超高精度平面度误差的混合优化评定及应用OA

To overcome the limited computational accuracy and slow convergence of conventional intelli-gent optimization algorithms in flatness error evaluation,a high-precision and high-efficiency evaluation method is developed to satisfy the stringent algorithmic requirements of a self-developed 1 200 mm-aper-ture non-contact flatness measurement instrument.A hybrid PSO-SQP algorithm is proposed,in which Sequential Quadratic Programming(SQP)is employed as the primary framework and Particle Swarm Op-timization(PSO)is incorporated as an auxiliary global search strategy.The global exploration capability of PSO is leveraged to perform a coarse search and rapidly identify a near-global optimum,which is used as a high-quality initial point for SQP.An adaptive step-size strategy is introduced in the refinement stage to replace the conventional fixed step size,enabling rapid and stable convergence during local optimiza-tion.Experimental results indicate that the proposed PSO-SQP algorithm exhibits strong robustness to ini-tial-point deviations,sampling scale variations,and measurement noise.Relative to a high-precision coor-dinate measuring machine(CMM),the deviation of the evaluation results is below 7 nm.Engineering ap-plicability is further validated through the evaluation of a 280 mm-diameter flat mirror in a practical mea-surement scenario,where the obtained flatness is consistent with the specified surface form accuracy.Overall,the proposed algorithm provides high computational accuracy,fast convergence,and strong ro-bustness,offering a reliable and efficient solution for flatness error evaluation in precision manufacturing.