Digital twin for the single and small batch production

适用于单件和小批量生产的数字孪生

基本信息

批准号：
406411721
负责人：
Professor Dr.-Ing. Günther Schuh
金额：
--
依托单位：
Werkzeugmaschinenlabor - WZL
依托单位国家：
德国
项目类别：
Research Grants
财政年份：
2018
资助国家：
德国
起止时间：
2017-12-31 至 2020-12-31
项目状态：
已结题

来源：
https://gepris.dfg.de/gepris/projekt/406411721?language=en
关键词：
Digital twin single small batch

项目摘要

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.

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性，以开发生产孪生模型。直接和间接地检查相互依赖的数据的因果关系，从而构成高效且有效地改进制造过程的基础。数据的使用进一步支持模拟生产流程并显着缩短上市时间。数据的评估和使用允许单件和小批量生产优化规划，从而缩短交货时间。生产各个环节的优化效果不佳。效率的提高只能通过优化整个生产系统来实现。然而，这需要了解整个生产过程及其各个子过程的依赖性。对过程和参数的依赖性的理解和准确表示可以预测某些事件，并最终有助于在发生负面事件时进行干预。主动干预流程的要求是理解和记录各个流程或子流程的因果关系，这可以通过理解数据依赖性来实现。识别出的数据以及这些数据之间的因果关系最终组合成数字化生产孪生模型。要实现的结果可分为七个子目标： a) 创建单个和小系列生产公司中所有生成的相关数据的通用目录 b) 创建单个和小系列中所有存储数据的通用目录生产公司 c) 开发数据结构之间的依赖性模式 d) 以多维矩阵的形式描绘数据结构中的依赖性 e) 识别正负数据依赖性 f) 以不同数据的因果关系形式识别依赖性g) 数据和结构的整合数字化生产孪生模型的因果关系。