![]() Recall that Gage R&R and gage accuracy (bias) are two different things. Don’t spend the time and effort to perform a Gage R&R to prove what you already know or suspect.Įnsure that the gage is calibrated through its operating range. You should have the philosophy that the Gage R&R will achieve acceptable results. If you have prior knowledge that there are worn out cables, bent pins, untrained operators, outdated software, or any other problems with the gage, get those resolved first. Prior to conducting a Gage R&R, the following steps/precautions should be taken.ġ) Address all known issues with the gage ![]() Gage R&R studies can be conducted on both variable data (measurements that can be displayed in decimal form), and attribute data (produces “go/no-go” results or a count of defects). To learn more, check out this article about using smaller, less intensive partial Gage R&R approach before completing a full study. A simple activity is to do repeatability tests (half of a Gage R&R study) on 1-2 parts, to see if there is any consistency. ![]() The other reason is that the cost of performing the study may seem to be too time-consuming or expensive. First, many companies do not know much about them, so they feel that calibration is the only step necessary in order to qualify a measurement system. There are a few reasons why Gage R&R studies are not done. This will prolong the analysis, frustrate the team, and could prevent them from solving the problem. There is a good chance that the team will be confused by the variation they encounter in the Analyze phase, as they search for variation causes outside the measurement system. Think about the possible outcomes if a measurement system is not evaluated and corrected during the Measure phase of a DMAIC project. Measurement system variation is inherently built into the values we observe from a measuring instrument or device, and a high variation measurement system can completely distort a process capability (Cpk and Ppk) study, not to mention the effects of false accepts and false rejects from a quality perspective. Yet this is one of the things that gets overlooked or skipped, ensuring that the data is valid before making any improvements. This was one of the biggest “eye-opening” experiences after completing Black Belt certification and projects, that by fixing the measurement system, it often fixed the overall problem that we were trying to solve. Measurement system variation is often a major contributor to the observed process variation, and in some cases it is found to the be the primary contributor. There was no confidence that the measurement system results could be trusted. In fact, we have seen measurement systems so poor that we recommended not taking any more measurements until the issues were resolved, as it was increasing the chance of false failures and false passes. This is why calibration alone is not enough. The effects of poor accuracy and a high Gage R&R can render a measurement system useless if not addressed.Therefore, before you feel comfortable making decisions about a measurement, you should have a completed calibration AND acceptable results from a Gage R&R study. However, the thermometer itself might also be calibrated ten degrees to the low side, meaning that, on average, the thermometer will read ten degrees below the actual temperature. For example, when reading an outdoor thermometer, we might find a total Gage R&R of five degrees, meaning that we will observe up to five degrees of temperature variation, independent of the actual temperature at a given time. Measurement variation can come from many sources, and is represented by the acronym PISMOEAĪ Gage R&R study quantifies the inherent variation in the measurement system, but measurement system accuracy (more specifically referred to as bias) must be verified through a calibration process. If the variation of repeatability and reproducibility exceeds 10% of the tolerance width (upper limit – lower limit), then it is recommended to make improvements in the measurement system to reduce the variation. Each employee will measure each item multiple times ( repeatability), and their average measurement of each item will be compared to the average for the operators or measurement tools ( reproducibility). A specific type of measurement system analysis ( MSA), using 2 or 3 operators (or measurement tools), 5 to 10 parts or items, and 2 to 3 repeat measurements.
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