Acoustic emission signal-based non-destructive testing of carbon content of Pr-Nd alloys

In the quality analysis of contemporary industrial production of praseodymium-neodymium (Pr-Nd) alloys, the amount of carbon content is mainly determined using chemical analysis methods. To overcome the shortcomings of the long durations and high costs of quality inspection cycles, this study proposes a non-destructive model for determining the carbon content of Pr-Nd alloys using acoustic emission signals collected using a mel frequency cepstral coefficient (MFCC) long short-term memory (LSTM) network (MFCC-LSTM) model and a data acquisition system. The MFCC ensures accurate signal feature extraction and data dimensionality reduction and the LSTM enables learning of the extracted features. The recognition rate of the MFCC-LSTM model reaches up to 97.53%, which can satisfy the quality inspection requirements for the industrial production of Pr-Nd alloys. In model evaluation, the receiver operating characteristic (ROC) curve shows good performance indices, indicating that the model is robust. Real-time verification of the model shows that the proposed method greatly shortens the time of each quality inspection link; the quality inspection time for a single piece of Pr-Nd alloy is only 0.3-0.65 s, which is a good real-time parameter.