Identifying Sources of Variation in Sheet Metal Stamping

  • Karl D. Majeske
  • Patrick C. Hammett


Manufacturers using traditional process control charts to monitor their sheet metal stamping processes often encounter out-of-control signals indicating that the process mean has changed. Unfortunately, a sheet metal stamping process does not have the necessary adjustability in its process variable input settings to allow easily correcting the mean response in an out-of-control condition. Hence the signals often go ignored. Accordingly, manufacturers are unaware of how much these changes in the mean inflate the variance in the process output.

We suggest using a designed experiment to quantify the variation in stamped panels attributable to changing means. Specifically, we suggest classifying stamping variation into three components: part-to-part, batch-to-batch, and within batch variation. The part-to-part variation represents the short run variability about a given stable or trending batch mean. The batch-to-batch variation represents the variability of the individual batch mean between die setups. The within batch variation represents any movement of the process mean during a given batch run. Using a two-factor nested analysis of variance model, a manufacturer may estimate the three components of variation. After partitioning the variation, the manufacturer may identify appropriate countermeasures in a variation reduction plan. In addition, identifying the part-to-part or short run variation allows the manufacturer to predict the potential process capability and the inherent variation of the process given a stable mean. We demonstrate the methodology using a case study of an automotive body side panel.

analysis of variance designed experiment dynamic batch mean sheet metal stamping variation reduction 


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Copyright information

© Kluwer Academic Publishers 2003

Authors and Affiliations

  • Karl D. Majeske
    • 1
  • Patrick C. Hammett
    • 2
  1. 1.The University of Michigan Business SchoolAnn ArborUSA
  2. 2.Transportation Research InstituteThe University of MichiganAnn ArborUSA

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