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Setup Adjustment of Multiple Lots Using a Sequential Monte Carlo Method

Summary: [This abstract is based on the authors' abstract.]A machine setup adjustment problem where process parameters are unknown is solved using a sequential Monte Carlo (SMC) method. The performance of the first SMC approach was equivalent to a proposed Markov chain Monte Carlo method in permitting on-line control, but at a fraction of the computational cost. Application of a second, modified SMC rule avoids unnecessary adjustments that can inflate the variance. A simulation approach allows tuning of the modified SMC rule to provide robust adjustment of unknown process parameters. Applications in short-run manufacturing processes are considered.

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  • Topics: Design of Experiments, Process Management, Statistical Process Control (SPC)
  • Keywords: Bayesian methods,Hierarchical experiments,Manufacturing process,Process control,Random effects,Short runs
  • Author: Lian, Zilong; Colosimo, Bianca M.; del Castillo, Enrique
  • Journal: Technometrics