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Converging material flow simulation models as digital twins of production systems

Converging material flow simulation models as digital twins of production systems
将物料流仿真模型融合为生产系统的数字孪生
批准号:
535966478
负责人:
Professorin Dr.-Ing. Gisela Lanza
金额:
$0.0万
依托单位国家:
德国
项目类别:
Research Grants
财政年份:
--
资助国家:
德国
项目状态:
未结题
起止时间:

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中文摘要
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英文摘要
In an increasingly volatile market environment, production companies must constantly adapt to new environmental conditions, react to external events and evaluate alternative courses of action under great time pressure and uncertainty. This can be efficiently and precisely supported by a material flow simulation model of the production system, which can handle the complex interactions, dynamic behavior and stochastic processes in the system. Since the model maintenance required to keep such a simulation model available with the required high accuracy is very labor-intensive and, in particular, know-how-intensive, this shall be automated in this project. To this end, procedures for using real production data (that is increasingly available because of digitalization) for the automated adaptation of the model to reality (the so-called convergence) are developed. The conventional simulation model thus becomes a digital twin of the production system. For this purpose, first a holistic concept in which, in addition to the overall procedure, the initial state of the digital twin development, the manually created seed model, is defined, must be developed. Based on this, the necessary methods for data collection and processing must be described. Therefore, systematics for the definition of numerous setup parameters, such as the validation metrics used, threshold values and the update period length, are to be developed. Since in practice simulation models are often created in the planning phase of production systems and digital twins should accompany the entire life cycle of the production system, the transition from the planning to the operating phase of the digital twin is also being investigated in the research project. The procedure and its algorithms will be implemented and tested for exemplary use cases from industry and the institute's own learning factory. In this way, the behavior of a digital twin after implementation can be examined in greater detail in order to gain further insights into its development and use.
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国内基金
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