The term “measurement system analysis,” or MSA for short, refers to a variety of methods that evaluate the effectiveness of a manufacturer’s measurement system. MSA, in its most basic form, requires an experiment to be conducted in order to identify any variance in a measuring procedure. The processes of measurement consist of a number of measurements, such as gages or software, and a number of potential sources of variation, such as human and environmental factors.
The experiment that is carried out in the course of the MSA will examine every facet of the process, including the test method, any measuring instruments that are utilized in the process, and every approach that is used to acquire measurements.
The analysis of the measurement system should aim to maintain the reliability of the data collection process as well as the data itself as its primary objective. For accurate data analysis and effective decision-making in the future, the incorporation of MSA into the six sigma approach and quality management is essential. Companies are better informed about the ramifications of any measurement error with the knowledge that is provided by the analysis, which drives decisions about individual products and processes.
Measurement and Systems Analysis Foundational Concepts
When conducting an examination of a measurement system, the first thing that needs to be determined is whether or not the appropriate measurement is being applied to the system. Does the strategy make sense when all of the possible factors are considered? Immediately after this comes the evaluation of the instrument being used for the measurement. A lot of the time, measuring tools like gages and fixtures wear out or fail, which reduces their accuracy and makes them less useful. The MSA will decide whether a measuring tool or equipment needs to be replaced, updated, or calibrated in each given situation.
In addition, the personnel’s capacity to properly carry out the instructions for the measurement system, as well as any environmental circumstances that can influence the process, will be evaluated as part of the analysis of the measurement system. Any deviations from the standard operating procedure could potentially provide skewed results, which would therefore result in defective products. The MSA’s mission is to pinpoint these variations and eliminate the possibility of their occurrence.
In the end, the analysis of the measurement system will calculate all of this fluctuation in order to evaluate whether or not the existing measurement system requires an upgrade. In spite of the fact that there are a variety of instruments and methods that can be utilized to finish an MSA, such as calibration studies or destructive testing analysis, the process for a Gage R&R is going to be the focus of this article.
Example to Illustrate Measurement System Analysis
A company that specializes in temperature control has a software application that may be instructed to cut a piece of metal to a length of 12 inches. Because this piece of metal will one day serve as the housing for thermal control, it is essential that the initial piece of metal be measured precisely at each and every step of the process. This company has developed a measuring system as a component of its quality assurance program. As part of this system, line operators will occasionally remove bits of metal from the production line in order to measure them using a digital length gauge. This contributes to the machine’s capacity to cut the metal with greater precision as a result.
However, how do these operators know that they can trust the digital length gauge that they are using? In this particular scenario, the business makes the decision to carry out a Gage Repeatability and Reproducibility Study (Gage R&R).
Step 1: Determine the type of data collection to use
In this instance, the manufacturing company is interested in determining whether or not there is any difference in the measurements of each individual piece of metal. This type of data is known as variable data, and it indicates that the possibility of having measurements that differ between samples exists.
Step 2: Collecting Representative Samples and Selecting Operating Procedures
The next thing that needs to be done is to gather a sampling of the sheet metal at random at some point throughout the production run. It is imperative that at least ten samples be collected. After the samples have been chosen at random, you should next seek out three operators who are familiar with the measurement system process and invite them to take part in the study. Before the study begins, the sampled pieces of sheet metal are tagged with their respective lengths. However, the operators are unaware of these labels being applied to the sheet metal pieces.
Step 3: Process of Measuring:
In this particular illustration, the random selection contains ten different enclosures made of sheet metal. The sample casings are going to be measured by each operator, and their data is going to be recorded. There will be a total of thirty measurements taken, with each operator taking three separate readings from the same random selection of 10 sheet metal cases. In the final step, the organizer of the study will shuffle the sample set among the several operators in order to eliminate any possibility of bias.
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Final Step Calculations:
The organizer of the study will do a comparison of each set of measurements to three different evaluation regions once the operators have finished all three rounds of measurement. To begin, the organizer will evaluate each measurement in relation to a baseline value. Second, the organizer is going to compare the measurements that each operator has submitted throughout each of the three rounds, which is effectively the same thing as comparing each operator to themselves. This type of variation is referred to as “inside.” In the final step, the organizer will compare the measures taken by each operator with those taken by the other appraisers. The term for this kind of variation is “among.”
When the operator compares the measurements for each variation, they are on the lookout for any possible measurement errors. If there is a significant amount of variation within the ‘within’ range, it is quite likely that the method the operator employs to measure the sheet metal casings is not consistent. If there is a large amount of difference among the operators, then there is probably inconsistency in the training that was given to them on how to measure the sheet metal casings.
As soon as the organizer has finished comparing the different measures of variance, they will begin the process of calculation to determine the following information:
- Readings on average for each of the operators
- The operator-specific standard deviation for each variation from each operator’s average and the standard deviation of their numbers
At this point, the event organizer is taking a look at how the data is broken down. If all of the values add up to a value that is relatively close to the mean that is wanted, which in this example is twelve inches, then the operator, the measurement method, and the measurement tools are all functioning correctly. This is known as accuracy, and it typically indicates that everything is proceeding as it should.