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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 [1] 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.