Design of optimal pre-form stages for flashless forging of long flat parts using stochastic optimization methods

Theme Tool- and Mold-Making, Forming technology, Artificial Intelligence
Project title Design of optimal pre-form stages for flashless forging of long flat parts using stochastic optimization methods (Vorformoptimierung)
Project duration 01.08.2007 – 31.01.2010
The design of forging sequences is still a trial-and-error process at the state of the art. Furthermore the quality of the forging sequences highly depends on the experience of the engineers. The results of this research project shall provide the possibility to automatically derive preforms based on the finishform by using genetic algorithms. The aim is to shorten the design process of forging sequences by replacing the conventional trial-and-error process. Additionally the quality of the derived forging sequence is not depending on the constructors’ experience.

Publications about the project

A smart option to increase the energy yield of wind turbine generators is to increase its height. There is an exponential increase of the usable wind energy at enlarging the tower’s height, but also an exponential increase of the tower’s weight. The application of lightweight design concepts in the production of wind turbine tower sections may lead to weight reduction while keeping the tower’s stiffness at an equal level. Here the results of a study for lightweight concepts and their implementation on towers and a guiding systematic approach are being presented. The investigated design solutions proved successfully in bionic, aerospace and automotive applications. FEA simulations were used to compare the different structures and to estimate their feasibility. The investigation’s main result is a lightweight structure which provides weight reductions up to 20 %, by using lower wall thicknesses.

forging, genetic algorithm, preforming optimization

Sponsor

The project no. 55518960 received funding from the German Research Foundation (DFG).

Your contact person

Mareile Kriwall
Dipl.-Ing.

Manager process technology

Dr.-Ing.

Benjamin Küster

Manager production automation