A Quality Framework to Check the Applicability of Engineering and Statistical Assumptions for Automated Gauges

In high-volume part manufacturing, interactions between program data and program flow can depart significantly from the initial statistical assumptions used during software development. This is a particular challenge for industrial gauging systems used in automotive part production where the applicability of statistical models affects system correctness. This paper uses a Quality Framework to track high-level engineering and statistical assumptions during development. Statistical Process Control (SPC) metrics define an “in-control” region where the statistical assumptions apply, and an outlier region where they do not apply. The gauge is monitored on-line to verify that production corresponds to the area of the operation where the gauge algorithms are known to work. If outliers are detected in the on-line manufacturing process, then parts can be quarantined, improved gauging algorithms selected, and/or process improvement activities can be initiated.