title: Gauge Capability Analysis: classical versus ANOVA.
abstract: In order to use Statistical Process Control (SPC) efficiently and effectively in today's modern industrial environment, it is essential to analyze and determine the extent of gauge variability. The variation that occurs on a control chart is essentially a combination of product and gauge variation. Studies of measurement or gauge variation are absolutely a waste of resources unless they can lead to a substantial reduction in process variability or to an improvement in process and product quality. The goal of gauge capability analysis is an understanding and quantification of the various sources of variability present in the measurement process. The purpose of this paper is to illustrate the fundamental difference between Classical Gauge Capability Analysis (CGCA) and the use of a more powerful approach based on the Analysis of Variance (ANOVA). The author recommends that the latter approach is more useful and powerful in the presence of an interaction between the parts and the operators involved in the measurement process. An example is illustrated in the paper to demonstrate the two approaches.
authors.affiliation: International Manufacturing Centre, University of Warwick, Coventry, England, United Kingdom.
authors.forename: J
authors.initials: J
authors.lastname: Antony
authors.forename: G
authors.initials: G
authors.lastname: Knowles
authors.forename: P
authors.initials: P
authors.lastname: Roberts
mesh_terms: D000704:Analysis of Variance; D002138:Calibration / Q000592:standards*; D004864:Equipment and Supplies / Q000592:standards*; D006801:Humans; D015203:Reproducibility of Results; D017749:Total Quality Management / Q000706:statistics & numerical data*