A Unifying View of Some Process Adjustment Methods

摘要

We consider the problem of adjusting a machine that starts production after an off-target setup operation.This setup adjustment problem was first studied by Grubbs (1954,1983).A general formulation for setup adjustment problems is presented in this paper.The formulation unifies some 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.The proposed formulation is Bayesian and based on a Kalman filter.The formulation allows us to show the equivalence of the setup process adjustment problem with a simple instance of what is called a Linear Quadratic Gaussian (LQG) controller in the control engineering literature.As an important byproduct,the LQG model allows us to solve more complicated setup adjustment problems with readily available techniques.Extensions to cases in which there are quadratic adjustment costs and in which the problem is multivariate are discussed.The multivariate setup adjustment solution is illustrated with a multihead filling machine example.