OPTIMIZATION AND PREDICTION OF ALUMUNIUM 3003 ALLOY USING
GENETIC ALGORITHM
N.Muthuprakash,
Assistant Professor Coimbatore Institute of Engineering And Technology [email protected]
V.Sivaprakash,
Assistant Professor Coimbatore Institute of Engineering And Technology
R.Venkatramanan,
Assistant Professor Coimbatore Institute of Engineering And Technology
V.Rajkumar,
Assistant Professor Coimbatore Institute of Engineering And Technology
Abstract
The aim of present research focuses on the prediction of machining parameters that improve the quality of surface finish. The surface roughness is one of the important properties of workpiece quality in the CNC turning process. An effective approach of optimization techniques GA and Response surface methodology has been implemented .The factors investigated were spindle speed, feed rate and depth of cut. The parameters that affect the turning operation are vibration, tool wear, surface roughness etc. Among this surface roughness is an important factor that affects the quality in manufacturing process. The main objective of this paper is to predict the surface roughness on Al 3003, by optimizing the input parameters such as spindle speed (α), feed rate (β) and depth of cut (γ) by using carbide tool. A second order mathematical model is developed using regression technique and optimization is carried out using Box-Behnken of response surface methodology. The experimental results indicate that the proposed mathematical models suggested could adequately describe the performance indicators within the limits of the factors that are being investigated. The spindle speed is the most significant factor that influences the surface roughness and. However, there are other factors that provide secondary contributions to the performance indicators. Therefore, this study attempts the application of response surface methodology and genetic algorithm to find the optimal solution of the cutting conditions for giving the minimum value of surface roughness.
Key words: Genetic algorithm, Optimization, RSM, Surface roughness
I. Introduction
Genetic Algorithms: The system is mainly based on a powerful optimization technique tool. GA is a part of the evolutionary algorithms that copy intelligence of nature in order to find global extremities on the given function problem(zeelan basha, 2013).
The response surface methodology (RSM) is a procedure able to determine a relationship between independent input process parameters and output data (process response). This procedure includes six steps . These are, define the independent input variables and the desired output responses, adopt an experimental design plan, perform regression analysis with the quadratic model of RSM, calculate the statistical analysis of variance (ANOVA) for the independent input variables in order to find parameters which significantly affect the response, determine the situation of the quadratic model of RSM and decide whether the model of RSM needs screening variables or not and finally, optimize, conduct confirmation
experiment and verify the predicted performance
characteristics. (Jenn- Hamdi, 2011).
the prediction of machining parameters that improve the quality of surface finish. The surface roughness is
one of the important properties of work piece quality in the CNC (Computer Numerical Control) turning process. An effective approach of optimization techniques genetic algorithm (GA) and response surfacemethodology (RSM) was
implemented to investigate the effect of the cutting parameters such as cutting speed, feed rate, and depth of cut on the surface roughness. (zeelan basha, 2013).
Aluminium and its alloys are the most versatile and acceptable engineering materials because of their unique characteristics such as high strength-to-weight ratio, more resistance to
corrosion, high thermal and electrical conductivity,
nontoxicity, reflectivity, and ease of formability and machinability. They have become the world's second most used metal after steel. The principal uses of aluminium and its alloys are in aerospace components, automobile components, electrical appliances, consumer durables, portable tools, etc. (Amit, 2011).
The effects of cutting speed, feed rate, work piece hardness and depth of cut on surface roughness and cutting force components in the hard turning were experimentally investigated. Four-factor (cutting speed, feed rate, hardness and depth of cut) and three-level fractional experiment designs completed with a statistical analysis of variance (ANOVA) were performed. Mathematical models for surface roughness and cutting force components were developed using the
response surface methodology (RSM) (HamdiAouici,
2014).The best surface roughness was achieved at the lower feed rate and the highest cutting speed.
training data set, different models for surface roughness were developed by genetic programming (M. Brezocnik, 2004). Accuracy of the best model was proved with the testing data. It was established that the surface roughness is most influenced by the feed rate, whereas the vibrations increase the prediction accuracy.
In this research work, the present study is focused on delimiting accurately the working parameters’ domain of turning. Also, the effect of process Parameters on the surface roughness for different values has been investigated. Moreover, this work treats the effect of spindle speed, feed rate and depth of cut on surface roughness in turning of Al 3003 with carbide tool using the RSM. Optimum cutting conditions with respect to the surface roughness parameters with the help of response optimization technique are proposed
II. METHODOLOGY Development of design matrix
Recording the responses
Development of mathematical model
Checking the adequacy of model
Figure- 1 Methodology flow chart
III.EXPERIMENTATION METHODOLOGY A. ALUMUNIUM WORK PIECE
3003 aluminum alloy is an alloy in the
wrought aluminum-manganese family. It can be cold worked to produce tempers with a higher strength but a lower ductility. Like most other aluminum-manganese alloys, 3003 is a general-purpose alloy with moderate strength, good workability, and good corrosion resistance.. The (table 1) below shows the chemical composition of aluminium 3003. (Standard Handbook for Mechanical Engineers)
Table 1 chemical composition for Al 3003
Weight (%) 3003
Al Bal
Si 0.6
Fe 0.7
Cu 0.05-0.20
Zn 0.10
B.MACHINING TOOL
Carbide Inserts for Turning Aluminium Grade AK10 (K10 Uncoated Micro–Grain Carbide Ground with Polished Surface) Main application are Aluminium, Copper Alloys, Plastics and Abrasive Materials, extended application – finishing Stainless AK10 Carbide Inserts for Turning Ground and Polished for Aluminium Uni-tip was used for turning shown fig.2.
Figure- 2 Cutting tool AK10 carbide insert
C.EXPERIMENTAL SET UP AND PROCESS
PARAMTERS
The experimental work was divided into two series:
The main aim of the first experiments series was the determination of the turning domain and the quantification of surface roughness evolution. This was carried out through facing operation with continuously varying cutting parameters. The purpose behind the second experiments series was to investigate the effects of cutting parameters on surface roughness, then to establish a correlation between them using the response surface methodology (RSM).Machining process was carried out in CNC lathe. The measurements of average surface roughness (Ra) were taken on surface roughness Tester SJ-210P.Three measurements of surface roughness were obtained at different surface of machined work piece and average value is used in the further analysis
D. SURFACE ROUGHNESS TESTER SJ-210
Roughness plays an important role in determining how a real object will interact with its environment. Rough surfaces
usually wear more quickly and have
higher friction coefficients than smooth surfaces Roughness is often a good predictor of the performance of a quality of the mechanical component, since irregularities in the surface may form nucleation sites for cracks or corrosion. On the other hand, roughness may promote adhesion. The Surface roughness tester used for measuring surface roughness (Ra) in this experimental analysis shown in fig.3
Figure- 3 Surface roughness Tester SJ-210P E.CNC LATHE
Figure- 4 CNC XL Turning Lathe Specification:
Control system-Fanuc emulated Spindle power-1.5Hp
Spindle speed-100 to 3500 rpm Max. Turning dia-34mm
Rapid traverse rate-1.4m/min
F.COMBINATION OF PARAMETERS AND THEIR LEVELS
In order to investigate the influence of machining parameters on surface roughness, three principal machining parameters, including the spindle speed (α), feed rate (β), depth of cut (γ)), were specified as machining parameters. In this study, these machining parameters were chosen as the independent input variables.
The desired responses were the surface roughness and the surface finish which are assumed to be affected by the above three principal machining
In this experimental analysis the parameters has been conductedin three levels -1, 0, 1 shown in table 2
Table 2 Combination of Parameters and their levels
G. EXPERIMENTAL VALUES 1) Machining parameters
x- Spindle speed (rpm) y- Feed rate (mm/min) z- Depth of cut (mm)
2) Responses
R -surface roughness Ra (μm)
Table 3 Actual machining parameters
H.ANOVA
Analysis of variance (ANOVA) tests the hypothesis that the means of two or more populations are equal. ANOVAs assess the importance of one or more factors by comparing the response variable means at the different factor levels. The null hypothesis states that all population means are equal while the alternative hypothesis states that at least one is different. The ANOVA of the actual values are given in figure 5. Significant at 95% confidence level
Figure- 5 Analysis of Variance Machining
parameter
Units Levels
-1 0 1
Spindle Speed rpm 1200 1700 2200
Feed Rate mm/min 0.08 0.1 0.12
Depth of Cut mm 0.4 0.6 0.8
Exp.no Machining Parameters
responses
x y z R
1
1700 0.12 0.4 0.62312
1200 0.08 0.6 0.683
1200 0.1 0.4 0.694
1200 0.12 0.6 0.675
1700 0.12 0.8 0.616
1700 0.1 0.6 0.637
2200 0.12 0.6 0.598
1200 0.1 0.8 0.699
2200 0.1 0.8 0.5410
1700 0.1 0.6 0.6411
2200 0.08 0.6 0.5212
1700 0.1 0.6 0.61313
1700 0.1 0.6 0.62514
1700 0.08 0.4 0.63815
1700 0.1 0.6 0.62916
1700 0.08 0.8 0.639I. MATHEMATICAL MODEL
Regression equations were formed using values of the coefficients of the polynomials .A statistical software design expert 10.1 was used to calculate the values of these coefficients. The second order mathematical model is developed using the experimental values and responses by neglecting the insignificant coefficients to predict the surface roughness to improve the surface finish.
The regression equation for Surface Roughness Ra (Y) is
R=+1.09121-1.67010E-004* spindle speed -4.67687* feed rate+0.082438* depth of cut +2.00000E-003* spindle speed * feed rate
+7.50000E-005* spindle speed * depth of cut-0.88125* feed rate * depth of cut-6.48500E-008* spindle speed2+9.53125*
feed rate2-0.092188* depth of cut
V.RESULTS AND DISCUSSIONS Prediction Vs Actual
Fig. 4 revealed that they have no obvious pattern and
Unusual structure thus this implies that the models proposed is adequate and there is no reason to suspect any violation of the independence or constant variation assumption
Fig.5. Prediction Vs Actual
Confirmation Report
The confirmation report shows that the confidence level is 95% is prefered for the factors cutting speed,feed rate,depth of cut for finding the significant adequecy in analysis of variance.
Best Fitness Using Genetic Algorithm Tool.
The system is mainly based on a powerful optimization technique tool. GA is a part of the evolutionary algorithms that copy intelligence of nature in order to find global extremities on the given function problemConsidering Population 100, Current generation 52 The Optimization values for cutting parameters are Spindle speed=1200 rpm
Feed rate=0.12mm/min, Depth of cut=0.8mm,
Best fitness of surface roughness is 0.6122µm
Figure 4Surface Roughness Testing
CONCLUSION
This investigation utilizing the application of genetic algorithm and recommends that optimal solution of the cutting conditions obtained on spindle speed is 1200 rpm, feed rate is 0.12 mm/min and depth of cut 0.8mm for achieving the minimum value of surface roughness 0.6122µm using matlab software 8.0.
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