cript 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 | Zendy

Roberto Marani | Zendy; Anna Gina Perri | Zendy

Open Access

cript 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,

Anna Gina Perri

Publication year - 2022

Publication title -

international journal of emerging technology and advanced engineering

Language(s) - English

Resource type - Journals

ISSN - 2250-2459

DOI - 10.46338/ijetae0222_04

Subject(s) - composite number , thermography , convolutional neural network , materials science , lock (firearm) , computer science , artificial intelligence , composite material , infrared , mechanical engineering , engineering , 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

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