What is it?

Variation is inherent to any system, and the data collection process is no exception. However, excessive variation in the data collection process will appear as variation on the control chart and can have a negative effect on process analysis. In addition to using operational definitions to ensure measurement consistency, you should periodically perform repeatability and reproducibility  tests and recalibrate gages.

Gage R&R refers to testing the repeatability and reproducibility of the measurement system. Repeatability is the variation found in a series of measurements that have been taken by one person using one gage to measure one characteristic of an item. Reproducibility is the variation in a series of measurements that have been taken by different people using the same gage to measure one characteristic of an item.

Gage R&R studies let you address two major categories of variation in measuring systems: gage variability and operator variability. Gage variability refers to factors that affect the gage’s accuracy, such as its sensitivity to temperature, magnetic and electrical fields and, if it is mounted, how tight or loose the mount is. Operator variability refers to variation caused by differences among people. It can be caused by different interpretations of a vague operational definition, as well as differences in training, attitude, and fatigue level.

Performing gage R&R studies can be made easier by using software such as GAGEpack.

When is it used?

Gages need to be recalibrated only when repeated test measurements show a lack of statistical control. Calibrating gages that do not need it or failing to calibrate gages that do need it can impair your ability to make accurate judgments about a process. Setting up a regular gage repeatability and reproducibility testing schedule can prevent either problem.

How is it made?

Note: The following are steps for a very basic gage R&R study. For a more in-depth analysis, refer to AIAG’s Measurement Systems Analysis or Evaluating the Measurement Process, by Donald J. Wheeler, Ph.D. and Richard W. Lyday.

1. Determine the number of operators, the number of parts, and the number of repeat readings. Consider how critical the dimension is. For more critical dimensions, use more parts to increase your degree of confidence in the study results. Also consider the part itself; large parts may be harder to handle and call for fewer samples and more trials.
2. Select 2 or 3 operators from those who are normally involved with the measurement process you are evaluating.
3. Collect the parts for the test. Parts should represent the range of variation in the day-to-day operation of the process. Number the parts, but do this in such a way that the operators will not know which part they are measuring.
4. Let the first operator measure each part in a random order and have another observer record the results. Enter the value in a column that represents that specific part number. Let the second and third operators measure the same parts in the same order without seeing the others’ readings. Record the data in the same manner, keeping data from each operator separated (alternating rows or different pages).
5. Repeat Step 4 for each trial (repeat reading), with the parts in a different random order each time.
6. Calculate averages and ranges for each operator for each part.
7. To analyze the repeatability , create an X-bar and R chart with the data. The range chart will show the consistency of the measurement process. If the range chart is in control, the repeatability of the measurement system is adequate.
8. The X-bar chart will show the reproducibility  (operator variation). Roughly 50% of the sample averages should fall outside the control limits. This indicates that the gage can distinguish among parts. To analyze the consistency among operators, use a Whiskers Plot.