Modelling Deformation of Hydroformed Circular Plates Using Neural Networks

The process of applying fluid pressure to form metal sheets into desired shapes is widely used in the industry and is known as hydroforming. Similar to most other metal forming processes, hydroforming leads to non-homogeneous plastic deformation of the workpiece. Predicting the amount of deformation caused by any sheet metal forming process leads to better products. In this paper, a model is developed to predict the amount of deformation caused by hydroforming using an artificial intelligence technique known as neural networks. The data used to design the neural network model is collected from an apparatus that was designed and built in our laboratory. The neural network model has a feedforward architecture and uses Powell’s optimisation techniques in the training process. Single- and two-hidden-layer feedforward neural network models are used to capture the nonlinear correlations between the input and output data. The neural network model was able to predict the centre deflection, the thickness variation, and the deformed shape of circular plate specimens with good accuracy.