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Process Capability
Published in Gisi Philip, Sustaining a Culture of Process Control and Continuous Improvement, 2018
Measurement systems analysis (MSA) is used to assess the quality of measurement data. It ensures data collection procedures and systems provide adequate resolution by understanding measurement bias and the influence of variation on specified tolerances. Bias is the difference between an average output measurement compared to a standard or reference value. It’s an assessment of gage accuracy where the smaller the difference between the actual versus reference value, the better (Figure 4.1).
Robust Quality for Analytics
Published in Rajesh Jugulum, Robust Quality, 2018
To obtain accurate measures, performing a measurement system analysis (MSA) is quite important. MSA is a method for evaluating how much variation is present in the process by which we collect data. It answers the question regarding the accuracy and reliability of data as well as the process of data collection. MSA also helps in evaluating the effectiveness of the data collection plan.
Gage R&R studies in measurement system analysis: A systematic literature review
Published in Quality Engineering, 2022
Williane de Oliveira Silva Soares, Rogério Santana Peruchi, Rômulo Augusto Ventura Silva, Paulo Rotella Junior
Measurement system analysis (MSA) is a set of statistical techniques that enables statistical quality control (Peruchi et al. 2014); allows the assessment of the influence of measurement errors on variations in manufacturing processes (Al-Refaie and Bata 2010); and quantifies the accuracy (Yeh and Sun 2013), precision and stability of a measurement system (MS) (Saikaew 2018). Investigating an MS is a standardized process for extracting measurements from a mensurand; it allows statistical inference on the quality characteristics and investigation of measurement error (AIAG 2010). MSs are evaluated in virtually any type of manufacturing process (Peruchi et al. 2014) through the application of statistical techniques and control logic in the measurement process (AIAG 2010). MSA helps in quantifying the capacity of a gage or measurement device to generate data that support decision-making (Majeske 2012a). Furthermore, it ensures that the variability of an MS is not significant in relation to the variation in the manufacturing process (Pereira et al. 2016). The total variation observed in a process is caused by two sources: the process itself and MS (Al-Refaie and Bata 2010). If both variations are improperly distinguished, that is, if process variations are confused with MS variations, unnecessary adjustments may be made to the process. Therefore, it is necessary to evaluate the MS and determine the degree of variability associated with measurement errors.
The use of D-optimal design in an expanded gauge R&R study
Published in Quality Engineering, 2021
The quality of measured data influences the decisions regarding the quality of production processes or products. For this reason, an important element of data evaluation is assessing whether the measurement errors are adequately small compared to the process variation or to the product specification limits. Measurement system analysis (MSA) is a set of procedures to evaluatelocation of the measurement results against a reference value (bias, linearity),variation of measurement results (repeatability, reproducibility).