Multi objective optimization of cutting parameters of end milling operation by Taguchi Grey
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Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s10751-024-02119-1.pdf
Reference22 articles.
1. Zhujani, F., Abdullahu, F., Todorov, G., Kamberov, K.: Optimization of multiple performance characteristics for CNC turning of Inconel 718 using Taguchi–Grey Relational Approach and Analysis of Variance. Metals. 14(2), 186 (2024)
2. Sur, G., Motorcu, A.R., Nohutçu, S.: Single and multi-objective optimization for cutting force and surface roughness in peripheral milling of Ti6Al4V using fixed and variable helix angle tools. J. Manuf. Process. 80, 529–545 (2022, August). https://doi.org/10.1016/j.jmapro.2022.06.016
3. Nguyen, A.T., Nguyen, V.H., Le, T.T., Nguyen, N.T.: Multiobjective Optimization of Surface Roughness and Tool Wear in High-Speed Milling of AA6061 by Machine Learning and NSGA-II. Advances in Materials Science and Engineering, 2022, 1–21. (2022)., May 27 https://doi.org/10.1155/2022/5406570
4. Rashid, D.K.M.J.: Optimize the Taguchi method, the signal-to-noise ratio, and the sensitivity. International Journal of Statistics and Applied Mathematics, 8(6), 64–70. (2023)., November 1 https://doi.org/10.22271/maths.2023.v8.i6a.1406
5. Hamzaçebi, C.: Taguchi Method as a Robust Design Tool. Quality Control - Intelligent Manufacturing, Robust Design and Charts. (2021)., March 24 https://doi.org/10.5772/intechopen.94908
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