Aiag Measurement System Analysis Manual Attribute Gauge
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Aiag Measurement System Analysis
Measurement systems are routinely analyzed using traditional gage. But another form of this tool—the attribute gage R&R—can improve process. The Automotive Industry Action Group's (AIAG) Measurement System Analysis text. But a spreadsheet was not required because the data could also be analyzed manually. AIAG MEASUREMENT SYSTEM ANALYSIS MANUAL ATTRIBUTE GAUGE measurement system analysis do you have continuous data or attribute data.
(November 2011) A measurement systems analysis ( MSA) is a thorough assessment of a measurement process, and typically includes a specially designed that seeks to identify the components of variation in that measurement process. Just as processes that produce a product may vary, the process of obtaining measurements and data may also have variation and produce incorrect results. A measurement systems analysis evaluates the, and the entire process of obtaining measurements to ensure the integrity of data used for analysis (usually quality analysis) and to understand the implications of measurement error for decisions made about a product or process. MSA is an important element of methodology and of other.
MSA analyzes the collection of equipment, operations, procedures, software and personnel that affects the assignment of a number to a measurement characteristic. A measurement systems analysis considers the following:. Selecting the correct measurement and approach.
Assessing the measuring device. Assessing procedures and operators. Assessing any measurement interactions. Calculating the of individual measurement devices and/or measurement systems Common tools and techniques of measurement systems analysis include: calibration studies, fixed effect ANOVA, components of variance, attribute gage study, gage R&R, and destructive testing analysis.
The tool selected is usually determined by characteristics of the measurement system itself. An introduction to MSA can be found in chapter 8 of Doug Montgomery's Quality Control book. These tools and techniques are also described in the books by Donald Wheeler and Kim Niles. Advanced procedures for designing MSA studies can be found in Burdick et.
Contents. Factors affecting a measurement process Factors might include:. Equipment: measuring instrument, fixturing. People: operators, training, education, skill, care. Process: test method,. Samples: materials, items to be tested (sometimes called 'parts'), sample preparation.
Environment:, conditioning,. Management: training programs, system, support of people, support of quality management system. These can be plotted in a 'fishbone' to help identify potential sources of measurement variation.
Goals The goals of a MSA are:. Quantification of measurement uncertainty, including the accuracy/bias, precision including and, the stability and linearity of these quantities over time and across the intended range of use of the measurement process. Development of improvement plans, when needed. Montgomery, Douglas C.
Introduction to Statistical Quality Control (7th ed.). John Wiley and Sons.
Wheeler, Donald (2006). EMP III: Evaluating the Measurement Process & Using Imperfect Data. Niles, Kim (2002). Characterizing the Measurement Process in iSixSigma Insights Newsletter, Vol. Burdick, Richard K.; Borror, Connie M.; Montgomery, Douglas C. Design and Analysis of Gauge R&R Studies: Making Decisions with Confidence Intervals in Random and Mixed ANOVA Models.
AIAG (2010). Measurement Systems Analysis, MSA (4th ed.). Automotive Industry Action Group. External links.
The method given by the AIAG MSA manual has the following strict requirements for data collection:. There must be a known reference value for each part. You must have at least 8 parts with reference values that are approximately equidistant intervals.
The minimum and maximum values must represent the process range. Each part must be measured by the gage m=20 times. The smallest part must have a number of acceptance = 0; the largest part must have a number of acceptance = 20; and the remaining must have a number of acceptance greater than or equal to 1 and less than or equal to 19.
If you specify the lower tolerance limit, the part with the lowest reference value must have 0 acceptances and the part with the highest reference must have the maximum number of possible acceptances. For the lower limit, as the reference values increase, the number of acceptances increases. If you specify the upper tolerance limit, the part with the lowest reference value must have the maximum number of possible acceptances and the part with the highest reference value must have 0 acceptances. For the upper limit, as the reference values increase, the number of acceptances decrease.