Shu Han, Zou Chunlong, Chen Jianyu, Wang Shenghuai
School of Mechanical Engineering, Hubei University of Automotive Technology, Shiyan Hubei, China.
Front Bioeng Biotechnol. 2021 Dec 15;9:775455. doi: 10.3389/fbioe.2021.775455. eCollection 2021.
Flatness error is an important factor for effective evaluation of surface quality. The existing flatness error evaluation methods mainly evaluate the flatness error of a small number of data points on the micro scale surface measured by CMM, which cannot complete the flatness error evaluation of three-dimensional point cloud data on the micro/nano surface. To meet the needs of nano scale micro/nano surface flatness error evaluation, a minimum zone method on the basis of improved particle swarm optimization (PSO) algorithm is proposed. This method combines the principle of minimum zone method and hierarchical clustering method, improves the standard PSO algorithm, and can evaluate the flatness error of nano scale micro/nano surface image data point cloud scanned by atomic force microscope. The influence of the area size of micro/nano surface topography data on the flatness error evaluation results is analyzed. The flatness evaluation results and measurement uncertainty of minimum region method, standard least squares method, and standard PSO algorithm on the basis of the improved PSO algorithm are compared. Experiments show that the algorithm can stably evaluate the flatness error of micro/nano surface topography point cloud data, and the evaluation result of flatness error is more reliable and accurate than standard least squares method and standard PSO algorithm.
平面度误差是有效评估表面质量的一个重要因素。现有的平面度误差评估方法主要是对三坐标测量机测量的微观尺度表面上的少数数据点的平面度误差进行评估，无法完成对微纳表面三维点云数据的平面度误差评估。为满足纳米尺度微纳表面平面度误差评估的需求，提出了一种基于改进粒子群优化（PSO）算法的最小区域法。该方法结合最小区域法原理和层次聚类方法，对标准PSO算法进行改进，能够对原子力显微镜扫描的纳米尺度微纳表面图像数据点云的平面度误差进行评估。分析了微纳表面形貌数据的区域大小对平面度误差评估结果的影响。比较了基于改进PSO算法的最小区域法、标准最小二乘法和标准PSO算法的平面度评估结果及测量不确定度。实验表明，该算法能够稳定地评估微纳表面形貌点云数据的平面度误差，其平面度误差评估结果比标准最小二乘法和标准PSO算法更可靠、准确。
