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In this article, you will be able to understand about Measurement system analysis(MSA) and how you can use it in your respective domains. You would also learn about the steps to perform MSA. And much more. Believe me, its all you need to know about MSA.

Introduction to Measurement System Analysis(MSA)

It is true that what gets measured, gets controlled but what if our measurements give false data? We would be making all decisions based on the wrong data. Wouldn’t that be a disaster?MSA comes in as a rescue.

MSA is a mathematical procedure to quantify variation introduced to a process or product by the act of measuring.

Or we can say, A measurement systems analysis (MSA) is a thorough assessment of a measurement process, and typically includes a specially designed experiment that seeks to identify the components of variation in the measurement process.

Why we do MSA?

MSA helps ensure that data being collected is accurate and the process used for data collection is appropriate. Wrong measurements will give you the wrong image of the current situation and would impact your decision making.

Wrong decisions can lead to disasters. For example, A manufacturing unit in NCR started receiving customer complaints about dimensionally non-conforming parts. Supervisor immediately checked if the operator was working as per instructions or not? To his surprise operator was working as per instruction and using prescribed gauges.

Further analysis suggested that the instrument used had a resolution less than required to detect NC parts.  An ineffective measurement system can allow bad parts to be accepted and good parts to be rejected, resulting in dissatisfied customers and excessive waste. The use of MSA could have prevented the problem and assured that accurate useful data was being collected.

Measurement System Analysis (MSA) as a Process

Measurement system analysis as a process includes certain elements.Following elements constitute MSA process.

An operator can refer to a person or can be different instruments measuring the same products.

Reference is a standard that is used to calibrate the equipment. 

The procedure is the method used to perform the test. 

Equipment is the device used to measure the product. 

The environment is the surroundings where the measures are performed.

MSA Terminology

Resolution or least count: The smallest increment, or unit of measure, available from a measurement process

Generally at least 1/10^th of the specification range.

Reference Value: The theoretically or agreed upon the correct value of the characteristic being measured, traceable to some standard

Accuracy: Accuracy describes the difference between the measurement and the actual value of the part that is measured. It includes:

• Bias: a measure of the difference between the true value and the observed value of a part. If the “true” value is unknown, it can be calculated by averaging several measurements with the most accurate measuring equipment available.
• Linearity: a measure of how the size of the part affects the bias of a measurement system. It is the difference in the observed bias values through the expected range of measurement.

Precision: A precise metric is one that returns the same value of a given attribute every time an estimate is made.

Precise data are independent of who estimates them or when the estimate is made.

Precision can be partitioned into two components:

• Repeatability: Repeatability is the variation in measurements obtained with one measurement instrument used several times by one appraiser while measuring the identical characteristic on the same part.
• Reproducibility: Reproducibility is the variation in the average of the measurements made by different appraisers using the same measuring instrument when measuring the identical characteristic on the same part.

In simplest terms, given a set of data points from repeated measurements of the same quantity, the set can be said to be precise if the values are close to each other, while the set can be said to be accurate if their average is close to the true value of the quantity being measured. In the first, the more common definition above, the two concepts are independent of each other, so a particular set of data can be said to be either accurate, or precise, or both, or neither.

Stability: The stability of a gauge is defined as the error (measured in terms of Standard Deviation) as a function of time.  Environmental conditions such as cleanliness, noise, vibration, lighting, chemical, wear, and tear or other factors usually influence gauge instability.  

Ideally, gauges can be maintained to give a high degree of Stability but can never be eliminated, unlike Reproducibility.  Gage Stability studies would be the first exercise past calibration procedures. 

Control Charts are commonly used to track the Stability of a measurement system over time.  

NDC(Number of distinct categories): The number of distinct categories tells you how many separate groups of parts the system is able to distinguish.

NDC value should not be less than 5. The formula for NDC is as follows :

NDC = (Part Variation/Gauge R and R)*1.41

Data Types

Quantitative data can be divided into two types :

• Continuous Data: Continuous data is quantitative data that can be measured. It has an infinite number of possible values within a selected range. For example, If the tolerance of a parameter is 10 +-0.1, you can have infinite output options.It could be 10.01,10.011,10.022…..etc. It further can be classified into two categories :
  • Interval: – Measured/scaled data: Each position equidistant – 0 can be relevant (temperature)
  • Ratio: – Numbers compared as multiples of one another
• Discrete Date: Discrete Data can only take certain values.Example: The number of students in a class. We can’t have half a student! It further can be classified into two categories :
  • Nominal: – Literally, “name” – Represents categories
  • Ordinal: – Ordered or ranked data – Not scaled

Types of MSA

There are two types of MSA :

• Variable MSA
• Attribute MSA

Methods of MSA Calculation 

There are three types of MSA calculation :

1. Range Method
2. Average and Range Method
3. ANOVA method

Steps of Variable MSA Calculation

Step 1: Call a team meeting and introduce the concepts of the Gage R&R

Step 2: Select parts for the study across the range of interest 

–If the intent is to evaluate the measurement system throughout the process range, select parts throughout the range 

–If only a small improvement is being made to the process, the range of interest is now the improvement range

Step 3: Identify the inspectors or equipment you plan to use for the analysis  

–In the case of inspectors, explain the purpose of the analysis and that the inspection system is being evaluated not the people

Step 4: Calibrate the gage or gages for the study 

–Remember Linearity, Stability, and Bias

Step 5: Have the first inspector measure all the samples once in random order

Step 6: Have the second inspector measure all the samples in random order 

–Continue this process until all the operators have measured all the parts one time 

–This completes the first replicate

Step 7: Repeat steps 5 and 6 for the required number of replicates 

–Ensure there is always a delay between the first and second inspection

Step 8: Enter the data in this sheet.

Step 9: Draw conclusions and make changes if necessary

How to read MSA?

MSA basically Addresses what percent of the tolerance is taken up by measurement error. MSA Includes both repeatability and reproducibility.

For this, we calculate GRR(Gauge repeatability and reproducibility).

The resulting percentage is used as a basis for accepting the gage. Following are the Guidelines for making decision-related to gauge acceptability:

If GRR is  < 10%,MSA is acceptable

If GRR is > 10% but <30%,MSA may be acceptable. It is dependent on the relative importance of the application.

If GRR is >30 %, MSA is not acceptable. It calls for action. Any actions identified to improve the measurement system should be evaluated for effectiveness

When interpreting the results of a Gage R & R, perform a comparative study of the repeatability and reproducibility values. 

If the repeatability value is large in comparison to the reproducibility value, it would indicate a possible repeatability problem. Adversely, if the reproducibility value is large in comparison with the repeatability value, it would indicate a possible reproducibility problem. 

Where to look for MSA improvement?

1. Repeatability Problems:

•Calibrate or replace gage.

•If only occurring with one operator, re-train.

2. Reproducibility Problems:

•Measurement machines

–Similar machines

•Ensure all have been calibrated and that the standard measurement method is being utilized.

–Dissimilar machines

•One machine is superior.

•Operators

–Training and skill levels of the operators must be assessed.

–Operators should be observed to ensure that standard procedures are followed.

•Operator/machine by part interactions

–Understand why the operator/machine had problems measuring some parts and not others.

•Re-measure the problem parts 

• Gage linearity could be a problem.

• It could be a fixture problem

• The problem could be poor gage design

Types of MSA Experiment Designs

• Crossed Design

•A Crossed Design is used only in non-destructive testing and assumes that all the parts can be measured multiple times by either operators or multiple machines. 

–Gives the ability to separate part-to-part Variation from measurement system Variation.

–Assesses Repeatability and Reproducibility.

–Assesses the interaction between the operator and the part.

• Nested Design

•A Nested Design is used for destructive testing and also situations where it is not possible to have all operators or machines measure all the parts multiple times.

–Destructive testing assumes that all the parts within a single batch are identical enough to claim they are the same.

–Nested designs are used to test measurement systems where it is not possible (or desirable) to send operators with parts to different locations.

Do not include all possible combinations of factors

When To do MSA?

•Study the % of the variation in our process that is caused by our measurement system.

•Compare measurements between operators.

•Compare measurements between two (or more) measurement devices.

•Provide criteria to accept new measurement systems (consider new equipment).

•Evaluate a suspect gage.

•Evaluate a gage before and after repair.

•Determine true process variation.

•Evaluate the effectiveness of training programs.

CONCLUSION

Current society is a data-driven society. Dependence on data for decision making is increasing at a tremendous pace.

In such scenarios, it becomes important that our data collection system is precise. Today manufacturing companies gather massive amounts of information through measurement and inspection. 

Making decisions on incorrect data can lead to massive issues. Hence MSA helps us collect the right data so that we can make the right decisions.

References

https://quality-one.com/msa/

https://en.wikipedia.org/wiki/Measurement_system_analysis

https://www.isixsigma.com/dictionary/measurement-system-analysis-msa/