**Gage
R&R - Variation Of Data For Quality Control **

Xmultiple's
Engineering Department

Quality
control includes ongoing inspections and data collection.
However, data collection contains random variations which
have a degree of inaccuracy. Part of this variation is due
to individual differences, but another part of this variation
is due to uncertainty in the measurements caused by variability
in the measurement equipment and process. If the measurement
uncertainty is too large, the measurement system may be
unusable. A gage repeatability and reproducibility (R&R)
study looks at this variability.

Gage
R&R helps determine the magnitude of the variation in
a measurement system as well as the sources of this variation.
While the sources of variation can be numerous, three of
these sources are fundamental: part-to-part variation, repeatability
and reproducibility.

In this gage performance curve, the red line shows the percent
probability of measuring a part in specification. The horizontal
axis is the actual reference value for the part. Source:
www.statsoft.com

Part-to-part
variation is the normal range over which measurements are
madeˇXthe part of your data you actually want to measure.
Repeatability is the variation because of the gage itself,
while reproducibility is the variation because of different
operators using the gage. Repeatability and reproducibility
together are called ˇ§measurement error,ˇ¨ or simply ˇ§noise,ˇ¨
and are measured as ˇ§gage R&R.ˇ¨ This noise is a nuisance
that adds uncertainty to your data. A good measurement system
has very low noise, preferably less than 1% of the total
variability in your data, indicated as a gage R&R of
less than 10%. A questionable system will have noise between
1% and 9% of the total variability, or a gage R&R between
10% and 30%. A poor system will have noise greater than
9% of the total variation, or a gage R&R greater than
30%.

Gage
R&R studies are usually performed on variable data -
height, length, width, diameter, weight, viscosity, etc.
Gage R&R measures the size of the noise relative to
the total data variation, which is called % of total variation
or %TV, and relative to the specification range, called
% of tolerance. It also separates the variability into its
sources, namely part-to-part variation, repeatability and
reproducibility. This information helps operators determine
how to fix a poor measurement system. For instance, a high
repeatability relative to reproducibility indicates the
need for a better gage. A high reproducibility relative
to repeatability indicates the need for better operator
training in the use of the gage.

One
way of seeing the consequences of measurement noise is to
use a gage performance curve. Such a curve shows the probability
of accepting a part as in specification using a specific
measurement system. Gage R&R software produces various
graphs to help operators understand measurements visually.

In
the "Gage Performance Curves" graph on the following
page, the red line shows the percent probability of measuring
a part in specification. The horizontal axis is the actual,
reference value for the part. The graph, showing a good
system, indicates that with gage R&R = 7% there is little
chance of rejecting a good part or accepting a bad one except
very near the specification limits, which are colored in
blue. For gage R&R = 14%, a questionable system, the
chance of error spreads over a wider range near the specification
limits. For gage R&R = 32%, a poor system, errors are
more common. These errors can be expensive by providing
measurements that are not reliable.

Gage
R&R helps determine if a measurement system is adequate
for your needs. The study also helps determine what needs
to be fixed if the system is poor, tells the operator if
the measurement system is trustworthy or if he needs a better
system and, ultimately, saves the operator from making costly
errors.

Guidelines
to accept or reject a Gage R&R.

If
the Total Gage R&R % study Var or % Tolerance is:

A. less than 10% accept

B. Between 10 & 30 % - Acceptable.

C. Over 30% - Unacceptable.

Should
not look at a single metric & justify acceptability
based on one metric passing? Rather we need to look at all
of the metrics within the Gage RR results.

.