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Validating Measurement Data in Manufacturing Processes

In: Operations Research Proceedings 2018

Author

Listed:
  • David Brück

    (Technische Universität Kaiserslautern)

  • Sven Oliver Krumke

    (Technische Universität Kaiserslautern)

Abstract

Statistical process control (SPC) is an industry-standard methodology for monitoring and controlling quality during manufacturing processes. In this method, one measures quality data of small samples in preset time intervals or after a specific amount of produced items. Based on this data and some mathematical statistics, one can extrapolate to the entirety and, if necessary, adjust process parameters to ensure perfect quality products. However, this method is conditioned on the fact that the measured data is correct and neither the measuring device nor the inspector manipulated the incoming data. We study the problem of detecting manipulations in measurement data of manufacturing processes, which we refer to as validation of measurement data. To this end, we combine Decision Stump Forests with a novel variation of the Smith-Waterman algorithm to detect characteristics from a predefined list of typical manipulations and test this for feasibility on real data from manufacturers in Germany.

Suggested Citation

  • David Brück & Sven Oliver Krumke, 2019. "Validating Measurement Data in Manufacturing Processes," Operations Research Proceedings, in: Bernard Fortz & Martine Labbé (ed.), Operations Research Proceedings 2018, pages 415-420, Springer.
    • Handle: RePEc:spr:oprchp:978-3-030-18500-8_51
    DOI: 10.1007/978-3-030-18500-8_51
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