Identification of correlation between 3D surfaces using data mining techniques : a case study of predicting springback in sheet metal forming

Abstract

This thesis presents data mining research work undertaken in the context of identifying correlations between 3D surfaces. More specifically, this research is directed at predicting distortions (referred to as springback) in sheet metal forming. The main objective was to identify a mechanism that best serves to both capture effectively 3D geometrical information while at the same time allowing for the generation of effective predictors (classifiers). To this end, three distinct 3D surface representation techniques are proposed based on three different concepts. The first technique, the Local Geometry Matrices (LGM) representation, is founded on the idea of Local Binary Patterns (LBPs), as used with respect to image texture analysis, whereby surfaces are defined in terms of local neighbourhoods surrounding individual points in a 3D surface. The second technique, the Local Distance Measure (LDM) representation, is influenced by the observation that springback is greater further from edges and corners, consequently surfaces are defined in terms of distance to the nearest edge or corner. The third technique, the Point Series (PS) representation, is founded on the idea of using a spatial linearisation with which to represent surfaces in terms of point series curves. The thesis describes and discusses each of these in detail including, in each case, the theoretical underpinning supporting each representation. A full evaluation of each of the representations is also presented. As will become apparent, the PS technique was found to be the most effective. The presented evaluation was directed at predicting springback, in the context of the Asymmetric Incremental Sheet Forming (AISF) manufacturing process, in such a way that an enhanced version of the desired 3D surface can be proposed intended to minimise the effect of springback. For the evaluation two at-topped, square-based, pyramid shapes were used. Each pyramid had been manufactured twice using Steel and twice using Titanium. In addition this thesis presents some idea on how the springback prediction mechanism can be incorporated into an intelligent process model. The evaluation of this model, by manufacturing corrected shapes, established that a sound prediction framework, incorporating the 3D surface representation techniques espoused in this thesis coupled with a compatible classification technique, had been established.