Abstract
A mechanism for describing 3-D local geometries is presented which is suitable for input into a classifier generator. The objective is to predict the springback that will occur when Asymmetric Incremental Sheet Forming (AISF) is applied to sheet metal to produce a desired shape so that corrective measures can be applied. The springback is localised hence the desired before shape and the actual after shape are expressed using the concept of a Local Geometry Matrix (LGMs). The reported evaluation demonstrates that the LGM idea can be usefully employed to capture local geometries with respect to individual shapes.
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Khan, M.S., Coenen, F., Dixon, C., El-Salhi, S. (2012). Finding Correlations between 3-D Surfaces: A Study in Asymmetric Incremental Sheet Forming. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2012. Lecture Notes in Computer Science(), vol 7376. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31537-4_29
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DOI: https://doi.org/10.1007/978-3-642-31537-4_29
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