Predictive optimization of surface quality, cost, and energy consumption during milling alloy 2017A: an approach integrating GA-ANN and RSM models
Author:
Publisher
Springer Science and Business Media LLC
Subject
Industrial and Manufacturing Engineering,Modeling and Simulation
Link
https://link.springer.com/content/pdf/10.1007/s12008-023-01613-0.pdf
Reference49 articles.
1. Khan, A.M., et al.: Multi-objective optimization of energy consumption and surface quality in nanofluid SQCl assisted face milling. Energies 12, 710 (2019)
2. Wang, H., et al.: An optimization model for energy-efficient machining for sustainable production. J. Clean. Prod. 232, 1121–1133 (2019)
3. Al-Zubaidi, S., et al.: Development of hybrid intelligent models for prediction machining performance measure in end milling of Ti6Al4V alloy with PVD coated tool under dry cutting conditions. Lubricants 10, 236 (2022)
4. Balonji, S., Tartibu, L.K., Okokpujie, I.P.: Prediction analysis of surface roughness of aluminum Al6061 in end milling CNC machine using soft computing techniques. Appl. Sci. 13, 4147 (2023)
5. Zhao, X., Li, C., Chen, X., Cui, J., Cao, B.: Data-driven cutting parameters optimization method in multiple configurations machining process for energy consumption and production time saving. Int. J. Precis. Eng. Manuf. Green Technol. 9, 709–728 (2022)
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