3D Eyeglass Lens Surface Evaluation for Defocus Region Sagging
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Solution Overview
Problem
The evaluation of the surface shape of eyeglass lenses with convex regions becomes unclear due to sagging at the boundary portions when coated with a hard coating film, leading to potential misalignment in controlling the progression of refractive errors.
Innovation Solution
A method involving three-dimensional data acquisition, cluster analysis, and curve fitting is employed to classify and evaluate the surface shape of eyeglass lenses with defocus regions, allowing for accurate determination of deviations from reference shapes without relying on design data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Strength
If a hard coating film is applied to the object-side surface of the eyeglass lens, then the surface durability is improved, but the boundary portion between convex regions and base regions becomes unclear due to sagging
Solution Approach 1:
The evaluation method segments the surface data into distinct regions (convex regions and base regions) using cluster analysis, allowing separate evaluation of each region's characteristics. This segmentation enables accurate measurement of boundary portions even when sagging occurs, as the method can identify and analyze the boundary region independently from the convex and base regions.
Solution Approach 2:
The invention transitions from two-dimensional surface evaluation to three-dimensional evaluation by measuring surface shape in the depth direction. This dimensional change allows the method to detect sagging in the boundary portions that occurs after coating, as the three-dimensional measurement captures the vertical displacement caused by sagging that would be invisible in planar views.
2Ease of manufacture
If the boundary portion becomes unclear due to sagging, then the manufacturing process is simplified by coating, but the surface shape evaluation accuracy deteriorates
Solution Approach 1:
The evaluation method performs preliminary classification of surface data into convex regions, base regions, and boundary portions before conducting the actual surface shape evaluation. This preliminary action of segmenting the data allows the subsequent evaluation to focus specifically on the boundary portions, ensuring accurate measurement even when these regions have become unclear due to sagging from the coating process.
Solution Approach 2:
The invention introduces cluster analysis as an intermediary processing step between the raw three-dimensional surface data and the final evaluation results. This intermediary classification process acts as a mediator that separates and identifies the boundary portions, enabling accurate evaluation despite the sagging effect that blurs the boundaries in the physical structure.
3Measurement precision
If cluster analysis is performed to classify data groups, then the evaluation accuracy is improved, but the computational complexity increases
Solution Approach 1:
The cluster analysis is applied locally to specific regions of the surface data rather than processing the entire surface uniformly. By focusing the computational effort on identifying and classifying boundary portions in specific areas, the method achieves high evaluation accuracy while minimizing unnecessary computational complexity in regions where simple evaluation suffices.
Data Source
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AI summary
With regard to an eyeglass lens that is provided with an object-side surface and an eyeball-side surface, and that has a plurality of defocus regions on at least one of the object-side surface and the eyeball-side surface, the surface shape is evaluated through a step of measuring the surface shape of the surface of the eyeglass lens that has the plurality of defocus regions, and acquiring three-dimensional data regarding the surface shape (S101), a step of classifying data groups regarding the plurality of respective defocus regions and a data group regarding a base region, which is a region where the defocus regions are not formed, by performing cluster analysis on the three-dimensional data (S103), a step of combining curved surface shape data obtained by performing curve fitting on each of the classified data groups, and extracting reference shape data regarding the object-side surface of the eyeglass lens (S104), and a step of comparing the three-dimensional data and the reference shape data, and obtaining degrees of deviation of the three-dimensional data from the reference shape data (S105).