No abstract

Using BP Neural Network to optimize AE characteristic parameters of crack in drawing parts.By detecting the optimized characteristic parameters of crack, the crack in drawing parts are identified.According to the quality of drawing parts,the output of the network are crack signal and normal signal.Comparing the sensitivity of the input characteristic parameters on the output characteristic parameters,then pick the characteristic parameters which have bigger sensitivity values.Finally,the AE characteristic parameters such as Rise Time、AE Event Counter、Energy、Amplitude、Frequency Centroid can represent the signal of crack in the drawing parts better.These five characteristic parameters can identify the crack signal in the forming process of the drawing parts.

The purpose of the study is to extract the characteristic parameters of the forming crack acoustic emission (AE) signals generated by the metal deep drawing. Time-series analysis and MATLAB were used to adopt independent component analysis (ICA) to isolate the crack AE signals and extracted the characteristic parameters of AE signals. This study isolate the crack AE signals of the drawing parts by the FastICA method based on the maximum negative entropy, the data was processed by MATLAB and the regression model of the various decomposition established by time-series analysis to extract the characteristic parameters of the crack AE signals. The results suggested that this method can isolate the crack AE signals of the deep drawing successfully and can extract the characteristic parameters and distribution maps of the crack AE signals of the metal drawing parts effectively, provide a favorable basis for the judgment of the molding part quality.

In the roughened surface of 25# steel composite with a plastic layer which consists of PPS、TLCP、TPI and Graphite. The dry friction performance of composite material was tested in room temperature environment. The surface of wear was observed and analyzed by scanning electron microscope. Finally, to analyze the bond strength between the metal material and plastic work layer through the bond strength test. The results showed: the composite material has excellent tribological properties, after the shot peening coarsening in the metal matrix surface can well improve the binding force between plastic layer and metal matrix, the thickness of plastic layer has a certain effect to bond strength, the maximum bond strength was obtained when the thickness of 1.5 mm.

The paper performs an experimental research on the crack identification of drawing parts using AE technique. Under the platform of the AE system, the AE signals of drawing parts crack are acquired. BP neural network is designed with three layers. They are ten neurons of input layer, three neurons of output layer and thirteen neurons of hidden layer. The characteristic parameters of the crack acoustic emission are considered as the input of BP neural network to exercise the network. The test data are inputted to the neural network after it is exercised. The test result is in accord with the experiment result. The method is proper to identify the crack of drawing parts. The emergence of many inferior parts and the waste of resource can be avoided. It also can debase the cost of manufacture and improve the productive efficiency.