A physics-informed machine learning model for surface roughness prediction in milling operations-Reference-Cited by-同舟云学术

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A physics-informed machine learning model for surface roughness prediction in milling operations

Published:2022-11-18 Issue:11-12 Volume:123 Page:4065-4076
ISSN:0268-3768
Container-title:The International Journal of Advanced Manufacturing Technology
language:en
Short-container-title:Int J Adv Manuf Technol

Author:

Wu Pengcheng,Dai Haicong,Li Yufeng,He Yan,Zhong Rui,He Jinsen

Funder

Innovative Research Group Project of the National Natural Science Foundation of China

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/s00170-022-10470-2.pdf

Reference29 articles.

1. Pimenov DY, Hassui A, Wojciechowski S, et al (2019) Effect of the relative position of the face milling tool towards the workpiece on machined surface roughness and milling dynamics. Appl Sci 9: https://doi.org/10.3390/app9050842

2. Kilickap E, Yardimeden A, Celik YH (2015) Investigation of experimental study of end milling of CFRP composite. Sci Eng Compos Mater 22:89–95. https://doi.org/10.1515/secm-2013-0143

3. Liao ZR, la Monaca A, Murray J, et al (2021) Surface integrity in metal machining - part I: fundamentals of surface characteristics and formation mechanisms. Int J Mach TOOLS Manuf 162: https://doi.org/10.1016/j.ijmachtools.2020.103687

4. Han J, Hao X, Li L et al (2020) Investigation on micro-milling of Ti-6Al-4V alloy by PCD slotting-tools. Int J Precis Eng Manuf 21:291–300. https://doi.org/10.1007/s12541-019-00247-1

5. Han J, Hao X, Li L et al (2020) Investigation on surface quality and burr generation of high aspect ratio (HAR) micro-milled grooves. J Manuf Process 52:35–43. https://doi.org/10.1016/j.jmapro.2020.01.041

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