Springback, the geometric difference between the loaded and unloaded configurations, is affected by many factors, such as material properties, sheet thickness, lubrication conditions, tooling geometry and process parameters. It is extremely difficult to develop an analytical model for springback control including all of these factors. The proposed neural network model is an attempt to deal with such a complicated non-linear system in a predictive way. For demonstration, an aluminum channel forming process is considered in this work. Our previous research  has shown that a variable binder force history during the forming operation can reduce the springback amount significantly while maintaining a relatively low maximum strain if an initial low binder force was used followed by a higher binder force. However, when and how much of the increase depends on the forming conditions of the current process. Here, several numerical simulations using Finite Element Method (FEM) were performed to obtain the teaching data required for training the neural network by means of the back-propagation algorithm. In the pre-dictive mode, different process inputs from the ones used in the previous stage were considered. For each case, the displacement where binder force increases and the level of the high binder force were predicted by the learned neural network and were numerically tested. Consistent low springback angle (< 0.5°) and moderate stretching amount (< 16%) were obtained even in the cases where the process parameters were varied as much as ±25% of the friction coefficient and sheet thickness or ±10% of the material's mechanical properties. The neural network can be easily implemented in experiments and/or in real production to resolve the uncertainty of springback amount due to the variations in material parameters and friction conditions.