Classification based 3-D surface analysis: predicting springback in sheet metal forming

This paper describes an application of data mining, namely classification, with respect to 3-D surface analysis. More specifically in the context of sheet metal forming, especially Asymmetric Incremental Sheet Forming (AISF). The issue with sheet metal forming processes is that their application results in springback, which means that the resulting shape is not necessarily the desired shape. Errors are introduced in a non-linear manner for a variety of reasons, but the main contributor is the geometry of the desired shape. A Local Geometry Matrix (LGM) representation is thus proposed that allows the capture of local 3-D surface geometries in such a way that classifier generators can be effectively applied. The resulting classifier can then be used to predict errors with respect to new surfaces to be manufactured so that some correcting strategy can be applied. The reported evaluation of the proposed technique indicates that excellent results can be produced.