If measurements are used to guide decisions, then it follows logically that. in the decisions based on those measurements.

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1 MSA Measurement System Analysis If measurements are used to guide decisions, then it follows logically that the more error there is in the measurements, the more error there will be in the decisions based on those measurements. The purpose of Measurement System Analysis is to qualify a measurement system for use by quantifying its accuracy, precision, and stability. Course Objectives To explain various sources of measurement system uncertainty. To conduct measurement system studies including assessment of linearity, stability, repeatability, and reproducibility. To define ways to improve measurement systems. To understand and implement a gage management and calibration system.

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3 Analyzing Measurement System Variation A comprehensive study of measurement system analysis involves a look at the accuracy, repeatability, reproducibility, stability and linearity. In Unit 2, we'll look at calibrating gages to address concerns regarding gage accuracy. In this unit, we'll look at other types of measurement uncertainties and how to measure gage repeatability, reproducibility, stability and linearity.

4 Measurement Uncertainties The analysis of a measurement system involves understanding the uncertainties associated with taking a particular measurement, and then, where possible, quantifying those uncertainties. In this lesson we will look at uncertainties that can't be quantified and uncertainties that can be quantified. For those uncertainties that can be quantified, we'll then look at how to determine gage linearity, gage stability, and gage repeatability and reproducibility.

5 Type A Evaluation of Uncertainty We will explore the uncertainties associated with measurement systems. These include accuracy, repeatability, reproducibility, linearity, and stability, commonly known as Type A uncertainties. This term comes from the NIST interpretation of the ISO Guide to the expression of uncertainty in measurement (or GUM for short). NIST designates a Type A Evaluation of Standard Uncertainty as one that involves the use of statistical methods.

6 Type B Evaluation of Uncertainty There are a number of other uncertainties associated with measurement systems that cannot be evaluated as readily by statistical methods. Some of these include temperature related uncertainties, part form differences, fixture variations, and part conditioning variations. These are termed Type B uncertainties because they are evaluated by a NIST-designated Type B Evaluation of Standard Uncertainty. A Type B evaluation of uncertainty is typically based on engineering and scientific judgment and not on statistical methods.

7 Temperature-Related Uncertainties Let's look at some of the Type B uncertainties before we get into statistical methods for evaluating Type A uncertainties. Temperature variations can wreak havoc on part measurements. Materials expand and contract based on their coefficients of thermal expansion. For very precise measurements, even handling a part by hand can heat it up slightly and add to the uncertainty of the measurement.

8 Standardize Temperature Our best bet with temperature related uncertainties is to control the temperature differences. A stable, controlled environment will bring the measurement system variation due to temperature close to zero. The reference standard for the room temperature is 68 degrees Fahrenheit, or 20 degrees Celsius. To control the temperature, we should have the parts, gages, and any fixtures stabilized at the same temperature before measurements are made. Or if the shop temperature varies significantly over the course of the day (for example, this might be the case for plants that operate 24-hours per day), then perhaps a special area that has a controlled environment should be set up

9 Part Form Errors Minor errors in part form also adds measurement uncertainty. For example the parallelism of two surfaces may cause the measurement device not to seat properly which would give us an erroneous measurement. Similarly, concentricity issues lead to uncertainty in our measurements. But perhaps the biggest uncertainty related to part form comes from burrs in metal cutting applications or parting lines in plastic molding applications. Even minor burrs or parting lines can prevent the gage from seating properly.

10 Fixture Variations If you have to use a locating fixture to hold a part for measurement, then there are also measurement uncertainties that go hand in hand with the fixture. The fixture may have been constructed so that the part doesn't seat exactly the same each time. Or the fixture may not be rigid enough. Or it may be mounted close to a large machine that vibrates. The fixture may not be cleaned properly. The fixture may be worn in the regions used most frequently. Or if you build a fixture each time you need to hold a part for measurement, the components may not go together this time exactly as they did last time. One or all of these issues may add variation to our measurements.

11 Part Conditioning Variations Some parts need to be conditioned prior to being measured. For example, a part may need to be exposed to certain temperatures and environmental conditions before measurements are made. Slight differences in exposure time or temperature may lead to differences in part dimensions. In addition, differences in chemical concentrations or UV intensity could also change parts and lead to measurement related errors.

12 Guide to Expression of Uncertainty The areas we've just discussed in the last few screens are normally evaluated by Type B, or non-statistical, evaluations of uncertainty. Estimates of measurement system variation for these may be based on input from such sources as previous measurement data, manufacturer's specifications, or other reference material. For more information on Type B evaluation of uncertainties, see the ISO Guide to the Expression of Uncertainty in Measurement.

13 Type A Evaluation of Uncertainty Let's switch gears now and look at Type A evaluations of uncertainty. We'll look at how to statistically analyze linearity, stability, repeatability, and reproducibility.

14 GR&R Studies We'll look closely at gage repeatability and reproducibility studies, called GR&R for short. GR&R studies will look at both the variation inherent in the gage itself and the variation that arises from the tester taking measurements. These are often the two largest sources of measurement system uncertainty.

15 Sources of Variation Sources of measurement system variation can be grouped into: the gage or equipment, the environment or test conditions, the people or the testers, the test methods, and the test specimen. Remember that everything varies and that can lead to variation in measurement readings.

16 Test Equipment Even results from the best gages exhibit some variation. When this variation is too great, we cannot be sure that the product we're measuring is actually suitable. The variation from the test equipment is called repeatability.

17 Measurement System Variation from the Environment In some cases, there is repeatability variation because measurements are made in different environments. For example, a metal part that is measured at 9:00 on a summer morning in an un-air-conditioned facility will measure larger if measured again at 5:00 that afternoon.

18 Repeatability A good way to think about repeatability is that it is the variation we get when we measure the same part a number of times with the same gage.

19 People & Methods Sometimes the measurement we make also depends upon who is taking the measurement and the method they use. There can be a great deal of measurement error when different people make measurements. The variation in measurements between people is called the reproducibility.

20 Reproducibility Reproducibility variation is the uncertainty, or variation, we get between people who are measuring the same parts with the same gage. Many times this is the largest source of measurement system variation.