The manufacturing sequence influence, to a large extent, component properties like fatigue life, shape accuracy and manufacturability. By simulating the manufacturing sequence, using numerical or empirical models, and extracting important accumulated data, like residual stress, hardness and shape, the possibilities of early analysis of a design concept and the associated manufacturing sequence will increase. An established methodology has the potential of reducing physical testing and the time and costs of product design and process planning. This thesis proposes an algorithm to be used for setting up a framework of interconnected process step models. With support from the algorithm, it is possible to extract a virtual simulation sequence from a physical manufacturing sequence. Thereby, you can replicate the aggregated effects of process steps on part key features and manufacturing features. The algorithm will serve as a tool in process planning when establishing virtual manufacturing sequences. The virtual sequences should be used for virtual prediction of component properties, optimization of process parameters and evaluation of the effects of replacing, removing or adding process steps to a manufacturing sequence The algorithm is based on stepwise upstream selection of process steps, definition of interconnected models and selection of interconnected datasets using breadth first search. The algorithm completes existing procedures for data mapping and exchange of data between models into an overall approach for establishing virtual manufacturing sequences. Other scientific contributions are methods for modelling of deep rolling and blasting, a model material for validation of rolling and forging simulation and principles for integration of process simulation with CAD/CAM. ; QC 20151016