3 State of Research and Deduction of Requirements for the Computational Chaining (p. 63-64)

3.1 General

In the previous chapter, the fundamentals of the various processes within the manufacturing process chain of frame structures were introduced regarding the process attributes, the influence on the structure properties of the treated components and, finally, of the finished assemblies after joining. Basic material models are presented, on which computational methods are established. Based on these perceptions, various simulation methods are introduced in order to solve specific problems during the manufacturing process or to predict structural behaviour of parts. In this chapter knowledge and applications of simulation methods concerning the manufacturing processes introduced in Chapter 2 are presented as a part of a holistic manufacturing chain. Moreover, recent developments in the area of computation and interlinking of different simulation steps for supporting manufacturing processes are discussed. Based on the existing methods and tools and on the increasing requirements on behalf of the industry concerning product quality and optimisation of the product development process, the need for further research is deduced. The manufacturing process chain considered in this work includes, sequentially: the manufacturing fields of forming, metal cutting, and welding. The simulation of the structural effects along this manufacturing chain is introduced in this chapter, along with the latest research activities in the area of computational chaining methods during manufacturing. 3.2 Forming simulation

The simulation of forming processes is widely applied for research and industrial purposes. Simulation methods assure product quality and allow the determination of optimal values for the process parameters. Material failure (e.g. cracks or necking) and undesired form characteristics can be virtually predicted in advance and, thus, can be avoided during the real process. This can save time and minimise costs and, at the same time, help to optimise product quality. In recent years, the process simulation in metal forming reached the appropriate level for its practical application for industrial purposes. Simulating the complete forming manufacturing process prior to the real production process is, nowadays, state of the art (ROLL 2004).

The application of the finite element analysis for describing forming processes began thirty years ago. Since that time, a rapid development has been seen. In the last fifteen years, a large number of different studies concerning forming simulation have been performed, so that nowadays the development is at an advanced level with about fifteen commercial software codes worldwide, which enable the FEA of forming processes (ROLL &, LEMKE 2005). In the area of forming simulation a lot of two-dimensional analyses are still performed, since many forming processes can be reduced into two-dimensional models (plane or axis-symmetrical deformation condition). Three-dimensional models are increasingly applied, however, with the disadvantage of difficulties during the automatic mesh regeneration. In addition, long computation times are required, so that three-dimensional practical examples can only be used with a large computational effort. In the case of extrusion moulding of aluminium profiles, different simulation methods may be applied in order to describe the material flow and the structural properties. Moreover, the simulation of the extrusion moulding process of aluminium profiles with reinforcing steel elements has been developed further (SCHIKORRA 2006). Hereby, already established material and contact laws are applied.

Prediction and application capabilities of the forming simulation are shown in Table 3-1. Failure occurrences, such as cracks, wrinkles, and necking can be predicted with a high accuracy along with material flow, resulting sheet thickness, strain allocation, and blankholder pressing. With the aid of the process simulation of the deep drawing operation step the effort for die spotting can be reduced (HOFFMANN et al. 2006).