In the last decades fatigue life prediction has reached a high level in respect to practical handling and accuracy. Deviations between numerical results and test results in terms of cycles till crack initiation are resulting mostly from insecure or lacking input data. On the one hand, the accuracy of Finite Element results gets better and better because of greatly increasing computer power and mesh density. Whereas on the other hand, the situation is much more critical regarding load data and especially regarding local material properties of the components (compared to specimen data). But in the last few years also the possibilities of process simulation have improved in such, that at least a few local material properties or quality indicators can be predicted with sufficient reliability. While for instance the detailed simulation of the welding process is still difficult during the common development process, sheet-metal forming and casting simulations are already widely applied to optimize properties of components in an early stage of development. Therefore, the idea to integrate process simulation into fatigue analysis is reasonable, because both simulation technologies represent a current state of the art. This integration has recently been realized both for forming simulation of steel sheet-metal as well as sand and die casting of aluminum and magnesium. The distribution of the effective plastic strain is an output of forming simulation which can be used as an indicator for local material properties. The secondary dendrite arm spacing (SDAS), whose distribution is an output from casting simulation, correlates significantly with porosity and endurance limit. For die casting, a pore free surface layer can be accounted for. All those parameters can be used as an input for fatigue analysis and practical examples demonstrate the influence on the predicted results.

Fatigue life prediction has reached a high level in respect to practical handling and accuracy in the last decades. As a result of insecure or lacking input data unacceptable deviations between numerical results and test results in terms of cycles till crack initiation are possible. On the one hand, the accuracy of Finite Element results gets better and better because of greatly increasing computer power and mesh density. Whereas on the other hand, the situation is much more critical regarding load data and especially regarding local material properties of the components (compared to specimen data).But in the last few years also the possibilities of process simulation have improved in such, that at least a few local material properties or quality indicators can be predicted with sufficient reliability. While for instance the detailed simulation of the welding process is still difficult during the common development process, sheet-metal forming and casting simulations are already widely applied to optimize properties of components and manufacturing processes in an early stage of development.Both simulation technologies represent a current state of the art. Therefore it is reasonable to integrate the results of process simulation into fatigue analysis to improve the accuracy of fatigue life prediction. For forming simulation of steel sheet-metal as well as for sand and die casting of aluminum and magnesium this integration has recently been implemented.As an output of forming simulation the effective plastic strain can be used as an indicator for local material changes. Using the distribution of the sheet metal thickness in FEA and fatigue life prediction is already possible because the required interfaces are available.With today's cast simulation tools distributions of local material parameters (e.g. ultimate strength, yielding point) can be predicted. Further the secondary dendrite arm spacing (SDAS), whose distribution is an output from casting simulation, correlates significantly with porosity and endurance limit. For die casting, a pore free surface layer can be accounted for.All those parameters can be used as an input for fatigue analysis and practical examples demonstrate the influence on the predicted results.

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