Precision measurement and evaluation of flatness error for the aero-engine rotor connection surface based on convex hull theory and an improved PSO algorithm

• Published in: Measurement science & technology Vol. 31; no. 8; pp. 85006 - 85015
• Main Authors: Zhang, Maowei, Liu, Yongmeng, Sun, Chuanzhi, Wang, Xiaoming, Tan, Jiubin
• Format: Journal Article
• Language: English
• Published: IOP Publishing 01.08.2020
• Subjects: convex hull; flatness error; form error evaluation; improved PSO algorithm; precision measurement
• ISSN: 0957-0233; 1361-6501
• DOI: 10.1088/1361-6501/ab8170

The precision measurement and evaluation of the geometry form error for high-end precision components is one of the most important tasks for metrologists. In this paper, a novel hybrid flatness error evaluation method is proposed to measure and evaluate the flatness error for large-scale point cloud data. This method is based on the convex hull algorithm and the improved particle swarm optimization (IPSO) algorithm. Not only are a large number of redundant measurement data points effectively removed through the convex hull algorithm, but also this operation does not lose any valuable information for flatness error evaluation. Then an accurate mathematical model for flatness error evaluation is established based on the minimum zone criteria recommended by ISO, and then the IPSO algorithm is adopted to solve this complex non-linear optimization problem. In particular, the non-linear dynamic inertia weights w and learning factors c1, c2 are introduced to improve the calculation accuracy. Finally, the proposed method is verified through an experiment measuring and evaluating the flatness error of an aero-engine rotor connection surface. The effective measurement data points are reduced from 18 000 to 856, or 4.76%, which is significant compared to the original data points. The flatness error evaluation result is 369 µm according to the IPSO method that we propose. Last but not least, we compare our method with the traditional least squares method and unmodified particle swarm optimization algorithm. The calculation results indicate that the evaluation accuracy is increased by 21 µm and 44 µm, respectively.