CORRELATION OF SURFACE ROUGHNESS PARAMETERS WITH OPERATIONAL VARIABLES IN TURNING OF A NEW ALUMINUM MATRIX STEEL PARTICULATE COMPOSITE: A MULTI-PARAMETER ANALYSIS (original) (raw)
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NEWTECH 2017: Proceedings of 5th International Conference on Advanced Manufacturing Engineering and Technologies, 2017
Metal matrix composites (MMCs) represent a new generation of engineering materials in which a strong reinforcement is incorporated into a metal matrix to improve its properties including specific strength, specific stiffness, wear resistance, corrosion resistance and elastic modulus. Aluminum matrix composites (AMCs), a specific type of MMCs, are rapidly replacing conventional materials in various engineering applications, especially in the aerospace and automobile industries due to their attractive properties. From the literature already published it is evident that the machining of AMCs is an important area of research, but only very few if any studies have been carried out using metal particles reinforced AMCs. A multi-parameter analysis of surface finish imparted by turning to a new L316 stainless steel flake-reinforced aluminum matrix composite is presented. Surface finish is investigated by examining a number of surface texture parameters. Spindle speed as well as feed rate was treated as the independent variables under a constant depth of cut whilst roughness parameters were considered as the responses under an L9 orthogonal array experimental design. ANOVA analysis was also conducted to study the effect of the two cutting variables on the surface texture responses. Keywords Surface texture Á Aluminum matrix particulate composite (AMPC) Á Stainless steel flakes (SSF) Á Turning Á Multi-parameter analysis
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Application of aluminum based metal matrix composites can be found in many manufacturing industries such as aircraft and aerospace components, marine fittings, transport, drive, shafts, brake components, valves, couplings. Metal matrix composites are heterogeneous material and they impose machinability issues with conventional turning process. This causes the deterioration of surface finish after machining. Thus, this paper aimed at conducting experiments on aluminum based metal matrix composites and investigating the influence of machining process parameters such as cutting speed (m/min), feed rate(mm/rev), depth of cut (mm) and nose radius (mm) on surface roughness. Experiments were carried out on CNC machine tool and coated tungsten carbide inserted cutting tool was used for machining. Further in this study an empirical model was developed for predicting surface roughness in terms of spindle speed, feed rate, depth of cut and nose radius using multiple regressions modeling method. At last, genetic algorithm has been employed to find out the optimal setting of process parameters that optimize surface roughness value. This provides a good flexibility to the manufacturing industries by selecting the good parameters setting as per the requirement.
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Materials Today: Proceedings, 2018
Influence of machining process parameters on the surface roughness characteristics (i.e. Rz and Rt) was investigated and optimized during turning Al 7075/SiC p MMC with uncoated tungsten carbide inserts in dry environment. Analysis of Variance (ANOVA) revealed that for Rz, feed was the most significant parameter followed by spindle speed; and the influence of depth of cut was insignificant. Similarly, spindle speed was the most significant parameter for Rt, followed by depth of cut and feed. Linear regression models were generated for both the responses; and their adequacy was verified.
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Materials Today: Proceedings, 2018
Influence of machining process parameters on the average surface roughness (Ra) and chip reduction coefficient (Z) was investigated and optimized during turning heat treated aluminium matrix composite (AMC) by multilayer TiN coated tungsten carbide inserts in dry environment. Analysis of Variance (ANOVA) revealed that feed was the only significant parameter; and the influence of spindle speed and depth of cut was insignificant both for Ra and Z. Linear regression models were developed for the responses; and their adequacy was verified. Morphology of chips formed during turning was also studied.
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Design of experiments is performed to analyse the effect of spindle speed, feed rate and depth of cut on the surface roughness of 6061 Aluminium alloy. The results of the machining experiments were used to characterise the main factors affecting surface roughness by the Analysis of Variance (ANOVA) method. The feed rate was found to be the most significant parameter influencing the surface roughness in the end milling process.
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SURFACE ROUGHNESS ANALYSIS IN MACHINING OF ALUMINIUM ALLOYS (6061&6063
Surface is one of the most significant requirements in metal machining operations. In order to attain enhanced surface quality ,the appropriate setting of machine parameters is important before the cutting operation take place. The objective of this research is to analyze the effect of machining parameters on the surface quality of aluminum alloy in CNC milling operation with HSS tool. A multiple regression model developed with spindle speed, feed rate and depth of cut as the independent variable and surface roughness parameter 'Ra' as the dependent variable. The prediction ability of the model has been tested and analyzed using 'Mini Tap' and it has been observed that there is no significant different between the mean of 'Ra' values of theoretical and experimental data at 5% level of significance. In addition to that, they are going to use Box-Behnken designs method which is used to analyze the surface roughness and it designs when performing non-sequential experiments. That is, performing the experiment once. These designs allow efficient estimation of the first and second-order coefficients. Because boxbehnken designs have fewer design points, they are less expensive to run than central composite designs with the same number of factors.
— Average Surface Roughness (R a) is one of the most frequently used texture parameters to define the quality of turned components. The roughness values of a turned surface much depends on cutting parameters such as cutting speed, feed rate and depth of cut. Optimization of these parameters is very important in relation to surface roughness as they reveal the best suitable conditions for the turning operation. In this project, a correlative study of machining parameters and the surface roughness for ferrous (stainless steel 304) and non–ferrous alloy (Aluminium) material is carried out. Response Surface Methodology (RSM) and Analysis of Variance (ANOVA) techniques are employed in this analysis to explain the influence of different cutting parameters on surface roughness values. The combination of optimum experimental parameters can be found by machining these ferrous and non-ferrous materials in CNC turning center and finding the least surface roughness parameters. ANOVA analysis, integrated with Design Expert software, is used to determine effective ratios of the parameters and subsequently the relationships between input parameters and their responses relationship are established. The minimum surface roughness results in reference to spindle rpm, feed rate, and depth of cut are determined and estimation of the optimal surface roughness values (Ra) for least surface roughness are the results obtained in the study. In case of stainless steel 304, optimal values of cutting speed, feed and depth of cut against the least surface roughness value of 1.33 microns are found to be 220 m. min-1 , 0.2 mm. rev-1 and 0.3 mm respectively. In case of Aluminium, optimal values of cutting speed, feed and depth of cut against the least surface roughness value of 2.8 microns are 200 m. min-1 , 0.2 mm. rev-1 and 1.15 mm respectively. These results reaffirm that RSM and ANOVA techniques are useful and efficient in achieving optimal set of machining parameters for select ferrous and non-ferrous materials in correlating the surface finish values.