Evaluation of statistical designs for experiments involving noise variables

Connie M. Borror, Douglas Montgomery, Raymond H. Myers

Research output: Contribution to journalArticlepeer-review

55 Scopus citations

Abstract

In process robustness studies, it is desirable to simultaneously minimize the influence of noise factors on the system and to determine the levels of controllable factors that will optimize the overall response or outcome. A methodology for evaluating designed experiments that involve both controllable and uncontrollable, or noise, factors is outlined and presented in this paper. Two variance expressions are developed for evaluating competing experimental design strategies. The maximum, average, and minimum scaled prediction error variances resulting from the models developed are displayed visually on variance dispersion graphs. The scaled prediction error variances account for mean model errors as well as variation transmitted to the process by noise variables.

Original languageEnglish (US)
Pages (from-to)54-70
Number of pages17
JournalJournal of Quality Technology
Volume34
Issue number1
DOIs
StatePublished - Jan 2002

Keywords

  • Central composite designs
  • Design of experiments
  • Response surface methodology
  • Robust parameter design

ASJC Scopus subject areas

  • Safety, Risk, Reliability and Quality
  • Strategy and Management
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering

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