Methodology for generating cross-technology metamodels (IKTINO)

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For manufacturing companies, it is of high relevance to design the production of components in such a way that the required component characteristics are manufactured with minimized time and cost investments. Until now, the individual processes for manufacturing a component were often considered separately from each other and only merged at higher planning levels. However, the cross-technology consideration of process sequences offers a high optimization potential, because the individual processes and process parameters are thus coordinated in a target-oriented manner. At the same time, increasingly shorter product development cycles mean that process sequences have to be designed in a short time. Therefore, fundamental models and methods are needed that support the rapid determination of economically optimized process parameters for process sequences.So far, no approach has been developed to quickly determine economically optimized process parameters for process sequences, taking into account the dependencies between the individual manufacturing processes and component characteristics. In order to close this research gap, a methodology will be developed in the proposed research project to determine economically optimized process parameters for process sequences on the basis of cross-technology metamodels. Metamodels map input-output correlations for individual manufacturing processes, exclusively resorting to a selection of the most important parameters for the application and thus enabling relevant insights into the design of process sequences within a short period of time. One focus of the proposed research project is therefore the metamodeling of process sequences. Metamodels of individual manufacturing processes are linked to cross-technology metamodels via previously identified transfer variables between the individual processes. Subsequently, the effects of the parameter reductions resulting from the selection of the most important parameters on the prediction accuracy of the overall model will be investigated so that an optimized parameter selection can be made.The second focus of the research project is the economic optimization of cross-technology metamodels. On the one hand, a method will be developed that enables meta cost models to be generated in order to determine the effects of individual process parameters on the costs of the process sequence. On the other hand, a novel optimization algorithm based on genetic algorithms will be developed, which enables the determination of economically optimized process parameters based on cross-technology metamodels and required component characteristics. The developed methodology will then be implemented in software and validated using two case studies.