Abstract
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.
Highlights
A typical engineering surface consists of a range of spatial frequencies (Raja et al, 2002)
For the three-dimensional point cloud data of micro/nano surface, the corresponding segmentation algorithm is used for preprocessing according to the complexity, and the appropriate flatness error evaluation method is used for evaluation
With the increasing area of surface topography, the reliability of flatness error evaluation results of the minimum zone method based on the improved particle swarm optimization (PSO) algorithm becomes better, whereas the standard least squares method and standard PSO algorithm become worse
Summary
A typical engineering surface consists of a range of spatial frequencies (Raja et al, 2002). Most of these algorithms are only used to evaluate the flatness error of the measurement results of micro scale CMM equipment Whether it can be used for micro/nano surface topography is unknown. The calculation accuracy of PSO is better than that of the least squares method, which is equivalent to that of other methods that meet the minimum zone conditions defined in the standard It can obtain high-precision results and is simple and easy to implement. This method is affected by equipment noise and surface defects in the process of micro/ nano surface topography treatment, resulting in unsatisfactory evaluation results. Experimental Results and Analysis gives the experimental results and analysis, and Conclusion summarizes the summary of this paper and the future research direction
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