Cutting Parameter Optimization in Finishing Milling of Ti-6Al-4V Titanium Alloy under MQL Condition using TOPSIS and ANOVA Analysis

  • V. C. Nguyen Department of Manufacturing Technology, Faculty of Mechanical Engineering, Hanoi University of Industry, Vietnam
  • T. D. Nguyen Department of Machine Tool and Tribology, School of Mechanical Engineering, Hanoi University of Science and Technology, Vietnam
  • D. H. Tien Department of Manufacturing Technology, Faculty of Mechanical Engineering, Hanoi University of Industry, Vietnam
Volume: 11 | Issue: 1 | Pages: 6775-6780 | February 2021 |


Titanium and its alloys give immense specific strength, imparting properties such as corrosion and fracture resistance, making them the right candidate for medical and aerospace applications. There is a wide range of engineering applications that use titanium alloys in a variety of forms. The cost of these alloys is slightly higher in comparison to other variants due to the problematic extraction of the molten process. To reduce costs, titanium alloy products could be made by casting, isothermal forging, radial swaging, or powder metallurgy, although these techniques require some kind of finishing machining process. Titanium and its alloys are difficult to machine due to skinny chips leading to a small cutting tool-workpiece contact area. The thermal conductivity of titanium alloys is too low and the stress produced is too large due to the small contact area, which results in very high cutting temperatures. This paper deals with the experimental study of the influence of the Minimum Quantity Lubricant (MQL) environment in the milling of Ti-6Al-4V alloy considering the optimization of surface roughness and production rate. Taguchi-based TOPSIS and ANOVA were used to analyze the results. The experimental results show that MQL with vegetable oil is successfully applied in the milling of Ti-6Al-4V. The research confirms the suitability of TOPSIS in solving the Multiple Criteria Decision Making (MCDM) issue, by choosing the best alternative at Vc=120m/min, fz=0.065mm/tooth, and ap=0.2mm, where the surface roughness and material removal rate are 0.41µm and 44.1492cm3/min respectively. Besides, ANOVA can be used to predict the best parameters set in the milling process based on the regression model. The parameters predicted by ANOVA analysis do not coincide with any implemented parameters

Keywords: surface milling, surface roughness, Taguchi-based TOPSIS, titalium alloy, Ti-6Al-4V, MCDM, MQL


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