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

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.

Effect of Non-Fulfillment of Assumptions on Gage Repeatability and Reproducibility Study Evaluation

The evaluation of the measurement system quality has already become an integral part of quality planning activities in both the automotive and metallurgical industries. An important assumption for obtaining the most relia ble results is compliance with the basic assumptions for evaluating the variability of the measurement system. The main goal of this paper is to analyze, how the failure to meet the basic assumptions influences the evaluation of the measurement system's statistical properties. This goal is achieved by performing a detailed analysis of the latest developments in the field of measurement systems analysis aimed at verifying the assumptions of normality and uniformity. The evaluation of the effect of non-fulfillment of both assumptions on the values of the most important statistical properties of the measurement system is performed using simulated data. Suitable graphical tools are used for practical verification of both assumptions.