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  4. Edge computing-based virtual measuring machine for process-parallel prediction of workpiece quality in metal cutting
 
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2022
Journal Article
Titel

Edge computing-based virtual measuring machine for process-parallel prediction of workpiece quality in metal cutting

Abstract
Digital process twins enable in-line quality inspection and root cause analysis, thus sustainably optimizing the production ramp-up and quality control cycle. Current approaches reply on expensive computing units or platforms to perform material removal simulations followed by virtual metrology. This raises deployment costs and conflicts with industrial edge devices. In this paper, a novel analytical approach is proposed to build an edge computing-based digital twin of machining processes for the process-parallel prediction of workpiece tolerances by leveraging machine internal signals and contextualizing metadata sources along the digital process chain, i.e., the digital thread. This concept is validated with the results of a state-of-the-art digital process twin and conventional measurement data from a coordinate measuring machine.
Author(s)
Huang, Ziqi
Rheinisch-Westfälische Technische Hochschule Aachen
Wiesch, Marian
Rheinisch-Westfälische Technische Hochschule Aachen
Fey, Marcel
Rheinisch-Westfälische Technische Hochschule Aachen
Brecher, Christian
Rheinisch-Westfälische Technische Hochschule Aachen
Zeitschrift
Procedia CIRP
Project(s)
EXC 2023: Internet of Production
Funder
Deutsche Forschungsgemeinschaft -DFG-, Bonn
Konferenz
Conference on Manufacturing Systems 2022
Thumbnail Image
DOI
10.1016/j.procir.2022.04.059
Language
English
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Fraunhofer-Institut für Produktionstechnologie IPT
Tags
  • digital shadow

  • digital thread

  • digital twin

  • edge computing

  • smart manufacturing

  • smart sensing

  • sustainable and resilient manufacturing

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