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A Unifying View of Some Process Adjustment Methods

Summary: [This abstract is based on the authors' abstract.]

A general formulation is presented for setup adjustment problems that involve a machine that starts production after an off-target setup operation. The new formulation, which is Bayesian and based on a Kalman filter, unifies several well-known process adjustment schemes including Grubbs' harmonic and extended rules, adjustment methods based on stochastic approximation and recursive least squares, and a recent method on adaptive EWMA feedback controllers. This formulation shows the equivalence of the setup process adjustment problem with an instance of what is called a Linear Quadratic Gaussian (LGQ) controller in the control engineering literature. A multivariate setup adjustment solution is illustrated using the example of a multihead filling machine.

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  • Topics: Process Management
  • Keywords: Process,Stochastic models,Kalman filtering,Exponentially weighted moving average (EWMA),Bayesian methods,Least squares
  • Author: Del Castillo, Enrique; Pan, Rong; Colosimo, Bianca M.
  • Journal: Journal of Quality Technology