Domain-specific Gaussian process-based intelligent sampling for inspection planning of complex surfaces

Abstract

Precision measurement of complex surfaces requires intensive sampling for fully characterising the surface geometry and reducing the measurement uncertainty, which is, however, less efficient when the data are costly to acquire. This paper presents a Gaussian process (GP)-based intelligent sampling method for achieving well balance between the measurement efficiency and accuracy. The method makes use of GP to model the surface with domain-specific composite covariance kernel functions. The statistical nature of the GP makes it capable of giving credibility to the arbitrary prediction over the entire established model which can be used in a critical criterion to perform intelligent sampling of the surfaces. The method is independent from the coordinate frames, which makes the sampling plan easily utilised without accurate pre-positioning in actual measurement. The effectiveness of the method is verified through a series of comparison study and actual application in measuring a multi-scaled complex mould insert on coordinate measuring machine.