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Statistical Process Control in Manufacturing

Encyclopedia of Systems and Control

Abstract

Statistical process control has been successfully utilized for process monitoring and variation reduction in manufacturing applications. This entry aims to review some of the important monitoring methods. Topics discussed include: Shewhart’s model, \(\bar{X}\) and R control charts, EWMA and CUSUM charts for monitoring small process shifts, process monitoring for autocorrelated data, and integration of statistical and engineering (or automatic) control techniques. The goal is to provide readers from control theory, mechanical engineering, and electrical engineering an expository overview of the key topics in statistical process control.

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Authors and Affiliations

  1. Department of Industrial and Manufacturing Engineering, High Performance Materials Institute Florida A&M University, Florida State University, Tallahassee, FL, USA

    O. Arda Vanli

  2. Department of Industrial and Manufacturing Engineering, The Pennsylvania State University, University Park, PA, USA

    Enrique Del Castillo

Authors
  1. O. Arda Vanli
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  2. Enrique Del Castillo
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Correspondence to O. Arda Vanli .

Editor information

Editors and Affiliations

  1. Electrical and Computer Engineering, Boston University, Boston, Massachusetts, USA

    John Baillieul

  2. Automation and Control Solutions, Honeywell, Golden Valley, Minnesota, USA

    Tariq Samad

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© 2014 Springer-Verlag London

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Vanli, O.A., Castillo, E.D. (2014). Statistical Process Control in Manufacturing. In: Baillieul, J., Samad, T. (eds) Encyclopedia of Systems and Control. Springer, London. https://doi.org/10.1007/978-1-4471-5102-9_258-1

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  • DOI: https://doi.org/10.1007/978-1-4471-5102-9_258-1

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  • Online ISBN: 978-1-4471-5102-9

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Chapter history

  1. Latest

    Statistical Process Control in Manufacturing
    Published:
    09 December 2019

    DOI: https://doi.org/10.1007/978-1-4471-5102-9_258-2

  2. Original

    Statistical Process Control in Manufacturing
    Published:
    05 November 2014

    DOI: https://doi.org/10.1007/978-1-4471-5102-9_258-1