Optical Surface Optimization via Global Cost Function
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing optimization methods for optical lenses are inefficient in handling a large number of criteria, leading to increased resource and time requirements, and often result in curvature variations, especially on the periphery of optimized surfaces.
Innovation Solution
A computer-based method that defines optical surface parameters using a global surface cost function, which is a weighted sum of individual cost functions, including derivatives, to optimize optical surfaces, ensuring smoothness and focusing on specific evaluation zones rather than the entire surface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If known optimization methods are used to determine optical surface shape, then the optical function is achieved, but curvature variations occur especially on the periphery of the optimized surface
Solution Approach 1:
The optical surface is divided into multiple evaluation zones (central zone, intermediate zone, peripheral zone) with different cost function weights. This segmentation allows different regions to be optimized with different priorities, ensuring smooth transitions and reducing curvature variations at zone boundaries while maintaining overall surface quality.
Solution Approach 2:
Different cost functions are applied to different evaluation zones based on their specific requirements. The central zone prioritizes optical performance criteria, while peripheral zones emphasize surface smoothness and curvature continuity. This local quality approach ensures each region is optimized for its specific function while maintaining global coherence.
2Manufacturing precision
If the number of criteria for personalized optical lenses is increased, then the optimization quality improves, but the resource and time requirements increase
Solution Approach 1:
The set of optimization criteria is segmented and assigned to different evaluation zones based on their relevance. Not all criteria are applied uniformly across the entire surface, but rather selectively in zones where they are most important. This reduces the computational burden while maintaining optimization quality.
Solution Approach 2:
The cost function weights are dynamically adjusted based on the evaluation zone and optimization stage. By changing the relative importance of different criteria in different regions, the method efficiently balances multiple objectives without requiring equal computational resources for all parameters throughout the entire optimization process.
Data Source
AI summary
Method for optimizing an optical surface comprising: an initial optical surface providing step, a working optical surface defining step, during which a working optical surface is defined to be equal to the initial optical surface, a first surface cost function providing step, during which a first surface cost function of the nth derivative of the surface is provided, a set of surface cost functions providing step, during which a set of surface cost functions function of at least one criterion over evaluation zones is provided, a global surface cost function evaluation step during which a global surface cost function equal to a weighted sum of the previous cost functions is evaluated, a modifying step, during which the working surface is modified, wherein the evaluation and modifying steps are repeated so as to minimize the global surface cost function.