Accounting for Within-Part Variability in Measurement System Analysis

Measurement system analysis is often performed in industry to check the quality of a measurement system. When assessing the capability of measurement systems, two key components of measurement error are commonly considered: repeatability and reproducibility. Experimental designs are generally used to quantify these error components, and the resulting investigation is referred to as a Gauge Repeatability and Reproducibility (Gauge R&R) study. Yet another possible source of error that is often overlooked is within-part variability, which can be a significant source of variability in the measurements of parts. Investigation of this error source requires modifications to both the experimental setup and the effects model for quantifying the components of variability. In this study, we analyze the effects of various measurement assessment plans on the Gauge R&R metric when within-part variation is present. Alternative measurement assessment plans and their ANOVA models are proposed. As common geometric forms in manufacturing processes, cylindrical and rectangular prism shaped parts are considered. Using computer simulations of parts having within-part variability, it is shown that neglecting within-part variation can cause misleading conclusions from an MSA study, and as a consequence, practitioners may not recognize the actual performance of their measurement system. Our conclusions are justified by a real world case study for a cylindrical part.