Determination of Optimum Machining Parameters for Face Milling Process of Ti6A14V Metal Matrix Composite

Abstract

This paper shows the novel approach of Taguchi-Based Grey Relational Analysis of Ti6Al4V Machining parameter. Ti6Al4V metal matrix composite has been fabricated using the powder metallurgy route. Here, all the components of TI6Al4V machining forces, including longitudinal force (Fx), radial force (Fy), tangential force (Fz), surface roughness and material removal rate (MRR) are measured during the facing operation. The effect of three process parameters, cutting speed, tool feed and cutting depth, is being studied on the matching responses. Orthogonal design of experiment (Taguchi L9) has been adopted to execute the process parameters in each level. To validate the process output parameters, the Grey Relational Analysis (GRA) optimization approach was applied. The percentage contribution of machining parameters to the parameter of response performance was interpreted through variance analysis (ANOVA). Through the GRA process, the emphasis was on the fact that for TI6Al4V metal matrix composite among all machining parameters, tool feed serves as the highest contribution to the output responses accompanied by the cutting depth with the cutting speed in addition. From optimal testing, it is found that for minimization of machining forces, maximization of MRR and minimization of Ra, the best combinations of input parameters are the 2nd stage of cutting speed (175 m/min), the 3rd stage of feed (0.25 mm/edge) as well as the 2nd stage of cutting depth (1.2 mm). It is also found that hardness of Ti6Al4V MMC is 59.4 HRA and composition of that material remain the same after milling operation.

Experimental Investigations and Effect of Nano-Powder-Mixed EDM Variables on Performance Measures of Nitinol SMA

“…Entire experimentations were carried out using Taguchi’s DOE approach as it offers minimum experimentations with robust parametric combinations [ 34 , 35 ]. This, in turn, saves time and cost and also provides a relationship between the design variables and performance measures [ 36 ]. Entire trials were repeated thrice to obtain superior precision and reproducibility of the results.…”

Estimation of cutting forces in CNC slot-milling of low-cost clay reinforced syntactic metal foams by artificial neural network modeling

“…Machining variables like cutting speed, feed rate and depth of cut are the major parameters influencing the operation outputs such as cutting force, surface roughness and dimensional accuracy. Therefore, in this research, all cutting process parameters were evaluated in the light of previous technical efforts (Szalóki and Viharos, 2018; Boswell et al ., 2017; Karabulut, 2015; Das et al ., 2022) and were selected as shown in Table 4.…”

Corrosion Behavior of Friction Stir Welded AA8090-T87 Aluminum Alloy

“…The corrosion test coupons have been extracted from the welded area of size 30 mm × 30 mm × 5 mm, polished and immersion corrosion test was conducted by dipping the sample in 57 g/L NaCl (neither acidic nor alkaline) + 10 mL/L H 2 O 2 (a weak acid) solution for 6 h at 32 °C as per ASTM standard G110 [ 26 ]. Scanning electron microscopy images were taken using Jeol 6000 plus microscope (Akishima, Japan) to investigate the sub surface after etching the sample using 85 mL distilled water, 15 mL HF, 5 mL H 2 SO 4 [ 27 , 28 , 29 ]. Electron Back Scatter Diffraction (EBSD) analysis has been carried out to analyze the grain boundary mis-orientation.…”