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.
Design of an Intelligent System for Defect Recognition in Composite Materials using Lock-In Thermography / Marani, Roberto; Perri, Anna Gina. - In: INTERNATIONAL JOURNAL EMERGING TECHNOLOGY AND ADVANCED ENGINEERING. - ISSN 2250-2459. - ELETTRONICO. - 12:2(2022), pp. 29-36. [10.46338/ijetae0222_04]
Design of an Intelligent System for Defect Recognition in Composite Materials using Lock-In Thermography
Roberto MARANIMethodology
;Anna Gina PERRI
Conceptualization
2022
Abstract
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
