Crack Detection in Composite Materials Using McrowDNN

In the aerospace industry, composite materials are becoming more common. The presence of a crack in an aircraft makes it weaker and dangerous, can lead to complete fracture catastrophic failure. To predict position depth crack, various methods have been developed. For repair, diagnosis is extremely important. Even then, due uncertainties arising from sources such as environmental conditions, packing, intrinsic material property changes, accurate real engineering applications remains challenge. Deep learning (DL) approaches demonstrated powerful recognition potential variety fields recent years. comparison conventional artificial neural networks, which ability perform better recognition, Neural Network (DNN) able improve pattern features achieve recognition. this study, DNN-based detection method called Modified Crow (McrowDNN) proposed. preparation Mcrow-based DNN carried out here order choose most appropriate weights prejudices. results show that proposed outperforms current like Artificial (ANN), Recurrent (RNN) Search Algorithm (CSA).