The forming tool design process generates large amounts of data up to the first falling parts. On the one hand, simulation results, geometric measurements and design models originate from different software tools, which leads to a non-consolidated set of data inventory. On the other hand, the total data volume is hard to handle economically. A stable and user-friendly data structure for overarching tool tryout is missing. Often several experience-based iterations are necessary to derive the tool's working surfaces, which is both time- and resource consuming and even may lead to postponed start of production. Meanwhile, early-generated data does not involve into the manual optimization process. Therefore, in this paper a parameterized data handling methodology is introduced, which enables systematic reverse engineering, data consolidation, and springback compensation. Each generated dataset during tryout is traced back to a mathematical description of geometry using so called control points (B-Spline model). Through, the parameterized description, the different data formats interact straightforward and need minimum storage. The developed concept is demonstrated for the springback analysis of a forming component using design models and numerical data.     «

The forming tool design process generates large amounts of data up to the first falling parts. On the one hand, simulation results, geometric measurements and design models originate from different software tools, which leads to a non-consolidated set of data inventory. On the other hand, the total data volume is hard to handle economically. A stable and user-friendly data structure for overarching tool tryout is missing. Often several experience-based iterations are necessary to derive the tool...     »