Neural network for enhancement of end milling processes through accurate prediction of temperature in the cutting zone-Reference-Cited by-同舟云学术

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Neural network for enhancement of end milling processes through accurate prediction of temperature in the cutting zone

Published:2024-05-03 Issue:6 Volume:46 Page:
ISSN:1678-5878
Container-title:Journal of the Brazilian Society of Mechanical Sciences and Engineering
language:en
Short-container-title:J Braz. Soc. Mech. Sci. Eng.

Author:

Baralic Jelena^ORCID,Mitrovic Andjelija,Petrovic Savic Suzana^ORCID,Djurovic Strahinja,Nedic Bogdan^ORCID

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s40430-024-04923-w.pdf

Reference31 articles.

1. Kumar RS, Kumar SS et al (2021) Optimization of CNC end milling process parameters of low-carbon mold steel using response surface methodology and grey relational analysis. Adv Mater Sci Eng 2021:11. https://doi.org/10.1155/2021/4005728

2. Pimenov DY, Hassui A et al (2019) Effect of the relative position of the face milling tool towards the workpiece on machined surface roughness and milling dynamics. App Sci 9(5):842. https://doi.org/10.3390/app9050842

3. Soori M, Arezoo B (2023) Effect of cutting parameters on tool life and cutting temperature in milling of AISI 1038 carbon steel. J New Technol Mater 13(1):33–48

4. Bagherzadeh A, Budak E (2018) Investigation of machinability in turning of difficult-to-cut materials using a new cryogenic cooling approach. Tribol Int 119:510–520. https://doi.org/10.1016/j.triboint.2017.11.033

5. Shokrani A, Betts J (2020) A new hybrid minimum quantity lubrication system for machining difficult-to-cut materials. CIRP Ann 69:73–76. https://doi.org/10.1016/j.cirp.2020.04.027

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