Sheet Metal Forming In The Properties Of Parameter Identification And Strain Path Control

It' s very essential to correctly determine the material parameter in constitutive model for appropriately analyzing and simulating the process of metal forming. Based on Hill' s new yield criterion, the approach of artificial neural networks was used to identify the material parameter m in variant stress states during the process of material deformation. It is the key to establish the control model for deformation path during the process of metal forming. Using the techniques of artificial neural networks and numerical simulation, a new approach of controlling deformation path based on identification of material parameter is proposed. In this approach, the identification of material parameter m value and the control of deformation path are finished one by one in each loading stage. That is the identification of material parameter is done through the true deformation increment in the loading stage before, loading increment, and the stress state by artificial neural networks, after this, we can get loading increment for the next deformation stage by the artificial neural networks trained by stress stage, target deformation increment and the identified m value. By the experiment of tension and torsion test of thin walled tube on MTS , the experimental strain increment was obtained and compared with theoretical strain increment which obtained by simulation with m value 2 and the identified m value. The results of comparison validated the correctness of identified m and deformation control method using the identified m. Furthermore by the experiment of tension and torsion test of thin walled tube on MTS equipment, the experimental strain path was obtained and compared with target strain path. The results of comparison validated the correctness of the approach of controlling deformation path.