Research On System For Concial Part Intelligent Deep Drawing

The intellectualization of sheet metal forming is the process in which, through an organic combination of sheet metal forming theory with control theory and computer science, and based on the characteristics of the workpiece to be worked, the material properties are identified and the optimum technique parameters are predicted on-line by means of easily-monitored physical quantities and the forming is automated to those parameters thus determined.The intellectualization of axisymmetrical workpiece is an important research field of sheet-metal-forming intellectualization and is the basis of intellectualization of complicated curved face workpiece. The identification of material properties and coefficient of friction is one important part of intellectualization. The precision and the time of identification will affect the realization of intellectualization directly. This thesis realizes the real-time identification of material properties and prediction of the optimum technique parameters by using the features and virtues of Artificial neural network (ANN). At the same time, this thesis realizes the real-time identification of material properties and prediction of the optimum technique parameters by using BP neural network.This thesis establishes a BP Neural network model for identification of material properties and prediction of the optimum technique parameters during conical part intellectualization, makes the program of BP Neural model using MATLAB program language. The real-time identification of material and the real-time prediction of the optimum technique parameters are successful, which are shown by the experimental results of different sheet metals from intelligent deep drawing, and it is significant for further study of the conical part intellectualization in sheet forming intelligent control. In order to realize real-time prediction and control in the process of intelligent deep drawing, the interface program between the identification model and the predictive model of BHF is developed by LabVIEW software. The integration and debugging work of the whole intellectualization control system was finished by combining the DAQ system and the control system.