Tao, YuntaoYuntaoTaoHu, CaiqiCaiqiHuZhang, HaiHaiZhangOsman, AhmadAhmadOsmanIbarra-Castanedo, ClementeClementeIbarra-CastanedoFang, QiangQiangFangSfarra, StefanoStefanoSfarraDai, XiaobiaoXiaobiaoDaiMaldague, Xavier P.V.Xavier P.V.MaldagueDuan, YuxiaYuxiaDuan2022-05-062022-05-062022https://publica.fraunhofer.de/handle/publica/41561310.1007/s10921-022-00845-6The non-uniformity of non-planar object inspection data makes their analysis challenging. This paper reports a study of the use of recurrent neural network and artificial feed-forward neural network in pulsed thermography during the automated inspection of non-planar carbon fiber reinforced plastic samples. The time series, including the raw temperature-time series and sequenced signals obtained from the first derivative after thermographic signal reconstruction was used to train and test the models respectively. Quantitative comparisons of testing results showed that the long short-term memory recurrent neural network model was more accurate in handling time dependent information compared to the artificial feed-forward neural network model.enpulsed thermographynon-planarcarbon fiber reinforced plasticlong short-term memory recurrent neural networkartificial feed-forward neural networks620658670journal article