A new surface roughness measurement method based on QR-SVM-Reference-Cited by-同舟云学术

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A new surface roughness measurement method based on QR-SVM

Published:2024-06-19 Issue:7-8 Volume:133 Page:3987-3997
ISSN:0268-3768
Container-title:The International Journal of Advanced Manufacturing Technology
language:en
Short-container-title:Int J Adv Manuf Technol

Author:

Yu Xiaofeng,Li Zhengminqing,Sheng Wei,Zhang Chuanmei

Funder

National Science and Technology Major Project

Defense Industrial Technology Development Program

National Natural Science Foundation of China

State Key Laboratory Fund of Helicopter Transmission Technology

Equipment Pre-research Application Innovation Project of AECC

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s00170-024-13898-w.pdf

Reference37 articles.

1. Fuh YK, Hsu KC, Fan JR (2012) Roughness measurement of metals using a modified binary speckle image and adaptive optics. Opt Lasers Eng 50:312–316. https://doi.org/10.1016/j.optlaseng.2011.11.003

2. Jeyapoovan T, Murugan M (2013) Surface roughness classification using image processing. Measurement 46:2065–2072. https://doi.org/10.1016/j.measurement.2013.03.014

3. Yi HA, Liu J, Lu EH, Ao P (2016) Measuring grinding surface roughness based on the sharpness evaluation of colour images. Meas Sci Technol 27:025404. https://doi.org/10.1088/0957-0233/27/2/025404

4. Zhang JG, Tan TN (2002) Brief review of invariant texture analysis methods. Pattern Recognit 35:735–747. https://doi.org/10.1016/S0031-3203(01)00074-7

5. Palani S, Natarajan U (2011) Prediction of surface roughness in CNC end milling by machine vision system using artificial neural network based on 2D Fourier transform. Int J Adv Manuf Technol 54:1033–1042. https://doi.org/10.1007/s00170-010-3018-3

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