optimization of machining parameters in Aluminum alloy (original) (raw)
Related papers
Optimization of cutting conditions for surface roughness in CNC end milling
International Journal of Precision Engineering and Manufacturing, 2011
The aim of this research is to develop an integrated study of surface roughness to model and optimize the cutting parameters when end milling of 6061 aluminum alloy with HSS and carbide tools under dry and wet conditions. A multiple regression analysis using analysis of variance is conducted to determine the performance of experimental measurements and to show the effect of cutting parameters on the surface roughness. The second-order mathematical models in terms of machining parameters have been developed for each of these conditions on the basis of experimental results. Genetic algorithm (GA) supported with the regression equation is utilized to determine the best combinations of cutting parameters providing roughness to the lower surface through optimization process. The value obtained from GA is compared with that of experimental value and found reliable. It is observed from the results that the developed study can be applied to other machining processes operating under different machining conditions.
Faculty of Mechanical Engineering Universiti Teknologi MARA (UiTM), 2019
Surface roughness is often used as a measure to identify surface integrity of machined parts. The objective of this study was to optimise part surface roughness by investigating the effects of cutting speed, feed rate, depth of cut and tool nose radius on the surface roughness of Aluminium 6061. A five-level L25 Taguchi orthogonal array was modified to accommodate a four-level process parameter. The optimization was conducted on the prediction model generated by use of Response Surface Methodology (RSM) together with Analysis of Variance (ANOVA), and confirmation test validated the predicted values obtained from the Genetic Algorithm (GA). The best combination of parameters for minimum surface roughness was found to be a cutting speed of 250 m/min, feed rate of 0.03 mm/rev, depth of cut of 0.2 mm and tool nose radius of 0.503 mm. The study proves the efficacy of the GA approach in optimisation of machining parameters for improved surface roughness.
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.
2015
1,2,3 Rajasthan institute of Engineering and Technology, Jaipur, Rajasthan Abstract— In this paper we have study on CNC end milling, o affect of various machining parameters like, tool feed (mm/min), speed of tool (rpm), diameter of tool (mm) and depth of cut (mm) .this paper is the result of an experiment conducted on AL 6351 – T6 material with three levels and four factors to optimize process parameter and surface roughness. In this paper we have used a L9 (3*4) Taguchi standard orthogonal array for the purpose of designing experiment (DOE).For the purpose of variation calculation we have used Analysis of variance (ANOVA) method. The result of the experiment is quality product generation at the end, which contribute in the higher and quality productivity. In this experiment we were found that order of significant of main parameter decreasing order is Tool feed, Tool speed and Depth of cut.
Optimization of CNC Turning Process Parameters on ALUMINIUM 6061 Using Genetic Algorithm
N.Zeelan Basha, G.Mahesh, N.Muthuprakash
This paper presents the effect of process parameter in turning operation to predict surface roughness. Application of alumunium 6061 can be found in many manufacturing industries such as aircraft and aerospace components, marine fittings, transport, bicycle frames, camera lenses, drive, shafts, electrical fittings and connectors, brake components, valves, couplings. But some of the limitations during machining of aluminum 6061 are lower strength at elevated temperatures and limited formability affects quality of desired output. A lot of parameters that affect the turning operation are vibration, tool wear, surface roughness etc. Among this surface roughness plays a major role which affects the quality in the manufacturing process. This paper presents the effect of process parameter by considering the Spindle speed, Feed rate and Depth of cut. The main objective of this paper is to predict the surface roughness. Aluminium 6061 is taken into a consideration, machining is done by using coated carbide tool. A second order mathematical model is developed using regression technique of Box-Behnken of Response Surface Methodology (RSM) in design expert software 8.0 and optimization carried out by using genetic algorithm in matlab8.0. This study attempts the application of genetic algorithm to find the optimal solution of the cutting conditions.
The International Journal of Advanced Manufacturing Technology, 2005
Influence of tool geometry on the quality of surface produced is well known and hence any attempt to assess the performance of end milling should include the tool geometry. In the present work, experimental studies have been conducted to see the effect of tool geometry (radial rake angle and nose radius) and cutting conditions (cutting speed and feed rate) on the machining performance during end milling of medium carbon steel. The first and second order mathematical models, in terms of machining parameters, were developed for surface roughness prediction using response surface methodology (RSM) on the basis of experimental results. The model selected for optimization has been validated with the Chi square test. The significance of these parameters on surface roughness has been established with analysis of variance. An attempt has also been made to optimize the surface roughness prediction model using genetic algorithms (GA). The GA program gives minimum values of surface roughness and their respective optimal conditions.
Surface Roughness Prediction Model of 6061-T6 Aluminium Alloy Machining Using Statistical Method
This paper explores on the optimization of the surface roughness of milling mould 6061-T6 aluminium alloys with carbide coated inserts. Optimization of the milling is very important to reduce the cost and time for machining mould. The purposes of this study are to develop the predicting model of surface roughness, to investigate the most dominant variables among the cutting speed, feed rate, axial depth and radial depth and to optimize Surface Roughness Prediction Model of 6061-T6 Aluminium Alloy Machining Using Statistical Method 251 the parameters. Response surface method based optimization approach was used in this study. It can be seen from the first order model that the feed rate is the most significantly influencing factor for the surface roughness. Second-order model reveals that there is no interaction between the variables and response.
Journal of Mechanical Engineering
Surface roughness is often used as a measure to identify surface integrity of machined parts. The objective of this study was to optimise part surface roughness by investigating the effects of cutting speed, feed rate, depth of cut and tool nose radius on the surface roughness of Aluminium 6061. A five-level L25 Taguchi orthogonal array was modified to accommodate a four-level process parameter. The optimization was conducted on the prediction model generated by use of Response Surface Methodology (RSM) together with Analysis of Variance (ANOVA), and confirmation test validated the predicted values obtained from the Genetic Algorithm (GA). The best combination of parameters for minimum surface roughness was found to be a cutting speed of 250 m/min, feed rate of 0.03 mm/rev, depth of cut of 0.2 mm and tool nose radius of 0.503 mm. The study proves the efficacy of the GA approach in optimisation of machining parameters for improved surface roughness.
Prediction of surface roughness of end milling operation using genetic algorithm
G. Mahesh, S. Muthu, S. R. Devadasan, 2014
In the present study, the predictive model is developed to observe the effect of radial rake angle on the end milling cutting tool by considering the following machining parameters: spindle speed, feed rate, axial depth of cut, and radial depth of cut. By referring to the real machining case study, the second-order mathematical models have been developed using response surface methodology (RSM). A number of machining experiments based on statistical five-level full factorial design of experiments are carried out in order to collect surface roughness values. The direct and interaction effects of the machining parameter with surface roughness are analyzed using Design Expert software. The optimal surface roughness value can be attained within the specified limits by using RSM. The genetic algorithm (GA) model is trained and tested in MATLAB to find the optimum cutting parameters leading to minimum surface roughness. The GA recommends 0.25 μm as the best minimum predicted surface roughness value. The confirmatory test shows the predicted values which were found to be in good agreement with observed values.
2018
In end milling, accurate setting of process parameters is extremely important to obtained enhanced surface roughness (SR). Due to a recent innovation in mechanization made it possible to produce high quality manufacturing products. The perceptions of quality in mechanical products are their physical look that is the surface roughness (SR). The aim of this research work is to develop mathematical expression (M.E) and mathematical model using least square approximation method and Response Surface Methodology (RMS) to predict the SR for end milling of Al 6061 alloy. The process parameters that were selected as predictors for the SR are Spindle speed (V), axial depth of cut (a), feed rate (f) and radial depth of cut (d). 30 samples of Al 6061 alloy were carried out using SIEG 3/10/0010 CNC machines and each of the experimental result was measured using Mitutoyo surface roughness tester and Presso-firm. The minimum SR of 0.5 μm were obtained at a spindle speed of 2034.608 rpm, feed rate ...