Stretch Bending Of The Intelligent Control Of Key Technologies

Stretch bending has been widely applied in the manufacture of extrusions forming in the field such as aviation, aerospace, weapon and automobile. The accuracy of the formed part is greatly affected by the forming defects of sheet metals for instance springback. which are affected by many non-linear factors such as the plastic forming characteristic and friction. As a result, it is important that we research the intelligent control technologies to improve the accuracy of the formed part. In this paper, a research was conducted on the on-line identification of material properties in stretch bending process and the prediction of the springback.For ensuring a better forming precision with the changes of material properties, on-line identification of material property parameters was researched. Then an on-line identification system combined with real time detect and on-line identification for material property parameters is established, by which the on-line identification for material property parameters during stretch bending is realized by digital filtering and curve fitting with the curve of loading-main cylinder of primary stretch bending. The error of identification was analyzed, then the model of zero calibrate and error compensation was established, which combined with the structure characteristic of stretch bending machine and resolved the problem of identification error efficiently. The research lays a foundation for further establishment of a system for real-time control of springback and intelligence of stretch bending.In this paper, a neural network model for prediction of forming springback is established by the neural network technology, the non-linear mapping between the deformation condition and springback was realized. An intelligent prediction system of stretch bending springback was build based on BP neural network, the extruded profiles stretch bending is studied respectively. The predicted precision is ideal, as shows the reliability and advantage of use of the neural network technology in forming springback prediction.