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Multivariate Statistical Process Control Based on Principal Component Analysis (MSPC-PCA): Some Reflections and a Case Study in an Autobody Assembly Process

Summary: [This abstract is based on the author's abstract.] Multivariate data collected in modern manufacturing processes through automated in-process sensing often exhibit high correlation, rank deficiency, low signal-to-noise ratio, and missing values. Conventional univariate and multivariate statistical process control are not suited to such environments. Multivariate statistical process control based on principal component analysis is recommended as an efficient statistical tool in these cases. The methodology is illustrated with data from an auto body assembly process.

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  • Topics: Engineering, Statistical Process Control (SPC)
  • Keywords: Assembly line methods, Automotive, Manufacturing process, Principal components, Statistical process control (SPC), Multivariate quality control, Case study,
  • Author: Ferrer, Alberto
  • Journal: Quality Engineering