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  4. AI-based Quality Monitoring of Coupled Digital Twins for Multistep Process Chains
 
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2021
Conference Paper
Title

AI-based Quality Monitoring of Coupled Digital Twins for Multistep Process Chains

Other Title
Quality Monitoring Of Coupled Digital Twins For Multistep Process Chains Using Bayesian Networks
Abstract
The digital representation of physical assets and process steps by digital twins is key to address pressuring challenges like adaptive manufacturing or customised production. Recent breakthroughs in the field of digital twins and Edge-based AI already enable digital optimization of individual process steps. However, high-value goods typically include multiple step process chains including a broad range from generative and additive processes over several steps of material removal up to assembly. Therefore, a digital twin over the holistic process chain is necessary. While even the set-up of representative twins for a single step is already challenging, a concept for monitoring of the interaction and overall quality control of holistic process chains does not exist yet. The paper introduces a machine-learning method based on probabilistic Bayesian networks to monitor the »digital twin quality« of coupled digital twins which includes several sub-instances of digital twins. The approach identifies the contribution of each instance to the overall prediction quality. Furthermore, it is possible to give a range-estimation for the prediction accuracy of the individual sub-instances. It is therefore possible to identify the most influential sub-instances of digital twins as well as their individual prediction quality. With the help of this information, the quality of the digital twin can be improved by considering individual sub-instances in a targeted manner. Finally, a preview emphasises the potential benefits of the quantum computing technology when dealing with parallel computation of large-scale inference models.
Author(s)
Selch, Maximilian  
Fraunhofer-Institut für Werkzeugmaschinen und Umformtechnik IWU  
Hänel, Albrecht  
Fraunhofer-Institut für Werkzeugmaschinen und Umformtechnik IWU  
Frieß, Uwe  
Fraunhofer-Institut für Werkzeugmaschinen und Umformtechnik IWU  
Ihlenfeldt, Steffen  
Fraunhofer-Institut für Werkzeugmaschinen und Umformtechnik IWU  
Mainwork
CPSL 2021, 2nd Conference on Production Systems and Logistics. Proceedings. Online resource  
Conference
Conference on Production Systems and Logistics (CPSL) 2021  
DOI
10.15488/11266
Language
English
Fraunhofer-Institut für Werkzeugmaschinen und Umformtechnik IWU  
Keyword(s)
  • digital twin

  • process chain model

  • digitalisation

  • machine learning

  • Bayesian Networks

  • quantum computing

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