On-line process decisions using convolutional neural network for centering high-precision short-focus lens

Abstract

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 <E0.1, and the circularity error to <0.1 mm. © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

Defect analysis of cementing lenses and parameter optimization based on a convolutional neural network algorithm

“…Experiments have consistently demonstrated the excellent accuracy and robust stability of CNNs in classification tasks. Compared with other machine-learning architectures, CNNs exhibit superior computational efficiency and classification accuracy, leading to their wide use for enhancing traditional prediction models 27 , 28 . Moreover, CNNs have been employed to generate highly realistic images, thereby compensating for data scarcity.…”

“…Compared with other machine-learning architectures, CNNs exhibit superior computational efficiency and classification accuracy, leading to their wide use for enhancing traditional prediction models. 27,28 Moreover, CNNs have been employed to generate highly realistic images, thereby compensating for data scarcity. Furthermore, CNN models have demonstrated powerful discriminative ability for diverse datasets, effectively reducing the time required for training model calibration.…”