Study on algorithm of dwell time in ion beam figuring based on RBF neural network

Dasen Wang; Guoxu Ding; Chunyang Wang; Qiang Wang

doi:10.1117/12.2068414

6 August 2014 Study on algorithm of dwell time in ion beam figuring based on RBF neural network

Dasen Wang, Guoxu Ding, Chunyang Wang, Qiang Wang

Proceedings Volume 9281, 7th International Symposium on Advanced Optical Manufacturing and Testing Technologies: Advanced Optical Manufacturing Technologies; 928120 (2014) https://doi.org/10.1117/12.2068414
Event: 7th International Symposium on Advanced Optical Manufacturing and Testing Technologies (AOMATT 2014), 2014, Harbin, China

Abstract

Give the figures, which describe the relation between Ion Beam Figuring parameters(Ion beam density, energy, incident angle) and removal features based on SRIM software, the beam removal function model is built and proposed an algorithm of dwell time using RBF neural network optical controlling removal function and processing route. Then, Analyzed the error of algorithm and did some related compensation researches. Results showed that under a certain accuracy, the proposed algorithm can greatly speed up, control more optimization, make the IBF technology more practical, promoting the further development of the super precision optical surface processing technology.

Citation Download Citation

Dasen Wang, Guoxu Ding, Chunyang Wang, and Qiang Wang "Study on algorithm of dwell time in ion beam figuring based on RBF neural network", Proc. SPIE 9281, 7th International Symposium on Advanced Optical Manufacturing and Testing Technologies: Advanced Optical Manufacturing Technologies, 928120 (6 August 2014); https://doi.org/10.1117/12.2068414

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available