期刊名称：Journal of Theoretical and Applied Computer Science
印刷版ISSN：2299-2634
电子版ISSN：2300-5653
出版年度：2012
卷号：6
期号：2
页码：45-59
出版社：Polska Akademia Nauk * Oddzial w Gdansku, Komisja Informatyki,Polish Academy of Sciences, Gdansk Branch, Computer Science Commission
摘要：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.
Loading...
