An Improved Method for NURBS Surface Based on Particle Swarm Optimization BP Neural Network

Xiaoqiang Tian,Li Yuan,Dehan Kong,Lingfu Kong,Deming Kong

doi:10.1109/access.2020.3029563

Xiaoqiang Tian, Li Yuan + Show 3 more

Open Access

https://doi.org/10.1109/access.2020.3029563

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Abstract

In order to further improve the accuracy and speed of the present commonly used NURBS surface method, an improved method for NURBS surface based on particle swarm optimization BP neural network is proposed. Firstly, node vectors of the data points are calculated by using the parametrization method of accumulating chord length. Then, prediction model of node vectors is constructed by using the particle swarm optimization BP neural network, and the experiment is presented to justify the feasibility and veracity of constructed prediction model. Finally, using the predicted node vectors, a fast and high-precision NURBS surface is realized. The results showed that the root mean squared error of fitting result of surface was deduced 84.05% and the run time was deduced 92.42% compared with the traditional NURBS method. Therefore, the proposed method is a fast and high-precise NURBS surface fitting method.

Highlights

With the development of mechanical processing industry, construction of complex surface model has been used in the field of reverse engineering machining [1], [2]
A fast and precise NURBS surface fitting method is realized by establishing prediction model of node vectors based on the particle swarm optimization BP neural network
In order to further verify the fast and high-precision NURBS surface in this paper, compared to the BP prediction method and support vector model prediction method, the average root mean squared error were reduced by 67.11% and 34.76%, respectively

Summary

Introduction

With the development of mechanical processing industry, construction of complex surface model has been used in the field of reverse engineering machining [1], [2]. INDEX TERMS Reverse engineering, particle swarm optimization BP neural network, node vector, NURBS surface. X. Tian et al.: Improved Method for NURBS Surface Based on Particle Swarm Optimization BP Neural Network thinking.

Results

Conclusion