Milling surface roughness prediction method based on spatiotemporal ensemble learning-Reference-Cited by-同舟云学术

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Milling surface roughness prediction method based on spatiotemporal ensemble learning

Published:2023-07-05 Issue:1-2 Volume:128 Page:91-119
ISSN:0268-3768
Container-title:The International Journal of Advanced Manufacturing Technology
language:en
Short-container-title:Int J Adv Manuf Technol

Author:

Zeng Shi,Pi Dechang,Xu Tao

Funder

Postgraduate Research & Practice Innovation Program of NUAA

National Science and Technology Innovation 2030-Key Project of “New Generation Artificial Intelligence”

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/s00170-023-11737-y.pdf

Reference46 articles.

1. Benardos PG, Vosniakos G-C (2003) Predicting surface roughness in machining: a review. Int J Mach Tools Manuf 43(8):833–844. https://doi.org/10.1016/S0890-6955(03)00059-2

2. Abbas AT, Yu Pimenov D, Erdakov IN, Mikolajczyk T, El Danaf EA, Taha MA (2017) Minimization of turning time for high-strength steel with a given surface roughness using the Edgeworth-Pareto optimization method. Int J Adv Manuf Technol 93(5):2375–2392. https://doi.org/10.1007/s00170-017-0678-2

3. Abouelatta OB, Madl J (2001) Surface roughness prediction based on cutting parameters and tool vibrations in turning operations. J Mater Process Technol 118(1–3):269–277. https://doi.org/10.1016/S0924-0136(01)00959-1

4. Ahmad MS, Adnan SM, Zaidi S, Bhargava P (2020) A novel support vector regression (SVR) model for the prediction of splice strength of the unconfined beam specimens. Constr Building Mater 248:118475. https://doi.org/10.1016/j.conbuildmat.2020.118475

5. Schreiber M, Klöber-Koch J, Bömelburg-Zacharias J, Braunreuther S, Reinhart G (2019) Automated quality assurance as an intelligent cloud service using machine learning. Procedia CIRP 86:185–191. https://doi.org/10.1016/j.procir.2020.01.034

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