Machine Learning-Based Optimization of Diamond Burnishing Parameters in Terms of Energy Efficiency and Quality Indexes
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Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s13369-024-09449-w.pdf
Reference29 articles.
1. Maximov, J.T.; Duncheva, G.V.; Anchev, A.P.; Dunchev, V.P.; Ichkova, M.D.: Improvement in fatigue strength of 41Cr4 steel through slide diamond burnishing. J. Braz. Soc. Mech. Sci. Eng. 42, 197 (2020)
2. Maximov, J.T.; Duncheva, G.V.; Anchev, A.P.; Dunchev, V.P.: Smoothing, deep, or mixed diamond burnishing of low alloy steel components: optimization procedures. Int. J. Adv. Manuf. Technol. 106, 1917–1929 (2020)
3. Kluz, R.; Antosz, K.; Trzepieciński, T.; Bucior, M.: Modelling the Influence of slide burnishing parameters on the surface roughness of shafts made of 42CrMo4 heat-treatable steel. Materials 14, 1175 (2021)
4. Felhő, C.; Varga, G.: 2D FEM investigation of residual stress in diamond burnishing. J. manuf. mater. 6(5), 123 (2022)
5. Duncheva, G.V.; Maximov, J.T.; Anchev, A.P.; Dunchev, V.P.; Argirov, Y.B.; Kandeva-Ivanova, M.: Enhancement of the wear resistance of CuAl9Fe4 sliding bearing bushings via diamond burnishing. Wear 510–511, 204491 (2022)
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