Numerical Control Interpolation Algorithm of Aspheric Surface Based on the Genetic Algorithms and Neural Network

Fei Hu Zhang; D.J. Chen; L.J. Li

doi:10.4028/www.scientific.net/KEM.364-366.25

Paper Titles

Diamond Turning Freeform Optics
p.1

Fabrication of Light Guide Plate on PDMS-Based Using MEMS Technique for Application of LED-Backlight
p.7

A Review on Machining of Micro-Structured Optical Molds
p.13

Novel Approach to Fabrication of 3D Micro-Structures by Laser Machining
p.19

Numerical Control Interpolation Algorithm of Aspheric Surface Based on the Genetic Algorithms and Neural Network
p.25

Production of Micro-Optical Lenses Using Micro-Jet Drop-On-Demand
p.30

Research on Mathematical Models of New Diamond Turning for Non-Axisymmetric Aspheric Mirrors
p.35

Single-Point Diamond Turning of Aspheric Mirror with Inner Reflecting Surfaces
p.39

Study on Manufacturing Process Optimizing of Lightguiding Plate for Soft Computing
p.43

HomeKey Engineering MaterialsKey Engineering Materials Vols. 364-366Numerical Control Interpolation Algorithm of...

Numerical Control Interpolation Algorithm of Aspheric Surface Based on the Genetic Algorithms and Neural Network

Abstract:

When the Neural Network model is used to interpolate the non-circular curves, there are shortcomings of converging slowly and getting into the local optimum easily. A novel numerical control interpolation algorithm based on the GA (Genetic Algorithms) and NN (Neural Network) was introduced for the ultra-precision machining of aspheric surfaces. The algorithm integrated the global searching of GA with the parallel processing of NN, enhanceed the convergence speed and found the global optimum. At the end, the quintic non-circular curve was taken as an example to do the emulation and experiment. The results prove that this algorithm can fit the non-circular curve accurately, improve the precision of numerical control interpolation and reduce the number of calculating and interpolation cycles.

You might also be interested in these eBooks

Optics Design and Precision Manufacturing Technologies

View Preview

Info:

Periodical:

Key Engineering Materials (Volumes 364-366)

Pages:

25-29

DOI:

https://doi.org/10.4028/www.scientific.net/KEM.364-366.25

Citation:

Cite this paper

Online since:

December 2007

Authors:

Fei Hu Zhang, D.J. Chen, L.J. Li

Keywords:

BP Algorithm, Genetic Algorithm (GA), Neural Network (NN), Numerical Control Interpolation

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] Li Zuoqing et al. Numerical Control Interpolation Princiole and Method Based Neural Network Model Nechanical Engineering of China. 1996, 7(1), p.35.

Google Scholar

[2] WANG lisong, et al(23). Parabolic Contour Interpolation Arithmetic Based on Three Layer Forward Network. Manufacturing Technology and Machine Tool. 2001, (3), p.21.

Google Scholar

[3] Marim, L. R, Neural-Network-Assisted Genetic Algorithm Applied to Silicon Clusters. Molecular and Optical Physics, 2003, 67(2): 9 3~98.

Google Scholar

[4] Lu Feng, Shi Min, Wu Danwen, Xie Yan, Zhou Xiaodong. Study and Application of BP Neural Network Model Based on Genetic Algorithm. Journal of Wuhan university of Technology.

Google Scholar

[5] Wei Haikun. The Theories and Methods of the Neural Network'Structure Design. National Defence Industry Press. 2005. 02.

Google Scholar

[6] Xuan Guangnan, Chen Runwei. Genetic Algorithms and Engineering Optimization. Qinghua University Press. 2004. 01.

Google Scholar

[7] Wang Xiaoping, Cao Liming. The Therories and Application of the Genetic Algorithms. Xi'an Jiaotong University Press (2000), p.19.

Google Scholar

[8] Marim, L. R, Neural-Network-Assisted Genetic Algorithm Applied to Silicon Clusters. Molecular and Optical Physics (2003) 67(2), p.93.

Google Scholar