A framework for variation visualization and understanding in complex manufacturing systems

  • Authors:
  • Lee J. Wells;Fadel M. Megahed;Jaime A. Camelio;William H. Woodall

  • Affiliations:
  • Virginia Tech, Blacksburg, USA 24061;Virginia Tech, Blacksburg, USA 24061;Virginia Tech, Blacksburg, USA 24061;Virginia Tech, Blacksburg, USA 24061

  • Venue:
  • Journal of Intelligent Manufacturing
  • Year:
  • 2012

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Abstract

This paper provides a framework that allows industrial practitioners to visualize the most significant variation patterns within their process using three-dimensional animation software. In essence, this framework complements Phase I statistical monitoring methods by enabling users to: (1) acquire detailed understanding of common-cause variability (especially in complex manufacturing systems); (2) quickly and easily visualize the effects of common-cause variability in a process with respect to the final product; and (3) utilize the new insights regarding the process variability to identify opportunities for process improvement. The framework is illustrated through a case study using actual dimensional data from a US automotive assembly plant.