Variable data measurement systems analysis: advances in gage bias and linearity referencing and acceptability

Abstract

Measurement systems analysis (MSA) is a set of requirements and procedures adopted by the automotive industry and other disciplines to evaluate the accuracy and precision of measurement systems through assessing and quantifying the random and systematic errors and assigning appropriate dispositions for tolerance and performance acceptance. The methodology of variable data MSA comprises studies of a system's stability, bias, linearity and gage repeatability and reproducibility (GR&R). This paper describes advances in referencing and criteria for estimation of uncertainty errors, dispositions, and acceptability of MSA bias and linearity, proposing an extension to the basic statistical zero null-hypothesis to include overlap between confidence intervals and uncertainty associated with the reference standards used in bias and linearity studies.