Abstract
Multivariate measurement systems analysis is usually performed by designing suitable gauge R&R experiments ignoring available data generated by the measurement system while used for inspection or process control. This article proposes an approach that, using the data that are routinely available from the regular activity of the instrument, offers the possibility of assessing multivariate measurement systems without the necessity of performing a multivariate gauge study. It can be carried out more frequently than a multivariate gauge R&R experiment, since can be implemented at almost no additional cost. Therefore the synergic use of the proposed approach and the traditional multivariate gauge R&R studies can be a useful strategy for improving the overall quality of multivariate measurement systems and is effective for reducing the costs of a multivariate MSA performed with a certain frequency.
Highlights
In a manufacturing environment, critical decisions about process and product quality depend on the quality of the measurement systems
The synergic use of the proposed approach and the traditional multivariate gauge R&R studies can be a useful strategy for improving the overall quality of multivariate measurement systems and is effective for reducing the costs of a multivariate Measurement systems analysis (MSA) performed with a certain frequency
H 0 is not rejected if and only if (n 1) ˆ 1 u1, where u1 is the upper percentage point of the largest characteristic root of a. The advantage of this method is that the measurement instrument is assessed by comparing its performance instant t with those at instant 0, without the necessity of performing a multivariate gauge study (MANOVA): the sample covariance matrix S can be estimated using the data available by the routine use of the measurement device at no additional costs
Summary
Critical decisions about process and product quality depend on the quality of the measurement systems. [5] proposed multivariate extensions of three commonly used MSA-approval criteria using the volume of constant-density contours to characterize the variability of the measurement system. These multivariate MSA-metrics require a multivariate analysis of variance (MANOVA) for estimating the covariance matrices for one factor and twofactor gauge studies. In order to ensure constant flows of reliable data, manufacturers should periodically assess their measurement systems and the costs involved in maintaining well performing measurement systems are normally relevant.
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