Virtual sample generation method based on generative adversarial fuzzy neural network

Cui, Canlin; Xia, Heng; Qiao, Junfei; Yu, Wen

doi:10.1007/s00521-022-08104-5

Cited by 10 publications

(1 citation statement)

References 41 publications

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“…created a life ratio based on actual faults and Particle swarm optimization faults, generated virtual samples, and verified the accuracy of the life assessment model through experiments. Cui et al [29] . proposed a virtual sample generation method based on generative adaptive fuzzy neural network for industrial testing process data, and verified the feasibility of the method through experiments, providing a theoretical basis for online measurement of key performance indicators in industrial processes.…”

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confidence: 99%

A new surface roughness measurement method based on QR-SVM

Yu,

Li,

Sheng

et al. 2023

Preprint

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This study proposes a method for detecting surface roughness in machining, which solves the problem of low detection accuracy caused by a small sample size based on machine vision detection. The fusion of QR and Support Vector Machine (SVM) methods is used to detect surface roughness. Firstly, a contact roughness detector is used to measure the surface roughness value, and a CCD is used to obtain the processed surface image to obtain the sample. Secondly, the QR decomposition method is improved to generate virtual samples and expand the sample size. Extract the texture feature values of the image using the gray level co-occurrence matrix, and establish the correlation between roughness and texture features. Finally, support vector machines are used to classify the surface roughness of mechanical machining. The experimental results show that the accuracy of the surface roughness detection method based on machine vision has increased from 80.6–96.5%, proving the feasibility of this method and providing a theoretical basis for on-site detection of small sample surface roughness. This method has certain engineering application potential.

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confidence: 99%