Measurement systems analysis
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A measurement systems analysis (MSA) is a specially designed experiment that seeks to identify the components of variation in the measurement.
Just as processes that produce a product may vary, the process of obtaining measurements and data may have variation and produce defects. A measurement systems analysis evaluates the test method, measuring instruments, 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 Six Sigma methodology and of other quality management systems.
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 measurement uncertainty 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, ANOVA gage R&R, destructive testing analysis and others. The tool selected is usually determined by characteristics of the measurement system itself.
Factors affecting measurement systems
Factors might include:
- Equipment: measuring instrument, calibration, fixturing, etc.
- People: operators, training, education, skill, care
- Process: test method, specification
- Samples: materials, items to be tested (sometimes called "parts"), sampling plan, sample preparation, etc.
- Environment: temperature, humidity, conditioning, pre-conditioning,
- Management: training programs, metrology system, support of people, support of quality management system, etc.
These can be plotted in a "fishbone" Ishikawa diagram to help identify potential sources of measurement variation.
ASTM has several procedures for evaluating measurement systems and test methods, including:
- ASTM D4356 Standard Practice for Establishing Consistent Test Method Tolerances
- ASTM E691 Standard Practice for Conducting an Interlaboratory Study to Determine the Precision of a Test Method
- ASTM E1169 Standard Guide for Conducting Ruggedness Tests
- ASTM E1488 Standard Guide for Statistical Procedures to Use in Developing and Applying Test Methods
- ASTM E2782 Standard Guide for measurement systems analysis
ASME has several procedures and reports targeted at task specific uncertainty budgeting and methods for utilizing those uncertainty estimates when evaluating the measurand for compliance to specification.
- B18.104.22.168 - 2001 Guidelines for Decision Rules: Considering Measurement Uncertainty Determining Conformance to Specifications
- B22.214.171.124 - 2007 Guidelines for the Evaluation of Dimensional Measurement Uncertainty (Technical Report)
- B126.96.36.199 - 2002 Guidelines for Assessing the Reliability of Dimensional Measurement Uncertainty Statements
The measurement systems analysis process is defined in a number of published documents including the AIAG's MSA manual, which is part of a series of inter-related documents the AIAG controls and publishes. The Automotive Industry Action Group (AIAG) is a non-profit association of automotive companies founded in 1982. These manuals include:
- The failure mode and effects analysis (FMEA) and Control Plan manual
- The statistical process control (SPC) manual
- The measurement systems analysis manual
- The production part approval process (PPAP) manual
- Measurement uncertainty
- Accuracy and precision
- Repeatability and reproducibility
- Attribute study
- Practical examples for calculating Bias, Stability, Linearity, Repeatability and reproducibility, Attribute study