Measurement Systems Analysis (MSA) in Quality Improvement

Bias (Multiple Parts, One Equipment, Multiple Appraisers)

The BIS.Net Team The BIS.Net Team

This model has been applied to Gauge R& R studies to obtain information about repeatability (same operator) and reproducibility (different operators). However, it is an inefficient use of technology if applied directly on measurements taken from multiple variable parts....

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Bias Analysis (Multiple Equipment, One Appraiser, One Part)

The BIS.Net Team The BIS.Net Team

Bias is usually studied one equipment (instrument, device) at a time. However, sometimes there are multiple devices available to the appraiser all the same type and model.

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Nested Gauge R&R (Multiple Equipment, Multiple Appraisers, One Part)

The BIS.Net Team The BIS.Net Team

A nested gauge R&R is performed when appraisers cannot take measurements using all the measuring devices available for the study.

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Gauge R&R (Multiple Equipment, Multiple Appraisers, One Part)

The BIS.Net Team The BIS.Net Team

There are many applications where an organization has multiple sets of measuring devices (equipment) opening the possibility of variation between the different devices, which can introduce an additional source of error to measurements.

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Gauge Bias Linearity (multiple equipment)

The BIS.Net Team The BIS.Net Team

A multiple equipment study is an extension of a single linearity study performed by one appraiser using one equipment. It is not uncommon for multiple measuring devices to be available for testing the same parts.

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Gauge Performance Curve

The BIS.Net Team The BIS.Net Team

A Gauge Performance Curve is used to determine the probability of accepting or rejecting a part whose value is equal to some reference value.

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Gauge Capability and Repeatability

The BIS.Net Team The BIS.Net Team

Gauge repeatability is a measure in terms of standard deviations of how close repeated measurements on the same part, by the same operator are. Gauge reproducibility is about close clustering of repeat measurements, not closeness to the target.

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Bias Analysis (Multiple Equipment)

The BIS.Net Team The BIS.Net Team

Bias is an important characteristic of a measuring system. It can be defined as the difference between the reference (‘actual’) value of a part and the measured value.

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Gauge R&R (One Equipment, one part, multiple Appraisers)

The BIS.Net Team The BIS.Net Team

There are several types of Gauge R&R analysis that can be performed. This option is applicable when one reference part is used for the study.

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Gauge R&R (One Equipment, multiple part, multiple Appraisers)

The BIS.Net Team The BIS.Net Team

A Gauge R&R (GRR), is used to study repeatability (by the same operator) and reproducibility (by different operators). For this application, consisting of multiple parts an Analysis of Variance is required to extract as much information as possible.

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Gauge Stability

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The objective of a Gauge Stability Study is to see how consistent repeatability, reproducibility and bias is over time. This may be achieved in several ways

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Attributes Measurement System

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Attributes measurement systems are a class of systems where outcomes are discrete, not continuous such as a weighing scale. Arguably most attributes gauges are the go-no-go type where a decision on a part is either accept or not accept....

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Gauge Bias Linearity

The BIS.Net Team The BIS.Net Team

The simple analysis assumes that bias is constant over all measurement ranges. This may not necessarily be the case. Bias may be dependent on the magnitude of the reference value and even testers.

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Simple BIAS Analysis

The BIS.Net Team The BIS.Net Team

Bias in the measurement system is the difference between the average of measured values and the actual value of a part. The actual value of the part is called the reference value. Ideally the bias should be zero.....

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Qtech International has specialized in the development of quality information systems and analytic technologies for leading manufacturing corporations for 30 years in 40 countries. Our research into machine-powered algorithms has spanned over 10 years, with foundation research being university supervised prior. Research has involved many sectors, including manufacturing, health, political campaigning, retail, finance and more.

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