Synapse
⌘+K
Synapse
PulseExploreClubsResearchersJournals
Instagram
HomeClubsExplore
August 5, 2024Optics and Lasers in EngineeringOpen Access

Influence of lock-in thermography set-up parameters on the capability of a temporal convolutional neural network to characterize defects in a CFRP

View Full Paper
Ask AI
Bookmark
Share

Authors

TMTiziana MatarresePolytechnic University of BariRMRoberto MaraniUniversity of PerugiaDPDavide PalumboUniversity of Bari Aldo Moro

Discussion

Loading...

Member takes

Implication

Key Points

Key points are not available for this paper at this time.

Cite This Study

Matarrese et al. (2024) studied this question.

synapsesocial.com/papers/68e5d588b6db64358756bd5dhttps://doi.org/10.1016/j.optlaseng.2024.108455
View Full Paper
Ask AI
Bookmark
Share

Also Consider

Synapse has enriched 3 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Theory of frequency modulated thermal wave imaging for nondestructive subsurface defect detection2006 · 307 citations
  2. 2Thermal wave interferometry: a potential application of the photoacoustic effect1982 · 257 citations
  3. 3Comparison of quantitative defect characterization using pulse-phase and lock-in thermography2016 · 46 citations