Guide: Gage R&R
Gage R&R, standing for Gauge Repeatability and Reproducibility, forms a key technique of the Six Sigma methodology, a disciplined approach focused on eliminating defects and variability in processes. In industries where precision is paramount, the accuracy and reliability of measurement systems are critical.
Gage R&R is a key statistical tool designed to evaluate and ensure the effectiveness of these measurement systems. It aims not just to measure but to illuminate the variability within the system, encompassing both the measuring instrument and the human operators. By quantifying this variability, Gage R&R helps distinguish between true process variation and measurement error, providing a clear view of the process’s actual performance. This distinction is vital, as it impacts the accuracy and reliability of data used for decision-making in Six Sigma processes.
Table of Contents
What is Gage R&R
Gage R&R stands for Gauge Repeatability and Reproducibility, is an important part of the Six Sigma methodology, which is a disciplined and data-driven approach to eliminating defects and variation in processes. Gage R&R is a key method in manufacturing and industrial businesses, where precise measurements are important, and the reliability and accuracy of measurement systems play an important role.
In Short, Gage R&R is a statistical tool designed to evaluate the effectiveness of measurement systems.
Purpose of Gage R&R
The primary use of Gage R&R is to be able to quantify the amount of variability within the measurement system itself. When collecting data on the amount of variation in the output of a process, there are two key points to consider. The true process variation and the amount of variation observed, there can often be a gap between these with poor measurement systems.
The method does not only consider the measuring instrument or gauge but also the human factor, e.g. the operators handling the measurement instrument or gauge.
This method aims to analyze and understand different sources of variation in measurements and distinguish between inherent process variation and measurement error.
By Isolating the variability that can be introduced by the measurement system from both the gauge and operator, Gage R&R is able to provide insights into how much total process variability is due to the measurement system rather than the process itself. Understanding this and being able to address guage variation is key in Six Sigma to ensure accurate data collection and analysis is used in decision-making processes with accurate and reliable data.
Components of Gage R&R
Gage R&R, a critical tool in Lean Six Sigma, is composed of two main components: repeatability and reproducibility. These components help in understanding and quantifying the variability in the measurement system.
Repeatability is the variation in measurements taken by the same operator using the same measuring instrument under the same conditions over multiple trials. It essentially evaluates the consistency of the measurement instrument.
Key aspects include:
- Consistency Over Time: It checks if the instrument gives consistent results over a period.
- Instrument Precision: It helps in assessing the precision of the gauge. A high degree of repeatability indicates a precise instrument.
- Operator’s Consistency: Even though it’s the same operator, this aspect also subtly checks the operator’s ability to consistently use the instrument in the same manner.
Reproducibility, in contrast, looks at the variation in measurements when different operators use the same gauge under similar conditions.
It involves human factors in the measurement process, such as:
- Operator Differences: Different operators may have slightly different techniques or interpretations when using the same gauge.
- Training and Experience: The level of training and experience of each operator can influence reproducibility.
- Human Error Variability: It helps in quantifying how much of the measurement variability is due to human error or differences.
Conducting a Gage R&R Study
Step 1: Selection of Parts, Operators and Trials
The first step of a Gage R&R study involves identifying parts, operators and trials.
The goal is to select a range of parts collectively representing the entire range of measurements the process encounters. It is important to include parts at the lower and upper limits of the tolerance range, as well as those in the middle. Importantly, the parts chosen should represent typical parts processed, not outliers or exceptions.
For example, you might end up with a set of 15 piston rods for the Gage R&R study:
- 5 rods at the lower tolerance limit (e.g., length at 99.5 mm for a tolerance range of 99.5 mm to 100.5 mm)
- 5 rods in the middle of the tolerance range (e.g., length at 100 mm)
- 5 rods at the upper tolerance limit (e.g., length at 100.5 mm)
This selection ensures that the Gage R&R study will provide insights into the measurement system’s performance across the full spectrum of parts that are typically encountered in the production of piston rods
By including parts from the lower, middle, and upper ranges of the tolerance spectrum, the study will accurately reflect the measurement system’s capability in real-world production scenarios.
When selecting operators, you should consider operators that have different levels of experience and skill. This is to best replicate the real-world scenario where different individuals interact with the measurement system. Ideally, operators who are regular users of the gauge should be selected to ensure the study reflects routine conditions.
For the study, you should also decide on the number of measurements each operator will take on each part. This number should be sufficient to capture variability without being excessive.
This number could differ from study to study, but the balance should be between the number of trials necessary for detailed data and the practical limitations of time and resources.
Step 2: Data Collection
The data collection step is the measurement process. A facilitator of the study should set out the parts in a randomized order, and each operator should measure each part multiple times. This should be done in such a way that the operator will not remember the measurement of the part the first time and just repeat the same measurement from memory.
These measurements should also be taken under conditions that closely resemble the actual production environment. For example, if it is a production line that only allows 5 seconds to take a measurement, the simulated process should have the same time limitation. Other conditions include using the same measurement equipment, the same level of lighting, and even PPE if that impacts the ability to take measurements.
When the measurement process is being conducted, there should be an independent facilitator keeping an accurate record of each measurement result and which part the result corresponds to. This data will form the basis of the entire study.
For this, we should ensure to record the data in a way clear analysis can be conducted later such as using a table like the one below:
|Part ID||Trial Number||Operator A Result||Operator B Result||Operator C Result||Appraised Measurement|
Step 3: Data Analysis
Next, we need to conduct a statistical analysis using either statistical software or manual methods to decompose the total observed variation. This decomposition usually identifies the variation due to the differences between parts, the variation due to the measurement device’s repeatability, and the variation due to different operators which is reproducibility.
This step should help to identify what are the key elements of the measurement system that are contributing the most to the overall variability.
Further analysis can be done to calculate the percentage of total process variability attributable to the measurement system. This involves comparing the gauge and operator variability to the total variability observed.
Based on these percentages, an understanding can be made about the adequacy of the measurement system. The higher the percentage indicates a need for improvement in the gauge, the measurement process or operator training.
Interpreting Gage R&R Results
Gage R&R results provide insight into the reliability and accuracy of a measurement system. These results are typically expressed as a percentage, representing the proportion of the total process variation attributed to the measurement system. This variation is divided into repeatability (variation due to the measurement instrument) and reproducibility (variation due to different operators).
Understanding the Percentage Results
- Less than 10%: If the Gage R&R percentage is below 10%, it suggests that the measurement system introduces relatively little variation compared to the total process variation. This is generally considered acceptable, implying that the system is reliable and does not significantly contribute to the overall process variability.
- Between 10% and 30%: A Gage R&R result in this range suggests a moderate level of variation due to the measurement system. In this scenario, the acceptability of the system depends on the specific requirements of the process and the criticality of the measurements. For example, in highly precise manufacturing processes, even a small level of measurement system variation might be unacceptable. Conversely, in less critical applications, this level of variation might be tolerable.
- Over 30%: This result indicates that a significant portion of the process variability is due to the measurement system. Such a high level of variation is generally considered unacceptable, indicating that the measurement system is unreliable and could lead to incorrect conclusions about the process.
Based on the findings from the Gage R&R study, specific actions can be undertaken to enhance the measurement system:
Training for Operators
- Consistency and Technique: Operators might need training to standardize their measurement techniques. Inconsistent methods among different operators can lead to significant reproducibility issues.
- Awareness of Best Practices: Training sessions can also include best practices for handling and using measurement instruments, which can reduce operator-induced variability.
Calibration or Maintenance of Gauges
- Regular Calibration: Regular calibration of the measuring instruments is crucial to maintain their accuracy over time.
- Preventative Maintenance: Routine maintenance can help in identifying and correcting issues before they affect measurement accuracy.
Revising Measurement Procedures or Replacing the System
- Procedure Review: Sometimes, the measurement procedure itself might be flawed or outdated. Reviewing and updating these procedures can help in reducing measurement variability.
- System Replacement: In cases where the measurement system is fundamentally unreliable (as indicated by a high Gage R&R percentage), replacing it with a more accurate system might be necessary.
Gage R&R studies offer invaluable insights into the measurement system’s contribution to overall process variability. By interpreting these results, organizations can determine the reliability and adequacy of their measurement systems. A low Gage R&R percentage indicates a reliable system, while higher values signal the need for improvement. Addressing these findings through operator training, gauge calibration, or procedure revisions ensures the collection of accurate and reliable data.
Ultimately, Gage R&R not only underscores the importance of precision in measurement systems but also guides continuous improvement efforts in Six Sigma practices. By isolating and addressing measurement system variability, organizations can make more informed decisions, leading to enhanced process control and product quality.
- Peruchi, R.S., Balestrassi, P.P., de Paiva, A.P., Ferreira, J.R. and de Santana Carmelossi, M., 2013. A new multivariate gage R&R method for correlated characteristics. International Journal of Production Economics, 144(1), pp.301-315.
A: Gage R&R (Repeatability and Reproducibility) is a statistical method used to evaluate the performance of a measurement system. It assesses the variation in measurements caused by different sources, such as equipment, operators, and environmental factors.
A: Gage R&R is important because it helps determine the reliability and accuracy of a measurement system. By identifying sources of variation, it allows for improvements to be made, leading to more consistent and accurate measurements. It ensures that the measurement system is capable of providing reliable data for decision-making.
A: Gage R&R focuses specifically on the measurement system’s performance and variation. While other statistical analyses may assess product or process variability, Gage R&R is specifically concerned with understanding and quantifying the sources of measurement variation.
A: A Gage R&R study typically involves defining the objective, selecting the measurement system, determining the characteristics to be measured, collecting data, analyzing the data using statistical methods, and interpreting the results. Additionally, it includes identifying and addressing sources of variation and implementing improvement actions.
A: Gage R&R data is collected by measuring the chosen characteristics using the selected measurement system. The measurements are typically taken in a random order to minimize bias, and operators are often unaware of the previous results to maintain objectivity.
A: Gage R&R data is analyzed using statistical techniques such as Analysis of Variance (ANOVA) or Attribute Agreement Analysis (AAA). These methods help calculate statistics such as repeatability, reproducibility, and the percentage of total variation due to each source.
A: The acceptability criteria for Gage R&R may vary depending on the specific industry or application. In general, a Gage R&R value below 10% of the tolerance range is often considered acceptable. However, it is important to establish appropriate criteria based on the specific requirements of the measurement system and the associated processes.
A: It is recommended to conduct a Gage R&R study when implementing a new measurement system, introducing significant process changes, or suspecting issues with the current measurement system. Additionally, regular monitoring and re-evaluation should be performed to ensure the measurement system’s ongoing performance and to identify the need for any adjustments or improvements.
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