This study integrated the use of a centering machine with an automatic optical axis measuring technique to improve the centering process for short-focus lenses, which are widely used in interferometric inspection, microscopy, and spectrometry. A major concern of the centering process is coma aberrations during the axis centering of a lens, which leads to deformation of the image system. Because of the small size and high curvature of short-focus lenses, high optical axis error and unstable grinding quality are highly problematic within the high-precision centering process. To reduce optical axis error and improve manufacturing quality, an on-line optical axis measuring system that applies convolutional neural network (CNN) machine learning for the evaluation of centering stability was developed. According to experimental results, the CNN achieved 95% accuracy. With the use of trace classification and optical axis measurements, the optical axis error was controlled to <150 μrad, the range of cracks to

No abstract