An intelligent AE sensor for the monitoring of finish machining process

Presents the latest results of sensing the cutting process on the basis of acoustic emission (AE) signals and some particularities in the further development of a monitoring model for the finish turning process. Due to nonlinearity, the large number of influencing parameters and missing information in AE data, artificial neural networks were chosen as a monitoring decision tool. The problem of accuracy in predicting the surface roughness on the basis of AE-because of the mutual interdependence of the data-requires a special procedure for building a neural network model. The final aim of such an approach is presented as improvements in learning or considerable reduction in error prediction. Further development of the monitoring model has the goal of building a so-called intelligent sensor, which should be able to perform the signal conditioning and feature extraction process.