Surface roughness and surface crack length prediction using supervised machine learning–based approach of electrical discharge machining of deep cryogenically treated NiTi, NiCu, and BeCu alloys
Author:
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/s00170-023-12269-1.pdf
Reference39 articles.
1. Ming W et al (2022) Progress in modeling of electrical discharge machining process. Int J Heat Mass Transfer 187:122563. https://doi.org/10.1016/j.ijheatmasstransfer.2022.122563
2. Shastri RK, Mohanty CP, Dash S, Gopal KMP, Annamalai AR, Jen C-P (2022) Reviewing performance measures of the die-sinking electrical discharge machining process: challenges and future scopes. Nanomaterials 12(3):384. https://doi.org/10.3390/nano12030384
3. Boopathi S (2022) An extensive review on sustainable developments of dry and near-dry electrical discharge machining processes. J Manuf Sci Eng 144:5. https://doi.org/10.1115/1.4052527
4. Baroi BK, Jagadish, Patowari PK (2022) A review on sustainability, health, and safety issues of electrical discharge machining. J Braz Soc Mech Sci Eng 44(2):59. https://doi.org/10.1007/s40430-021-03351-4
5. Kannan E, Trabelsi Y, Boopathi S, Alagesan S (2022) Influences of cryogenically treated work material on near-dry wire-cut electrical discharge machining process”. Surf Topogr: Metrol Prop 10(1):015027. https://doi.org/10.1088/2051-672X/ac53e1
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