Uncertainty estimation in form error evaluation of freeform surfaces for precision metrology

Freeform surfaces are widely used in precision components to realize novel functionalities. In order to evaluate the form qualities of the manufactured freeform parts, surface matching/fitting is required. The uncertainty of the obtained form deviations needs to be estimated to assess the reliability of form error evaluation. The GUM approach is extensively adopted for uncertainty assessment in precision metrology, but it is not suited for assessing the nonlinear matching/fitting problems of freeform models. In this paper a Monte-Carlo method is developed to estimate the uncertainty of the fitted position, shape and form error metrics. Based on the correlation analysis, the effects of objective functions in numerical optimization, noise amplitudes in measurement, shapes of freeform surfaces and so on are determined. Then the significant factors dominating the reliability of the fitted results can be identified. Henceforth the matching/fitting procedures can be arranged appropriately to reduce the uncertainty of the evaluation results and improve the reliability of freeform surface characterization.