A real-time intelligent method to identify mechanistic cutting force coefficients in 3-axis ball-end milling process using stochastic gradient decent: the mechanistic network-Reference-Cited by-同舟云学术

A real-time intelligent method to identify mechanistic cutting force coefficients in 3-axis ball-end milling process using stochastic gradient decent: the mechanistic network

Published:2023-10-20 Issue:7-8 Volume:129 Page:2949-2968
ISSN:0268-3768
Container-title:The International Journal of Advanced Manufacturing Technology
language:en
Short-container-title:Int J Adv Manuf Technol

Author:

Forootan Mahmoodreza^ORCID,Akbari Javad^ORCID,Ghorbani Mohammad^ORCID

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/s00170-023-12460-4.pdf

Reference45 articles.

1. Marei M, El Zaatari S, Li W (2021) Transfer learning enabled convolutional neural networks for estimating health state of cutting tools. Robot Comput-Integr Manuf 71:102145. https://doi.org/10.1016/j.rcim.2021.102145

2. Ghorbani M, Movahhedy MR (2019) Extraction of surface curvatures from tool path data and prediction of cutting forces in the finish milling of sculptured surfaces. J Manuf Process 45:273–289. https://doi.org/10.1016/j.jmapro.2019.07.008

3. Moufki A et al (2000) Thermoviscoplastic modelling of oblique cutting: forces and chip flow predictions. Int J Mech Sci 42(6):1205–1232. https://doi.org/10.1016/S0020-7403(99)00036-3

4. Tsai C-L, Liao Y-S (2008) Prediction of cutting forces in ball-end milling by means of geometric analysis. J Mater Process Technol 205(1–3):24–33. https://doi.org/10.1016/j.jmatprotec.2007.11.083

5. Mahnama M, Movahhedy M (2012) Application of FEM simulation of chip formation to stability analysis in orthogonal cutting process. J Manuf Process 14(3):188–194. https://doi.org/10.1016/j.jmapro.2011.12.007