Prediction of surface roughness of Ti6Al4V and optimization of cutting parameters based on experimental design

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Authors

  • Nguyen Van Toan Faculty of Mechanical Engineering, Le Quy Don Technical University
  • Nguyen Thi Hai Van The University of Technology and Education, Da Nang University
  • Nguyen Kim Hung Faculty of Mechanical Engineering, Le Quy Don Technical University
  • Doan Tat Khoa (Corresponding Author) Faculty of Mechanical Engineering, Le Quy Don Technical University

DOI:

https://doi.org/10.54939/1859-1043.j.mst.87.2023.108-116

Keywords:

Ti-6Al-4V alloy; Cutting parameters; Surface roughness; ANOVA.

Abstract

The effect of machining parameters on the surface roughness in dry-turning Ti6Al4V alloy using an experimental design method was investigated. A mathematical equation based on the response surface methodology was established to fully understand the influence of machining parameters (cutting speed, feed rate, and depth of cut) on the surface roughness. A set of experiments based on a three-level statistical full factorial design of the experimental method was performed to collect the mean of surface roughness data. The model of R2=0.9656 shows a good correlation between the experimental results and predicted values. The analysis results from the model revealed that the feed rate is the dominant factor affecting surface roughness, followed by cutting speed, and depth of cut. The surface roughness was minimized when the feed rate and depth of cut are set to the lowest, and the cutting speed was set to the highest level. Verification of the experimental results indicated that the surface roughness of 0.832 µm at cutting speed of 200 m/min, feed rate of 0.1 mm/rev, and depth of cut of 0.1 mm were achieved under the optimal conditions

References

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Published

25-05-2023

How to Cite

TOAN, N. V., N. Thi Hai Van, N. Kim Hung, and D. Tat Khoa. “Prediction of Surface Roughness of Ti6Al4V and Optimization of Cutting Parameters Based on Experimental Design”. Journal of Military Science and Technology, vol. 87, no. 87, May 2023, pp. 108-16, doi:10.54939/1859-1043.j.mst.87.2023.108-116.

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