Evaluating measurement and process capabilities by GR&R with four quality measures

A new multivariate gage R&R method for correlated characteristics

A fuzzy expert system to increase accuracy and precision in measurement system analysis

This paper utilizes the six-sigma define-measure-analyze-improve-control (DMAIC) procedure to improve quality through enhancing the measurement system capability (MSC) of a wood industry. In the first stage, the major reported defects at the customer site are identified, and then the performance of the measurement system is evaluated by conducting a gage repeatability and reproducibility (GR&R) experiment on a drilling process, i.e. the outputs of a drilling machine. The obtained results show that the measurement system is inadequate and results in wrong classification of the produced wooden parts. Thus, to improve MSC, corrective actions, including operators' training, proper selection of measuring instruments, and improved measuring methods, were implemented. To determine the achieved improvement in MSC, a second GR&R study was carried out under the same conditions. In conclusion, adopting the DMAIC procedure including GR&R study turns out to be an effective method in improving the quality system involving measurements. Copyright © 2008 John Wiley & Sons, Ltd.

Improving wooden parts' quality by adopting DMAIC procedure

In a process that is integral to a measurement system, some variation is likely to occur. Measurement system analysis is an important area of study that is able to determine the amount of variation. In evaluating a measurement system's variation, the most adequate technique, once an instrument is calibrated, is gauge repeatability and reproducibility (GRR here the geometric mean is used as an agglutination strategy for the eigenvalues extracted from variance–covariance matrices. This approach, however, has some weaknesses. This paper thus proposes new multivariate indexes based on four weighted approaches. Statistical analysis of empirical and data from the literature indicates that the most effective weighting strategy in multivariate GR&R studies is based on an explanation of the percentages of the eigenvalues extracted from a measurement system’ matrix.

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Measurement system analysis using designed experiments with minimum α–β Risks and n