Task-Specific Uncertainty for Industrial Measurements
Analytics
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Abstract
The purpose of measurement is to provide information about a quantity of interest – a measurand. Since no measurands are exactly known, measurement uncertainty is estimated for common measuring tasks in order to quantify the result of a measuring process. Task-specific uncertainty estimation methods are performed to determine the measurement uncertainty for a specific scenario that is not addressed by the "standard" uncertainty budget. Often these situations are those in which a traditional sensitivity analysis, as recommended by the Guide to the Expression of Measurement Uncertainty (GUM), is not feasible or if the flexibility of the instrument allows the evaluation of many different measurands, making a "generic" uncertainty budget impractical. For the traditional sensitivity analysis procedure, a mathematical model of the particular measurand must be developed in order to compute the sensitivity coefficients (partial derivatives) that are used in the Law of Propagation of Uncertainty (LPU) estimation for the combined standard uncertainty. Major drawbacks arise from this analysis method, in that the mathematical model for a measurand is often complex, resulting in problems of nonlinearity, non-analytical solutions, or solutions by numerical-approximates. A task-specific uncertainty estimation - as with any valid estimate - must take into account all the uncertainty sources associated with the details of the measurement process, hence is a function of the measurand. The difficulty in performing the uncertainty analysis will be related to the details of the measurand. A series of ISO standards and ISO technical specifications exists today in support of task-specific uncertainty; these documents describe best-practices in determining the methodology, influence quantities, and analysis for the measurement uncertainty for a particular measurand. These ISO and ISO/TS documents cover everything from basic definitions of metrological characteristics to off-line uncertainty evaluation software (UES) packages used to simulate uncertainty sources numerically. It should be considered, however, that these documents are written for users of traditional, Cartesian Coordinate Measuring Machines (CMMs) that are utilized in controlled metrology lab environments. There has been minimal work done on shop floor CMMs and even less done on non-Cartesian CMMs (e.g. portable metrology technologies) that are built specifically for shop floor measurements. Portable metrology technologies and non-Cartesian CMMs include laser trackers, articulating arm coordinate measuring machines (AACMMs), laser scanners and theodolites. In general, metrology equipment used mainly in large-scale applications where the instrumentation has to be taken to the work-piece being inspected. The standards for the performance evaluation of these technologies are still evolving and no standardized methods for task-specific measurement uncertainty evaluation, like that of the traditional Cartesian CMM, have been suggested. The research presented in this dissertation develops preliminary evaluation methods for task-specific uncertainty analysis of the available portable CMM technologies. These methods are based on existing methods, but consider the different construction of the instruments, the environments in which they operate, and the nature of the work-pieces they are used to inspect. Case studies of a typical industrial measurement processes using these portable CMM technologies are presented.