Digital twin for the single and small batch production

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The goal of this research project is to investigate the dependencies among the captured data in manufacturing processes in order to develop a production twin model. Both, directly and indirectly, interdependent data is examined for cause-effect relationships, and thus form the basis for an efficient and effective improvement of manufacturing processes. The use of data further supports to simulate production processes and to shorten the time-to-market significantly. An evaluation and use of data allows the single and small series production to optimize the planning whereby lead times can be reduced. The optimization of individual sections of the production is not effective. An increase of the efficiency can only be achieved by optimizing the overall production system. However, this requires an understanding of the entire production process and dependencies of their individual sub-processes. An understanding and accurate representation of the dependency of processes and parameters allow then to predict certain events and finally help to interfere in case of negative events. The requirements for a proactive intervention in the process is the understanding and documentation of cause-effect relationships of individual processes or sub-processes, which can be attained through the understanding of data dependencies. The identified data and cause-effect relationships between these data are combined finally to a digital production twin model. The results to be achieved can be divided into seven sub-goals: a) Creation of a generic catalog of all generated and relevant data in single and small series production companies b) Creation of a generic catalog of all stored data in single and small series production companies c) Development of dependency patterns between data structures d) Portrait of the dependencies in data structures in the form of a multidimensional matrix e) Identification of positive and negative data dependencies f) Identification of dependencies in the form of causeeffect relationships of different data structures g) Integration of the data and the cause-effect relationships to a digital production twin model.