Using FEM and neural network prediction on hydrodynamic deep drawing of T-piece maximum length
The incremental updated Lagrangian elastoplastic finite element method and abductive network was applied in this study to analyze hydrodynamic deep drawing of 3D T-piece design. A series of 24 sets of different geometry tools were used for a 3D T-piece deep drawing process, with difference variations in the die radius, material radius and matching the different thickness of product. The results of the deep-drawing maximum length from FEM-simulation are then input to a neural network to establish a model for the T-piece deep-drawing variables. The neural network is composed of a number of functional nodes. Once the T-piece control parameters (material thickness, material radius and die radius) are given, the drawing processing performance (deep-drawing maximum length) can be accurately predicted by this developed network. This research achieved a satisfactory result based on demonstration of simulation, proving that it is a new and feasible approach used in control of T-piece drawing process of materials.
t-piece abductive network deep-drawing