The present work has been focused on cutting force (Fc) and analysis of machined surface in turning of AA 6061 alloy with uncoated and PVD-TiB2 coated cutting inserts. Turning tests have been conducted on a CNC turning under dry cutting conditions based on Taguchi L18 (21 × 33) array. Kistler 9257A type dynamometer and equipment have been used in measuring the main cutting force (Fc) in turning experiments. Analysis of variance (ANOVA) has been applied to define the effect levels of the turning parameters on Fc and Ra. Moreover, the mathematical models for Fc and Ra have been developed via linear and quadratic regression models. The results indicated that the best performance in terms of Fc and Ra was obtained at an uncoated insert, cutting speed of 350 m/min, feed rate of 0.1 mm/rev, and depth of cut of 1 mm. Moreover, the feed rate is the most influential parameter on Ra and Fc, with 64.28% and 54.9%, respectively. The developed mathematical models for cutting force (Fc) and surface roughness (Ra) present reliable results with coefficients of determination (R2) of 96.04% and 92.15%, respectively.
This work focuses on developing the mathematical model of surface roughness (Ra) in the turning of Inconel 625 superalloy with cryogenically treated tungsten carbide inserts. The influence of cryogenic treated on the microstructure and hardness of tungsten carbide tools was also investigated for the as-received inserts and deep cryogenic treatment at − 196 °C for 12, 24, and 36 h conditions. Turning experiments have been performed according to an orthogonal array L16 with three parameters (cutting tool, feed rate, cutting speed) at different levels with a 1 mm depth of cut. The ideal cutting tool and cutting parameters were evaluated in terms of the surface roughness (Ra). Analysis of Variance has been applied to determine the percentage of each cutting factor. It has been observed that the cutting speed has a maximum with 66.28% contribution on Ra. The best optimal turning parameters are obtained as A3B3C1 according to S/N ration. The mathematical model of Ra has been developed by regression analysis. The developed model is tested with verification experiments and found to be in good agreement with the experimental results.
This study focuses on optimization of cutting conditions and numerical analysis of flank wear in milling of Inconel 625 superalloy using PVD AlTiN and CVD TiCN/Al2O3/TiN-coated carbide inserts. The milling experiments have been performed in CNC vertical machining centre according to Taguchi L18 orthogonal array. Finite element modelling of tool wear was performed using Deform 3D software. Analysis of variance was utilized to define the influences of the milling conditions on Vb. The results showed that the feed rate (with 41.5% contribution rate) is the most important parameter affecting Vb. The linear and quadratic regression analyses were used to estimate the results of the test. The regression analysis results showed that the estimated Vb values achieved by the quadratic regression model were more effective compared to the linear regression model. Statistical results revealed that the Taguchi method was successful to define optimum cutting parameters in the milling of Inconel 625.
The main objective of this work is to experimentally investigate and statistically evaluate the effects of the milling parameters on surface roughness (Ra) and flank wear (Vb) in the milling of Inconel 625. Thus, milling experiments on different cutting conditions with Physical vapor deposition (PVD) and Chemical vapor deposition (CVD) coated inserts have been conducted on CNC milling machine according to Taguchi L18 orthogonal array. The effect levels of the milling conditions on Ra and Vb have been determined with analysis of variance (anova) at 95% confidence level. The analysis results indicate that the cutting tool is the most significant parameter affecting Ra while the feed rate is the most significant parameter affecting Vb. Then the linear and quadratic regression models have been applied in order to estimate Ra and Vb. The results show that a higher correlation coefficient ([Formula: see text] is obtained via the quadratic regression model with a value of 0.97 for both Ra and Vb. Finally, the verification results are in excellent agreement with experimental findings, regarding the surface roughness (Ra), and tool wear (Vb).
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