Optical freeform surface with high geometric complexity requires appropriate sampling strategy to achieve holistic measurement with improved efficiency and accuracy. This paper presents an adaptively sampling plans to measure the points based on particle swarm optimization algorithm for the problem of freeform surface measurement. In order to provide mathematical foundation for parametric modeling of freeform surfaces, the mathematical model of the freeform surface was then analyzed. The particle swarm optimization algorithm was studied, and the error optimization function between the interpolation surface of sampling points and the original freeform surface was constructed, and the coordinates of the sampling points were converted into particle positions for optimization, so as to distribute the sampling points adaptively. Simulation studies was conducted by using serval complex freeform surfaces. The results shows that the adaptively measured points can achieve the optical surfaces with improved accuracy and with less sampling points compared the traditional method.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
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.