Abstract:
Measurement systems analysis (MSA) is an assessment of variation contributed by the measuring system. When implementing any statistical method that relies on data, it is important to be sure that the systems that collect that data are both accurate and precise. A set of procedures, often referred to as "Gage Studies", are widely used to assess the quality of measurement systems.
Keywords:
Measurement System Analysis, MSA, Linearity, Gage R&R Studies, Repeatability, Reproducibility, Sensitivity, Stability, Precision, Acceptable
Learning Outcomes:
After undergoing this article you will be able to understand the following
1. What's Measurement System Analysis ( MSA)?
2. Why MSA is important for quality assurance?
3. What are the elements of MSA?
4. What's the characteristics of MSA?
5. How MSA is carried out?
6. What's the benefits of MSA?
7. What's the disadvantages of MSA?
8. How frequency of MSA is determined?
9. Is calibration a must for instrument used in production?
10. Strategies for Successful implementation of MSA
11. Conclusions
12. FAQs
References
1. What's Measurement System Analysis ( MSA)?
Measurement Systems Analysis (MSA) is a tool for analyzing the variation present in each type of inspection, measurement, and test equipment. It is the system used to assess the quality of the measurement system.
Measurement Systems Analysis (MSA) connects to measurement data that is used in nearly every manufacturing process. As the quality of the data improves, the quality of decisions improves.
2. Why MSA is important for quality assurance?
A measurement system tells you in numerical terms important information about the part that you measure. How sure can you be about the data that the measurement system delivers? Is it the real value that you obtain out of the measurement process, or is it the measurement system error that you see?
Measurement system errors can be costly, and can affect your capability to obtain the true value of what you measure. It is often said that you can be confident about your reading of a parameter only to the extent that your measurement system can allow.
For example, a process may have total tolerance to an extent of 30 microns. The measurement system that you use to measure this process, however, may have an inherent variation (error) of 10 microns. This means that you are left with only 20 microns as your process tolerance. The measurement system variation is eating into your process tolerance.
3. What are the elements of MSA?
The different elements of MSA are
- Attribute data – Data that can be counted for recording and analysis (sometimes referred to as go/ no go data)
- Variable data – Data that can be measured; data that has a value that can vary from one sample to the next; continuous variable data can have an infinite number of values
- Bias – Difference between the average or mean observed value and the target value
- Stability – A change in the measurement bias over a period of time
- A stable process would be considered in “statistical control”
- Linearity – A change in bias value within the range of normal process operation
- Resolution – Smallest unit of measure of a selected tool gage or instrument; the sensitivity of the measurement system to process variation for a particular characteristic being measured
- Accuracy – The closeness of the data to the target or exact value or to an accepted reference value
- Precision – How close a set of measurements are to each other
- Repeatability – A measure of the effectiveness of the tool being used; the variation of measurements obtained by a single operator using the same tool to measure the same characteristic
- Reproducibility – A measure of the operator variation; the variation in a set of data collected by different operators using the same tool to measure the same part characteristic
4. What's the characteristics of MSA?
The characteristics of MSA are two types
1. Static characteristics
2. Dynamic characteristics
Brief explanation of characteristics are as follows
1. Static characteristics
Precision
Precision is a measurement of how close a series of measurements(different samples) are to one another, irrespective of the actual/true value.
You can evaluate the precision of measurement by comparing two or more repeated values of measurement.
For example: if you measure your height 4 times and get 6′ 1” each time then your measurement is very precise.
Accuracy
Accuracy is the evaluation of how close a measured quantity comes to the actual or true value.
To evaluate the accuracy of a measurement, the measured value must be compared to the true or standard value.
For example: if you know your actual height is exactly 6’1” and you measure your height 6’1” again with a measuring tape then your measurement is accurate.
2. Dynamic characteristics
Sensitivity
Sensitivity is the smallest absolute amount of change that can be detected by measurement within a given resolution. It is often expressed in terms of millivolts, micro ohms or 10th of a degree.
For example: For a voltmeter that ranges between 0-15 volt with 5 divisions in a single reading (0-5 volt) will have a sensitivity of 0.5 volts.
Repeatability
Repeatability is the variation in measurements obtained when one person measures the same unit (test material) with the same method on the same condition in a short period of time.
Reproducibility
The proximity between the results of measurement when a particular test material is measured by different personnel with different methods using distinct instruments under various conditions, location and times is known as Reproducibility.
Calibration
Calibration is the process of evaluating and adjusting the precision and accuracy of the measuring instrument by comparing it with known standard measurement.
Interchangeability
Interchangeability is the condition when one part or component get assemble perfectly with the other one while both are selected randomly, satisfying the functionality of the assembled good.
Interchangeability is possible when certain standards are followed like having a similar drawing for a particular component.
Amplification
The electronic method of magnification that is ideally suitable for the processing of signals is called amplification.
Drift
Drift is an undesirable gradual deviation of the instrument output over a period of time caused by wear and tear and high stress over some parts.
Resolution
Resolution is the ability of an instrument to read the smallest dimension of measurement. The higher the resolution, the smaller the measurement it can measure.
5. How MSA is carried out?
MSA is a collection of experiments and analysis performed to evaluate a measurement system’s capability, performance and amount of uncertainty regarding the values measured.
During an MSA activity, the amount of measurement uncertainty must be evaluated for each type of gage or measurement tool defined within the process Control Plans. Each tool should have the correct level of discrimination and resolution to obtain useful data. The process, the tools being used (gages, fixtures, instruments, etc.) and the operators are evaluated for proper definition, accuracy, precision, repeatability and reproducibility.
For gages or instruments used to collect variable continuous data, Gage Repeatability and Reproducibility (Gage R & R) can be performed to evaluate the level of uncertainty within a measurement system. To perform a Gage R & R, first select the gage to be evaluated. Then perform the following steps:
- Obtain at least 10 random samples of parts manufactured during a regular production run
- Choose three operators that regularly perform the particular inspection
- Have each of the operators measure the sample parts and record the data
- Repeat the measurement process three times with each operator using the same parts
- Calculate the average (mean) readings and the range of the trial averages for each of the operators
- Calculate the difference of each operator’s averages, average range and the range of measurements for each sample part used in the study
- Calculate repeatability to determine the amount of equipment variation
- Calculate reproducibility to determine the amount of variation introduced by the operators
- Calculate the variation in the parts and total variation percentages
6. What's the benefits of MSA?
MSA can provide several benefits for quality improvement, such as improving the accuracy and reliability of measurements, detecting and correcting measurement problems, enhancing the credibility and validity of data, and supporting the analysis and improvement of processes.
It evaluates the reliability of the measurement system in statistical and scientific way.
Since a scientific approach is used to evaluate the measurement system, it is
- possible to implement this methodology to assess the correctness of the measurement system.
- The method is not only applicable continuous data but also to discrete data type.
- By analyzing the existing system in an unbiased and low erroneous way, it is then possible to compare the existing system with its’ future state in a reliable way.
- MSA is scientifically reliable and not dependent on any subjective measuring error or bias.
7. What's the disadvantages of MSA?
MSA can present certain challenges, such as selecting the right MSA method and design, which can depend on the type and level of data, the number and type of operators, the number and type of parts, and the measurement system characteristics.
The drawbacks of the measurements that have been identified are: 1) Cost 2) Error 3) Modification of the measured object (and even of the measure itself) 4) Unwanted side effects 5) Misinterpretation 6) Invisibilization.
8. How frequency of MSA is determined?
You could perform an MSA at the start of each shift, or each lot of parts, or once a week, or once a month, every 3 months, whatever. If the MSA fails, your containment lot size of possible affected parts is all of the parts made since the last MSA.
9. Is calibration a must for instrument used in production?
Over time, the tool may start to wear out and experience a decrease in performance. So, it is important to calibrate measuring instruments to maintain the function and performance of the tool. In addition, calibration is also required for product and equipment quality management. This is in accordance with ISO 9000.
10. Strategies for Successful implementation of MSA
The focus of the executives should be to understand the focus of the exercise and interpretation of the results.
The top strategies are the following
Step 1: Plan the Study
Confirm the Key Measures: Any process consists of a large number of measures. The sum total of these measures is called the measurement system. Just like there are many inputs, but only few of them are vital and have any meaningful impact on the outputs, similar is the case with measures.
Develop Operational Definition: The next step must be to come up with exact operational definitions for these measures. Since an entire analysis is going to be conducted on these measures, they must be standardized. The operational definition helps eliminate any ambiguity.
Step 2: Conduct the Study
Determine Number of Measurement Trials: The optimum number of trials that are enough to decide whether a measure is appropriate or not must be decided. This can be considered analogous to the sample size that is used while conducting experiments.
Determine Organization of Trials: Trials can be collected at the same time or at different times of the day. The frequency, with which the trials are collected, plays a vital role in the analysis. Therefore it must be carefully decided.
Different Operators: The study must be designed to negate the influence of a specific operator. Hence data must be collected from different people.
Different Equipment: The study must also negate the influence of specific machines. For instance, if a machine is newer than the other, it may function better as compared to the other. Such influences must be negated to reach the true measurement variation.
Different Conditions: The study must be planned in such a way that trials must be conducted in as varied circumstances as possible. The whole point of this is to negate the influence of any specific factor and bring to the forefront the true measurement error.
Step 3: Analyse the Results
Once the study has been conducted, the next step is to analyse the result to see if they are as per your requirements. Whether the measurement system is good enough is a question that depends upon the intended usage of the measurements. In case the measurements are going to be used for precision engineering, they need to be highly accurate.
According to analysis determine variation acceptance as per standards like
Below 10 % variation - acceptable
Below 30% variation but more than 10% - may be acceptable or may not be acceptable
Step 4: Fix Measurement System
It is difficult to state a generalized way to fix measurement system errors. However, since we know that most of the process variation is caused by a handful of factors, we can try fixing them to fix the measurement system.
Change Equipment: Most of the times, wrong measurements are the result of faulty systems. This means that if people are using manual means of collecting data, the organization must consider automating it. It could also mean that the measurement systems are old and faulty and need to be replaced. Depending upon the need of the management and the budget it is willing to expend, the measurement system can be fixed.
Train Operators: In many cases, the measurements are taken manually. This could be either because of the nature of the process. Alternatively it could be because of the management’s reluctance to invest in automation. In this case, the operators must be trained to cut down on the error.
Further Analysis: If results reveal that neither the operators nor the machine are responsible for the dismal performance of the measurement systems, then in that case further analysis must be deployed till the problem is found and fixed.
11. Conclusions
Measurement System Analysis uses techniques to understand the variation within measuring equipment. For example, it is the variation brought into the equipment by people and by the environment. There are some useful methods for estimating how much error is due to gage and also by an individual.
Without effective measurement systems in place, segregating conforming product becomes more difficult and process controls, process capability and improvement activities may be rendered ineffective.
Measurement systems and associated test equipment need to be suitable for use and capable of producing valid results. Measurement systems can include your data collection methods, work procedures, operators and test equipment.
A measurement systems analysis (MSA) assesses the precision, consistency, and bias of a measurement system.
12. FAQs
Q. Why MSA?
Ans.:
A measurement system tells you in numerical terms important information about the part that you measure. How sure can you be about the data that the measurement system delivers? Is it the real value that you obtain out of the measurement process, or is it the measurement system error that you see?
Measurement system errors can be costly, and can affect your capability to obtain the true value of what you measure. It is often said that you can be confident about your reading of a parameter only to the extent that your measurement system can allow.
Q. How does MSA differ from calibration?
Ans.:
Calibration is a process to compare the measuring instrument against standards of known value and uncertainty, and correct the difference if any. Calibration is done under controlled conditions and by specially trained personnel.
However on the shop floor, where these instruments are used, the measurement process is affected by many different factors such as method of measurement, appraiser’s influence, environment and the method of locating the part. All these can introduce variation in the measured value. It is important we assess, measure and document all the factors affecting the measurement process, and try to minimize their effect.
Q. When should MSA be applied ?
Ans.:
MSA can be applied on these conditions:
There is a new manufacturing process
There is a new product to manufacture
There are customer concerns
There are internal quality issues
There is a change in process capability
There is a change in skill level
The study will aim to identify the elements of the total process variation which is due to the measurement system and the element which is due to actual part variation.
Q. How will MSA benefit my organisation?
Ans.:
MSA helps reduce both the type of risks associated with measurement of a process and making decisions, the risk of False Alarm and the risk of Missed Opportunities.
References
1. Measurement Systems Analysis (MSA), 4th Edition, 1st Printing, June 2010 31.4 KB
3. Montgomery, Douglas C. (2013).
Introduction to Statistical Quality Control (7th ed.). John Wiley and Sons.
ISBN 978-1-118-14681-1.
4. Wheeler, Donald (2006).
EMP III: Evaluating the Measurement Process & Using Imperfect Data. SPC Press.
ISBN 978-0-945320-67-8.
5. Niles, Kim (2002).
Characterizing the Measurement Process in iSixSigma Insights Newsletter, Vol. 3, #42.
ISSN 1530-7603.
6. Burdick, Richard K.; Borror, Connie M.; Montgomery, Douglas C. (2005).
Design and Analysis of Gauge R&R Studies: Making Decisions with Confidence Intervals in Random and Mixed ANOVA Models. SIAM.
ISBN 978-0-898715-88-0.
7. AIAG (2010).
Measurement System Analysis, MSA (4th ed.). Automotive Industry Action Group.
ISBN 978-1-60-534211-5
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