Investigating the Effect of Cutting Parameters on Average Surface Roughness and Material Removal Rate during Turning of Metal Matrix Composite Using Response Surface Methodology (original) (raw)
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Optimization of Surface Roughness during Turning of Aluminium based Metal Matrix Composites
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.
In modern era of manufacturing, the Hybrid Metal matrix composites (HMMC) are most advanced material, which are mostly used in today's industries. In this paper we calculate the influence of most prominent parameters of CNC turning machine on material removal rate (MRR) and surface roughness (SR) of the hybrid composite material. Turning process parameters i.e. Feed, Speed and Depth of Cut are considered and have calculated their response in term of MRR and SR. To develop a hybrid metal matrix composite material Aluminium Al6061 as a base material and silicon carbide (10%wt) and graphite (3%wt) particles as reinforcements are used. Stir casting process was used to fabricate the hybrid composite because of easy setup and cheap method of fabrication. To optimize our output parameters, RSM (response surface methodology) is used. We diagnosed the best combination of input parameters for the maximum output (MRR) and minimum (SR). TNMG160408, TNMG2000 and K10 tool inserts are used as cutting tool. The purpose of the present study is to calculate the optimum setting of process parameters for better output results.
Present study concerns with a multi-parameter experimental and statistical analysis of surface texture in turning of stainless steel flakes reinforced cast aluminum matrix particulate composite (AMPC). Spindle speed, n (rpm) and feed rate, f (mm/rev) were assigned to an L9 Taguchi orthogonal array as independent variables whilst depth of cut was kept constant. The response/output (performance characteristic) was an increased number of surface roughness parameters including amplitude, hybrid, statistical and fractal ones. The multi-parameter analysis of surface finish was selected since the evaluation of the roughness with one or two only parameter is ambiguous. The correlation of these parameters with the machining conditions was investigated. Then, statistical analysis and was applied to quantitatively allow exploration of the effect which each machining input yields on roughness outputs. Regression equations were constructed then, in order to develop prediction models with the possible lower estimation error.
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.
2021
The demand for new class of specific materials is rising day by day. In this context machining plays an vital role to finish the final product according to required geometry. Lightweight material such as aluminum based metal matrix composites is one of the most suitable material for most of the engineering and structural works. In this research work, an effort is made to optimize the process parameters of 16-TC Botliboi CNC machine to get desired output responses of MRR, surface roughness (SR) and tool flank wear during the processing of previously developed aluminum hybrid composite A359/B4C/Al2O3. Response surface methodology (RSM) has been applied to get the mathematical model of input parameters to get the desired response. The results reveal the optimized value of feed rate, rotational speed and depth of cut to experimentally find the outputs. Further confirmation experiments are performed at optimum level of process parameter.
2019
In manufacturing industries, for determining the quality, surface finish of a product is very important.This study focuses on different effects due to different reinforcement on aluminium based metal matrix composite. The objective of the current research is to minimize surface roughness. Al 7075 is used as a matrix and as reinforcement RHA & GSA are used in varying proportion i.e.2%, 3.5%, 5% respectively. Different cutting parameters have different influence on the surface finish. The specimen was turned under different levels of parameters and measured the surface roughness using a Taylor Hobson’s Surtronic 3+. From hardness test it is concluded that metal matrix composites (Al-RHA-GSA) have higher hardness value than pure Al 7075 alloy and as the increase in percentage of reinforcement, hardness value also increases
2013
The objective of this research work is to apply taguchi method to investigate the effects of turning parameters such as cutting speed, depth of cut and feed rate on surface roughness, in Turning of Al-Al2O3 MMC using TiC coated HSS cutting tool. The Signal–to- Noise (S/N) ratio, the analysis of variance (ANOVA), and regression analysis are used to analyze the effect of drilling parameters on the quality characteristic of machined work piece. A series of experiments are performed based on L9 orthogonal array. The experimental results obtained are analyzed using MINITAB software package. Linear regression equations are used to develop a statistical model with an objective to establish a correlation between the selected drilling parameters with the quality characteristics of the machined work piece.
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.
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