Adaptive sampling strategy of five-axis inspection for free-form surface reconstruction

Abstract Five-axis inspection is widely used for measuring free-form surfaces, which require high-precision surface reconstruction for quality analysis, remanufacturing and so on. Nevertheless, existing sampling strategies for five-axis inspection neither make full use of surface characteristics nor are they able to pre-calculate the number of paths based on the required surface reconstruction error. To solve these problems, we propose an adaptive sampling strategy aiming at improving the precision of surface reconstruction without affecting the inspection efficiency. The proposed algorithm uses the chord height of corresponding points between two adjacent sampling paths as a criterion to determine the distribution and shape adjustment of the sampling paths. Experiments proved that the proposed algorithm can improve the surface reconstruction by 26.3% in terms of the maximum error and 19.5% in terms of the mean error compared with two traditional sampling algorithms.