detecting Teflon defects within the composite material with a polymer matrix (Carbon Fiber-reinforced polymers, CFRP). In particular, a deep learning based network, made of a succession of convolutional layers, is implemented to process single thermal sequences generated in a simulation environment. As a result, the proposed methodology can accurately identify subsurface defects. Keywords— Composite materials, lock-in thermography, deep learning, convolutional neural network