A bidirectional curve network based sampling method for enhancing the performance in measuring ultra-precision freeform surfaces

Measuring ultra-precision freeform surfaces with sub-micrometer form accuracy usually requires high density and intensive sampling in order to fully characterize the surface geometry and reduce the measurement uncertainty. However, this usually imposes many challenges such as insufficient sampling or long measurement time. In this paper, a bidirectional curve network based sampling (BCNBS) method is presented for enhancing the performance in measuring ultra-precision freeform surfaces. The BCNBS method is based on scanning two sets of curves on the measured surface in two different directions to form a curve network which is used to construct a substitute surface to represent the measured surface. A series of experiments has been conducted to verify the accuracy and efficiency of proposed sampling method and the results are compared with the conventional one directional raster sampling method. It is interesting to note that the sampling plan produced by the BCNBS method has significant improvement in terms of the efficiency in data sampling and the accuracy in surface geometry representation.