Identification of correlations between 3D surfaces using data mining techniques: Predicting springback in sheet metal forming

Abstract

A classification framework for identifying correlations between 3D surfaces in the context of sheet metal forming, especially Asymmetric Incremental Sheet Forming (AISF), is described. The objective is to predict "springback" , the deformation that results as a consequence of the application of a sheet metal forming processes. Central to the framework there are two proposed mechanisms to represent the geometry of 3D surfaces that are compatible with the concept of classification. The first is founded on the concept of a Local Geometry Matrix (LGM) that concisely describes the geometry surrounding a location in a 3D surface. The second, is founded on the concept of a Local Distance Measure (LDM) derived from the observation that springback is greater at locations that are away from edges and corners. The representations have been built into a classification framework directed at the prediction of springback values. The proposed framework and representations have been evaluated using two surfaces, a small and a large flat-topped pyramid, and by considering a variety of classification mechanisms and parameter settings. © Springer-Verlag London 2012.