Volume 8, 2021
|Number of page(s)||13|
|Published online||17 February 2021|
Validation and optimization of cutting parameters for Ti-6Al-4V turning operation using DEFORM 3D simulations and Taguchi method
Department of Mining, Materials and Petroleum Engineering, Jomo Kenyatta Universityof Agricculture and Technology, Nairobi, Kenya
2 School of Chemical and Metallurgical Engineering, University of the Witwtaresrand, Johanesburg, South Africa
3 Deaprtment of Mechanical Engineering Science, University of Johannesburg, South Africa
4 Materials, Design & Manufacturing Group (MADEM), Department of Mechanical Engineering, Dedan Kimathi University of Technology, Nyeri, 10143, Kenya
5 African Academy of Sciences, PO Box 24916-0052, Nairob, Kenya
Accepted: 13 January 2021
In this study, we show that optimising cutting forces as a machining response gave the most favourable conditions for turning of Ti-6Al-4V alloy. Using a combination of computational methods involving DEFORM simulations, Taguchi Design of Experiment (DOE) and analysis of variance (ANOVA), it was possible to minimise typical machining response such as the cutting force, cutting power and chip-tool interface temperature. The turning parameters that were varied in this study include cutting speed, depth of cut and feed rate. The optimum turning parameter combinations that would minimise the machining responses were established by using the “smaller the better” criterion and selecting the highest value of Signal to Noise Ratio. Confirmatory simulation revealed that using cutting speed of 120 m/min, 0.25 mm depth of cut and 0.1 mm/rev feed rate, the lowest cutting force of 88.21 N and chip-tool interface temperature of 387.24 °C can be obtained. Regression analysis indicated that the highest correlation coefficient of 0.97 was obtained between cutting forces and the turning parameters. The relationship between cutting forces and the turning parameters was linear since first-order regression model was sufficient.
Key words: Ti-6Al-4V machining / cutting forces / finite element analysis / ANOVA / regression
© J. O. Obiko et al., Published by EDP Sciences 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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