Uncertainty budget of a large-range nanopositioning platform based on Monte Carlo simulation

Abstract

The objective of precision systems design is to obtain machines with very high and totally predictable work-zone accuracies. In already functional systems, where the errors can be measured, this is achieved by error correction and compensation. The aim of this work is to propose an uncertainty budget methodology to obtain the final measuring uncertainty of precise measuring systems, after error compensation. The case study is a nanopositioning platform, referred as NanoPla, with a confocal sensor integrated as measuring instrument. The NanoPla performs precise positioning in a large range of 50 mm × 50 mm, and its target is surface topography characterization, at a submicrometre scale. After performing the uncertainty budget of the NanoPla, Monte Carlo method is used to obtain the final measuring uncertainty along the whole NanoPla working range, considering all the casuistry. By studying the results, the authors are able to propose solutions to minimize the final measuring uncertainty