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Autor: Alexander Seelig

From Random Sampling to Real-time Data

From Random Sampling to Real-time Data

Integrated plant engineering to increase process capability
Alexander Seelig
The digitization of processes is complex and error-prone. That is why manufacturing processes are monitored using statistical process control methods. The aim of the presented project was to answer the questions how the data basis for the use of the quality control chart (QRC) can be extended from random samples to near real-time data and how the implementation of the solution should be done. The software solution was developed and tested in the Fischertechnik learning factory. It could be shown that the data from the learning factory is suitable to be displayed in a closely timed manner and to be evaluated by means of process indicators of the QRK. In this way, errors can be avoided and capacities saved. (Only in German)
Industrie 4.0 Management | Volume 39 | 2023 | Edition 1 | Pages 48-52
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  • I4S+
  • Industry 4.0
    • Automation
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  • Artificial Intelligence
  • Functions
    • Start-up Management
    • Maintenance
    • Logistics
    • Assembly
    • Product Development
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    • Production Control
    • Process Management
    • Quality Management
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  • Tools
    • Additive Manufacturing
    • Analytics
    • Augmented Reality
    • Blockchain
    • Modularization
    • Training
    • Robotics
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    • Simulation
    • Software
  • Management
    • Services
    • Dynamics
    • Energy Efficiency
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    • Business Models
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    • SME
    • Management
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    • Resource Efficiency
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  • Journal
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