The planning and configuration of complex flexible production lines-cyclically interlinked systemscover numerous aspects and various possibilities. It is not possible to determine the applicability and characteristics for each of these variations by applying already existing planning methods. This paper outlines an innovative approach to this problem, which is based on the application of learning algorithms such as artificial neural networks. The iterative design process of a production line is initiated by a customer request. An initial layout is usually planned by an engineer and considered in a forecast system, which classifies the layout as stable or unstable. In case of stable layouts, the performance and cost values are visualized with operating figures. On this basis, the engineer is able to evaluate the layout. If the result is not satisfactory, new production line layouts are generated by modifying the overruled layout and another iteration begins.
The demand of parts with low weight, high material properties and relatively low cost leads to lightweight constructions. Especially hollow structures with a high stiffness and minimized weight are expected to have high potentials. In comparison to other manufacturing technologies, e.g. hydroforming or casting, characterized by their limitations of material thickness or mechanical strength, forging is a costeffective way to produce lightweight parts with excellent material properties and a balanced relation between strength and mass. Based on known flashless forging processes, a new technology to produce forged parts with hollow structures is developed at IPH — Institut fu¨r Integrierte Produktion Hannover, Germany. The hollow structure is filled by an incompressible liquid which is removed after the forming process. Maximum deformations are reached by using punches moving into the liquid.
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