Systematic errors introduced by springback phenomena in metal-sheet stamping ask for part-process adaptations, involving blanks, die features, and process parameters. Springback simulations for a couple of common parts are presented, and the long trials for setting up realistic simulations are pointed out. A data-driven methodology is then suggested, based upon the combined use of FEM and Artificial Neural Networks (ANN) analyses. A test is given for a simple shaped part: starting from a small set of reliable simulated results, an ANN is trained and adopted to retrieve the springback distorted profile for different combinations of parameter values.
Springback control in stamping car body parts
Steuerung des Rückfederns beim Stanzen von Karosserieteilen
2004
6 Seiten, 9 Bilder, 11 Quellen
Aufsatz (Konferenz)
Englisch
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