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A framework for variation visualization and understanding in complex manufacturing systems

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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.

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Authors and Affiliations

  1. Virginia Tech, Blacksburg, VA, 24061, USA

    Lee J. Wells, Fadel M. Megahed, Jaime A. Camelio & William H. Woodall

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  1. Lee J. Wells
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  2. Fadel M. Megahed
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  3. Jaime A. Camelio
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  4. William H. Woodall
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Correspondence to Lee J. Wells.

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Wells, L.J., Megahed, F.M., Camelio, J.A. et al. A framework for variation visualization and understanding in complex manufacturing systems. J Intell Manuf 23, 2025–2036 (2012). https://doi.org/10.1007/s10845-011-0529-1

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  • DOI: https://doi.org/10.1007/s10845-011-0529-1

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