Surface error consistency coefficient based on cross-correlation for evaluating the consistency of surface error measurement methods

Accuracy and correctness are significant to the entire measurement. The measurement results of new methods are usually compared with the results of mature measurement methods aiming at evaluating the consistency of the two methods, which can estimate the feasibility of new methods. Two criteria are usually utilized to evaluate the consistency of surface measurements. One criterion is to compare the Peak-Valley (PV) value and Root-Mean-Square (RMS) value directly. However, lots of surfaces which are not similar or even completely different share the same PV and RMS values. The other criterion is to analyze the point-to-point difference. But this criterion still utilizes the PV value and RMS value as the consistency evaluation of the point-to-point difference. Surface Error Consistency Coefficient (SECC) is proposed as a criterion in this paper. In this criterion, the principle of cross-correlation is introduced to evaluate the consistency of two measurement results and all the data are utilized. This criterion can evaluate the consistency of two surfaces by a percentage and is not susceptible to some special single points. In this paper, some surfaces are evaluated in simulations, and the consistency of surface maps by Coordinate-transform method and Fourier-transform method is evaluated.