An intelligent process model: predicting springback in single point incremental forming

This paper proposes an intelligent process model (IPM), founded on the concept of data mining, for predicting springback in the context of sheet metal forming, in particular, single point incremental forming (SPIF). A limitation with the SPIF process is that the application of the process results in geometric deviations, which means that the resulting shape is not necessarily the desired shape. Errors are introduced in a nonlinear manner for a variety of reasons, but a contributor is the geometry of the desired shape. A local geometry matrix (LGM) representation is used that allows the capture of local geometries in such a way that they are suited to input to a classifier generator. It is demonstrated that a rule-based classifier can be used to train the classifier and generate a classification model. The resulting model can then be used to predict errors with respect to new shapes so that some correction strategy can be applied. The reported evaluation of the proposed IPM indicates that very promising results can be obtained with regard to reducing the shape deviations due to springback.