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Predicting Features in Complex 3D Surfaces Using a Point Series Representation: A Case Study in Sheet Metal Forming

  • Subhieh El-Salhi
  • Frans Coenen
  • Clare Dixon
  • Muhammad Sulaiman Khan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8346)

Abstract

This paper presents an integrated framework for learning to predict geometry related features with respect to 3D surfaces. The idea is to use a training set of known prediction values to create a model founded on local 3D geometries associated with a given surfaces so that predictions with respect to a new “unseen” surfaces can be made. The local geometries are represented using point series curves. Two variations are proposed: (i) discretised and (ii) real number. To act as a focus for the work a sheet metal forming application is considered where we wish to predict the errors that are introduced as a result of applying a forming process. Given a desired surface T, the surface T actually produced as a result of the sheet metal forming process is affected by a phenomena called Springback (the feature we wish to predict). The proposed process has been evaluated using two flat-topped pyramid shapes and by considering a variety of parameter settings. Excellent results have been obtained in terms of accuracy and Area Under ROC Curve (AUC).

Keywords

3D Surface Representation Point series curves Classification 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Subhieh El-Salhi
    • 1
  • Frans Coenen
    • 1
  • Clare Dixon
    • 1
  • Muhammad Sulaiman Khan
    • 1
  1. 1.Department of Computer ScienceUniversity of LiverpoolUnited Kingdom

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