Optimization of Cutting Parameters and Result Predictions with Response Surface Methodology, Individual and Ensemble Machine Learning Algorithms in End Milling of AISI 321-Reference-Cited by-同舟云学术

Optimization of Cutting Parameters and Result Predictions with Response Surface Methodology, Individual and Ensemble Machine Learning Algorithms in End Milling of AISI 321

Published:2023-02-13 Issue:9 Volume:48 Page:12075-12089
ISSN:2193-567X
Container-title:Arabian Journal for Science and Engineering
language:en
Short-container-title:Arab J Sci Eng

Author:

Demircioglu Diren Deniz^ORCID,Ozsoy Neslihan^ORCID,Ozsoy Murat^ORCID,Pehlivan Huseyin^ORCID

Publisher

Springer Science and Business Media LLC

Subject

Multidisciplinary

Link

https://link.springer.com/content/pdf/10.1007/s13369-023-07642-x.pdf

Reference42 articles.

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3. Anburaj, R.; Kumar, M.P.: Experimental studies on cryogenic CO2 face milling of inconel 625 superalloy. Mater. Manuf. Processes (2020). https://doi.org/10.1080/10426914.2020.1866199

4. Bag, R.; Panda, A.; Sahoo, A.K., et al.: Sustainable high-speed hard machining of AISI 4340 steel under dry environment. Arab. J. Sci. Eng. (2022). https://doi.org/10.1007/s13369-022-07094-9

5. Selvakumar, S.; Sreebalaji, V.S.; Ravikumar, K.: Machinability analysis and optimization in micro turning on tool wear for titanium alloy. Mater. Mater Manuf. Processes (2021). https://doi.org/10.1080/10426914.2020.1866198

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