Novel monitoring method for material removal rate considering quantitative wear of abrasive belts based on LightGBM learning algorithm-Reference-Cited by-同舟云学术

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Novel monitoring method for material removal rate considering quantitative wear of abrasive belts based on LightGBM learning algorithm

Published:2021-04-23 Issue:11-12 Volume:114 Page:3241-3253
ISSN:0268-3768
Container-title:The International Journal of Advanced Manufacturing Technology
language:en
Short-container-title:Int J Adv Manuf Technol

Author:

Wang Nina,Zhang Guangpeng,Pang Wanjing,Ren Lijuan,Wang Yupeng

Publisher

Springer Science and Business Media LLC

Subject

Industrial and Manufacturing Engineering,Computer Science Applications,Mechanical Engineering,Software,Control and Systems Engineering

Link

https://link.springer.com/content/pdf/10.1007/s00170-021-06988-6.pdf

Reference25 articles.

1. Wang YJ, Huang Y, Chen YX, Yang ZS (2016) Model of an abrasive belt grinding surface removal contour and its application. Int J Adv Manuf Technol 82(9–12):2113–2122

2. Uhlmann E, Lypovka P, Hochschild L, Schröer N (2016) Influence of rail grinding process parameters on rail surface roughness and surface layer hardness. Wear 366–367:287–293

3. Wang RQ, Li JY, Liu YM, Wang WX (2016) Modeling material removal rate of heavy belt-grinding in manufacturing of U71Mn material. Key Eng Mater 693:1082–1089

4. Yang ZY, Xu XH, Zhu DH, Yan SJ, Ding H (2019) On energetic evaluation of robotic belt grinding mechanisms based on single spherical abrasive grain model. Int J Adv Manuf Technol 104(9–12):4539–4548

5. Wuest T, Irgens C, Thoben K (2014) An approach to monitoring quality in manufacturing using supervised machine learning on product state data. J Intell Manuf 25(5):1167–1180

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