Smart measurement system based on deep learning for microstructured surfaces

Yongqiang Yang; Chi Fai Cheung; Da Li

doi:10.1117/12.3009078

29 December 2023 Smart measurement system based on deep learning for microstructured surfaces

Yongqiang Yang, Chi Fai Cheung, Da Li

Proceedings Volume 12976, Eighth Asia Pacific Conference on Optics Manufacture and Third International Forum of Young Scientists on Advanced Optical Manufacturing (APCOM and YSAOM 2023); 1297618 (2023) https://doi.org/10.1117/12.3009078
Event: 8th Asia Pacific Conference on Optics Manufacture & 3rd International Forum of Young Scientists on Advanced Optical Manufacturing, 2023, Shenzhen, China

Abstract

The microstructured surfaces are playing an important role in industry. Measuring these surfaces quickly and accurately can provide significant quantitative results for machining process. However, traditional measurement methods and systems cannot meet the requirements of high precision machining. Measured samples are often moved several times in order to achieve the desired result. To address these limitations, a smart measurement system based on deep learning algorithm is established. The whole measurement system is portable and can be employed in many works situation. This smart measurement can offer an effective way of capturing high resolution images. To enhance the accuracy and efficiency of data processing, a deep learning model is applied in this system. This model can be employed to generate disparity after the input of high-resolution images. After the information fusion of disparity and the depth, which is obtained from the depth estimation module, the microstructured surfaces can be reconstructed accurately. This system is inputted with the synthetic data and experimental results show that the measurement uncertainty could reach at a sub-micrometer level.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yongqiang Yang, Chi Fai Cheung, and Da Li "Smart measurement system based on deep learning for microstructured surfaces", Proc. SPIE 12976, Eighth Asia Pacific Conference on Optics Manufacture and Third International Forum of Young Scientists on Advanced Optical Manufacturing (APCOM and YSAOM 2023), 1297618 (29 December 2023); https://doi.org/10.1117/12.3009078

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KEYWORDS

Deep learning

3D metrology

3D modeling

Data modeling

Convolution

Information fusion

Metrology

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