Segmentation phase measuring deflectometry for measuring structured specular surfaces

Abstract

Abstract Accurate and fast three-dimensional (3D) measurement for industrial products/components designed to possess 3D structured shapes is a key driver for improved productivity. However, challenges for current techniques are considerable to measure structured specular surfaces. A technique named segmentation phase measuring deflectometry (SPMD) is proposed in this paper, which enables structured specular surfaces to be measured with high accuracy in one setup. Concept of segmentation in topology is introduced into phase measuring deflectometry, which separates a surface with complex structures into continuous segments. Each segment can be reconstructed based on gradient information to achieve good form accuracy, and all reconstructed segments can be fused into a whole 3D strucutred form result based on their absolute spatial positioning data. Here, we propose and discuss the principle of SPMD, a segmentation technique to separate a strucured surface into segments, a spatial positioning technique to obtain absolute position of the segments, and a data fusion strategy to fuse all reconstructed segments. Experimental results show SPMD can achieve nanometer level accuracy for form measurement of continuous segments by comparing with stylus profilometer, which is significantly higher than the accuracy of direct phase measuring deflectometry. Meanwhile, SPMD has micron level spatial positioning accuracy for structures by measuring two specular steps and comparing with coordinate measuring machine, which differentiates this technique from gradient-based phase measuring deflectometry that extends measurement capability from continuous specular surfaces to complex structured specular surfaces. Compared with the existing measurement techniques, SPMD significantly improved the convenience and ability to measure freeform and structured specular surfaces with the advantages of high measurement accuracy, fast measurement, and potential application for embedded measurement.