中文摘要：

为了润滑和节能减排,要求精加工的气缸套内表面具有某些＂深谷＂纹理以储存油剂,但这种分层功能表面也给表面质量的评定带来了困难。传统滤波法和基准拟合法所提取的基准面常常因＂深谷＂被＂拉低＂,最终偏离了理想表面,造成基准面的失真,使得后续的参数评定不够精确。现提出一种新的回归稳健拟合算法,即以二次函数和三角函数为基函数,结合马夸特算法（Levenberg-Marquardt,LM）的最大偏差最小化（Least absolute value,L∞）回归方法,实现在降低对分层功能表面中异常点影响的同时,快速完美重构基准面。仿真结果表明,该方法与最小二乘法（Least squares,Ls）相比,稳健性大大提高;与高斯回归滤波相比稳健性一致,但速度提高了近105倍,且ISO 25178-2中规范的表面三维幅度参数和功能参数与真实值的偏差都不超过2%;最后对实际气缸套内壁表面进行应用试验,结果表明比Ls更稳健,比高斯回归滤波更省时,能够满足提取基准面高精度与高效率的要求。

英文摘要：

For lubrication, energy saving and emission reduction, some ＂valley＂ textures are required for the finishing cylinder liner inner surface to store oil, but some difficulties are brought by this hierarchical functional surface during the surface quality assessment .The reference which is extracted by traditional filtering method and the baseline firing method often be lowered by the ＂ valley ＂ and eventually deviate from the ideal surface , resulting in the distortion of the reference plane, making the subsequent parameters can not be assessed precisely. Nowadays, to solve this problem, robust Gaussian regression filtering is highly recognized, but eventually being too time -consuming and can not be promoted. Therefore, a new robust regression fitting algorithm is proposed, which is based on quadratic function and trigonometric function, combined with the Levenberg- Marquardt algorithm＇s minimizes the maximum deviation （least absolute value, L∞） regression method, making lower the impact of outlier on the hierarchical fimctional surface ,then, reconstruct the datum quickly and perfectly to be achieved. The simulation results show that compared with least squares （Ls）, robustness is greatly improved by the proposed method; compared with Gaussian regression filter, it has the same robustness, but the speed increases nearly 105 times, and the deviation between the surface in three-dimensional amplitude parameters and function parameters with the true value do not exceed 2% in the ISO 25178-2 standard. Finally, the experiments are conducted on the actual cylinder liner inner wall surface, and the results show that the new method is more robust comparing with Ls, more time-saving comparing with Ganssian regression filter, and it has high precision and high efficiency during extracting datum.