Fernandes, Henrique CoelhoHenrique CoelhoFernandesZhang, HaiHaiZhangQuirin, StevenStevenQuirinHu, JueJueHuSchwarz, MichaelMichaelSchwarzJost, HendrikHendrikJostHerrmann, Hans-GeorgHans-GeorgHerrmann2022-03-062022-03-062022https://publica.fraunhofer.de/handle/publica/27068210.1016/j.ndteint.2021.102561In this work, infrared thermography is used to detect defects on a 3D hybrid aluminium-CFRP composite structure. First, radiometric calibration and geometric distortion correction are performed for 3D inspection. Second, we propose a new unsupervised probabilistic low-rank component factorization thermographic de-noising model to improve image performance and defect visualization. Signal profiles and standard deviation analysis is used to assess the results, and x-ray CT inspections are compared to the infrared inspection results. Finally, we can conclude that the proposed algorithm can detect voids and resin rich areas presenting a better image performance if compared to direct infrared inspection results.eninfrared thermographyhybrid aluminium-CFRPradiance calibrationgeometric correctionprobabilistic de-noising620658670journal article