Laser & Optoelectronics Progress, Volume. 59, Issue 23, 2324001(2022)
Milling Surface Roughness Measurement Under Few-Shot Problem
Most machine vision-based roughness measurement methods either build a prediction model based on roughness correlation indices or build an index-free prediction model using deep learning networks. However, both these models have disadvantages. The artificial designed index has a complicated calculation process, which is not conducive to inline detection. In comparison, deep learning models rely heavily on big data. It is difficult to train an effective model when the amount of data is insufficient. To address the above problems, this study proposes a graph neural network-based method for measuring the roughness of milling surfaces. This proposed approach acquired the ability to learn autonomously during the training phase. Thus, only a few milling samples were required to measure the roughness of the milling workpieces. The experimental results show that the proposed method can automatically extract features on roughness measurement of milling workpieces with high accuracy and good robustness of lighting environment.
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Huaian Yi, Runji Fang, Aihua Shu, Enhui Lu. Milling Surface Roughness Measurement Under Few-Shot Problem[J]. Laser & Optoelectronics Progress, 2022, 59(23): 2324001
Category: Optics at Surfaces
Received: Mar. 2, 2022
Accepted: Jun. 14, 2022
Published Online: Nov. 28, 2022
The Author Email: Yi Huaian (yihuaian@126.com)