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Titanium alloys are extensively used in aerospace, missiles, rockets, naval ships, automotive, medical devices, and even the consumer electronics industry where a high strength to density ratio, lightweight, high corrosion resistance, and resistance to high temperatures are important. The machining of these alloys has always been challenging for manufacturers. This article investigates the combined effect of radial depth, cutting speed and feed rate on cutting forces, tool life, and surface roughness during face milling of Ti6Al4V alloy. This study focuses on the significance of radial depth of cut on cutting force, tool life and surface roughness compared to that of cutting speed and feed rate during face milling of Ti6Al4V alloy. In this paper, mono and multi-objective optimization of the response characteristics have been conducted to find out the optimal input parameters, namely, cutting speed, feed rate, and radial depth of cut. Taguchi method and analysis of variance (ANOVA) analysis have been used for mono-objective optimization, while Taguchi-based grey relational analysis has been used for multi-objective optimization. The regression analysis has been performed for developing mathematical models to predict the surface roughness, tool life, and cutting forces. According to ANOVA analysis, the most significant parameters for tool life and cutting force (FY) are cutting speed, and radial depth of cut, respectively, while feed rate is observed to be the most significant parameter for surface roughness and force (FX). The optimal combination of input parameters for tool life and FY are 50m/min cutting speed, 0.02mm/rev feed rate, and 7.5mm radial depth of cut. However, the optimal parameters for surface roughness are 65m/min cutting speed, 0.02mm/rev feed rate, and 7.5mm radial depth of cut. For FX, the optimal condition is observed as cutting speed 57.5m/min, 0.02mm/rev feed rate, and 7.5mm radial depth of cut. A validation experiment, conducted at the optimal parameters of surface roughness, shows an improvement of 31.29% compared to the surface roughness at initial condition. Taguchi-based grey relational analysis for multi-objective optimization shows an improvement of 55.81%, 6.12%, and 23.98% in tool life, surface roughness, and FY, respectively. ANOVA analysis based on grey relational grade shows that the radial depth of cut is the most significant parameter for multi-objective optimization during the face milling of Ti6Al4V.


© 2021 The Author(s). Published by Elsevier B.V.

Publication Title

Procedia Manufacturing





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