Digital twin for the single and small batch production

该研究项目的目标是调查制造过程中捕获的数据之间的依赖关系，以便开发生产孪生模型。直接和间接检查相互依赖的数据的因果关系，从而形成有效和有效地改进制造工艺的基础。数据的使用进一步支持了对生产过程的模拟，并大大缩短了上市时间。对数据的评估和使用使单件和小批量生产可以优化计划，从而减少交货期。个别生产环节的优化并不奏效。只有通过优化整个生产系统才能提高效率。然而，这需要了解整个生产过程及其各个子过程的依赖关系。对过程和参数的依赖性的理解和准确表示允许随后预测某些事件，并最终帮助在发生负面事件的情况下进行干预。对进程进行主动干预的要求是了解和记录各个进程或子进程的因果关系，这可以通过了解数据相关性来实现。识别的数据和这些数据之间的因果关系最终被组合成数字生产孪生模型。要实现的成果可分为七个分目标：a)为单一和小批量生产公司的所有生成和相关数据创建一个通用目录；b)为单个和小批量生产公司的所有存储数据创建一个通用目录；c)开发数据结构之间的依赖关系；d)以多维矩阵的形式描绘数据结构中的依赖关系；e)确定正负数据依赖关系f)以不同数据结构的因果关系的形式确定依赖关系g)将数据和因果关系整合到数字生产孪生模型中。

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.