Optimization Of Machining Parameters Engineering Essay

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Abstract- In recent years, the utilization of IC engine valve (EN-52) material in many engineering fields has increased tremendously like Bajaj pulsar and TATA Nano vehicles. Accordingly the need for accurate machining of EN-52 has also increased enormously. Despite the recent developments in the near net shape manufacture, IC engine valve parts often require CNC machining to meet dimensional tolerances, surface quality and other functional requirements. In the present work, the surface roughness of EN-52 has been studied in this paper by turning the IC engine valve raw bars using coarse grade polycrystalline diamond (PCD) insert under different cutting conditions. Experimental data collected are tested with analysis of variance (ANOVA) and Taguchi techniques. Multilayer perception model has been constructed with back-propagation algorithm using the input parameters of depth of cut, cutting speed and feed. Output parameter is surface finish of the machined component. On completion of the experimental test, ANOVA and the Taguchi are used to validate the results obtained and also to predict the behavior of the system under any condition within the operating range.

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Keywords- EN-52, Machining, Machinability, Surface roughness, Matlab, Taguchi and Anova.

Introduction

In modern industry the goal is to manufacture low cost, high quality products in short time. Automated and flexible manufacturing systems are employed for that purpose along with computerized numerical control (CNC) machines that are capable of achieving high accuracy and very low processing time. Turning is the most common method for cutting and especially for the finishing machined parts. Furthermore, in order to produce any product with desired quality by machining, cutting parameters should be selected properly. In turning process parameters such as cutting tool geometry and materials, depth of cut, feed rate, cutting speed as well as the use of cutting fluid will impacts on material removal rate and machining quality like surface roughness, roundness of circular and dimensional deviations of the product surface roughness of cutting process has been studied intensively, mostly through experiments. It employed

Taguchi method to investigate cutting characteristics of EN-52 steel bar using tungsten carbide cutting tools. The optimal cutting parameters of cutting speed, feed rate and depth of cut for turning operations with regard to performance index such as tool life and surface roughness are considered. Investigated influence of cutting conditions (cutting velocity and feed) and cutting time on turning EN-52 raw part of IC Engine valve manufacturing processes. An orthogonal array andthe analysis of variance are employed to investigate the cutting characteristics of flank wear (VB), power required (Pm) and surface roughness (Ra) [1]. It took the significant cutting parameters into consideration and used multiple linear regression mathematical models relating to surface roughness height Ra and Rt to the cutting parameters for turning process of EN-52. It is used an orthogonal array and the analysis of variance (ANOVA) to optimization of cutting parameters in turning hardened EN-52 steel (58 HRC) with friction welded high alloy steel as a stem part in an IC Engine valve. The flank wear (VB) and surface roughness (Ra) had investigated a process optimization to determine optimal values of cutting parameters, such as cutting speed, feed rate and depth of cut. It has used Taguchi method to find the optimal cutting parameters for surface roughness in turning operations of EN-52 steel bar using TiN coated tool (TNMA K10 160408). Three cutting parameters namely, insert radius, feed rate and depth of cut are optimized with considerations of surface roughness, and so on [2]. However, very few studies have been conducted to investigate roundness under different turning parameter. Additionally, cutting fluid properly applied can increase productivity and reduce costs by choosing higher cutting speed, higher feed rate and greater depth of cut. Effective application of cutting fluid can also increase tool life, decrease surface roughness, increase dimensional accuracy and decrease the amount of power consumed. The water soluble (Water-miscible) cutting fluid are primarily used for high speed machining operations because they have better cooling capabilities [3]. The fluid is best for cooling machined parts to minimize thermal distortions. Water-soluble cutting fluid were mixed with water at different ratio depending on the machining operation. Therefore, the effect of water-soluble cutting fluid under different ratio was also considered in this study. Planning experiments through the Taguchi orthogonal array has been used quite successfully in process of optimization. This study applied a Taguchi L9 (34) orthogonal array to plan the experiment on turning operations. Four controlling factors including cutting speed, feed rate, depth of cut, and cutting fluid mixture ratio with three levels for each factor were selected. The analysis of variances (ANOVA) is then applied to examine how turning operation factor influence the quality targets of roughness average, roughness maximum and roundness. An optimal parameter combination was obtained. Through analyze the most influential factor for individual quality target of turning operation can be identified. Additionally, the ANOVA was also utilized to examine the most significant factor for the turning process as roughness average, roughness maximum and roundness are simultaneously considered.

Experimental Procedure

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Figure 1. Typical structure of turning process

From Fig.1, in order to achieve the objective of this experimental work, EN-52 of type X45 Cr Si 8 (chromium with 7.5% silicon, 2.44% magnesium, reinforced with 20% volume particles of silicon carbide (SiC) was tested. The silicon carbide particle size ranges from 56 to 185 m. A medium duty lathe with 2kW spindle power was used to perform the experiment. The TNMA 160408 inserts with PCLNR 25 X 25 M12 tool holder with PCD was used to turn the billet of 150-mm diameter. The tool geometry follows: top rake angle 0â-¦, nose radius 0.8 mm. The work material was machined at five different cutting speeds ranging from 100 to 600 m/min with two feed rates of 0.108 and 0.200 mm/rev and depth of cut as 0.25, 0.5 and 0.75 mm. The chemical composition of the work material is given in Table 1. Each experimental trial was carried out for 3 min duration. Experimental parameters are given in Table 2. The average surface finish (Ra) in the direction of tool movement was measured in three different places of the machined surface. With use of surface roughness tester and Mitutoyo Surf. Test-301 with a cut-off and transverse length of 0.8 and 2.5 mm respectively. Finally surface mean roughness (Ra) in micron value of the three locations was considered for the particular trial [6], [3]. The Raw Material properties are given below Table 1.

Physical properties:

Density

7.75 Kg/ cm2

Modulus of elasticity

211.0 N/ mm2

Magnetism

Magnetic

Melting range

1450-1480 °C

Mechanical properties at room temperature:

Yield strength

685 N/ mm2

Tensile strength

880-1100 N/ mm2

Elongation

= >14%

Reduction of area

= > 40 %

Hardness

29-35 HRC min

At elevated Temperature:

Hot tensile strength = 12 N/ mm2

EXPERIMENTAL DATA AND S/N RATIO

The experimental works consist of three replications. The term "signal" represents desirable value and "noise" represents undesirable value. The formula for signal-to-noise was designed such that, the experimentalist can always select the larger factor level setting to optimize the quality characteristics of an experiment. Therefore, the method of calculating the signal-to-noise ratio depends on whether the quality characteristic has smaller-the-best, larger-the-better or normal-the-better formulation is chosen. The equation for calculating S/N ratio for low band (LB) characteristics (in dB) is

S/N HB = 10 loge (1/n) (Sm1-Ve1)

Ve1

ANALYSIS OF VARIENCES

Analysis of variance is a method of portioning variability into identifiable source of variation and the associated degree of freedom in an experiment. The frequency test (F-test) is utilized in statistics to analyze the significant effects of parameters, which form the quality characteristics. Table 2 shows the result of ANOVA analysis of S/N ratio for surface roughness. This analysis was carried out for a level of significance of 5%, i.e. for 95% a level of confidence. The last column of the table shows the "percent" contribution (P) of each factor as the total variation, indicating its influence on the result. From the analysis of Table 2 it is apparent that, F-values of cutting speed, feed rate and depth of cut were all greater than F0.05, 2.26 = 3.146 and have statistical, physical significance on the surface roughness. ACE CNC is one of the automatic and computer numerical control machine. The following are process parameters used in turning process for EN-52 material which is to be optimized,

1. Speed limit between 1600rpm to 2000 rpm

2. Feed limit between 0.06 mm/rev to 0.1mm/rev

3. Depth of cut limit between 0.02mm to 0.06mm

Here the output response is surface finish up to 0.2 Ra. Experiments were planned as per Taguchi's L9 orthogonal array. Following are the trails conducted according to the levels.

Mathematical Modeling

Y ∞ xn

Taking log both sides, log y = nlogx1

Since we have three process parameters so,

logy = nlogx3

By adding the above equations

3logy = n1logx1 + n2logx2 + n3logx3

By applying logy=Y and log x=X then we will get,

3Y=n1X1+n2X2+n3X3

This is objective function. As a part of constraint equation surface finish must lie between 0.1 to 0.8 Ra so,

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n1X1+n2X2+n3X3 ≥ 0.1 Ra,

n1X1+n2X2+n3X3 ≤ 0.8 Ra

Figure 2. Effect of Depth of Cut on Roughness

Figure 3. Effect of Speed on Roughness

Figure 4. Effect of Speed on Roughness

PERFORMANCE ANALYSIS

The mathematical model was developed in the previous section solved using the 'Matlab' software, apart from this solution is also verified using Taguchi technique using the experiments designed in the previous section [6].

>>type objfun2 (Program 1)

function f = objfun(x)

% objective function

function f = objfun(x)

f=(-0.2522+0.0721*x(1)+0.8664*x(2)-0.1964*x(3)-0.4018*x(4));

>>type confuneq2

function [c, ceq] = confuneq(x)

% Nonlinear inequality constraints:

function [c, ceq] = confun(x)

% Nonlinear inequality constraints

c = (-0.1576-0.4531*x (1) +0.9776*x (2)-0.1570*x (3) +0.0186*x (4));

% Nonlinear equality constraints

ceq = []

% Nonlinear equality constraint:

>> % Again, make a guess at the solution

x0= [1 1 1 1 1];

>> % Set optimization options:

% Turn off the large-scale algorithms (the default):

options = optimset('LargeScale','off');

>> % This time we will use the bounded syntax:

% X = FMINCON (FUN, X0, [],[],[],[], LB, UB, OPTIONS, NONLCON)

lb = zeros(1,2,3,4,5); % Lower bounds X >= 0

ub = []; % No upper bounds

>> [x,fval,exitflag,output] = fmincon('objfun2',x0,[],[],[],[],lb,ub,'confuneq2',options);

> In C:\toolbox\optim\private\checkbounds.m at line 26

In C:\toolbox\optim\fmincon.m at line 153

Optimization terminated successfully:

Search direction less than 2*options.TolX and

maximum constraint violation is less than options.TolCon

Active Constraints:

1

2

3

4

8

>> % The solution to this problem has been found at:

x = 3.4320 3.8733 2.7700 0 1.3860

>> % The function value at the solution is:

fval

fval =

-36.2591

>> % The constraint values at the solution are:

[c, ceq] = confuneq(x)

c = -23.2935

ceq = 14.6522

>> % The total number of function evaluations was:

output.funcCountans = 75

Table.3 Optimal results from Matlab

Sr

Elements

Solution

1

Speed

2

2

Feed

1

3

Depth of Cut

3

Result and Discussion

Taguchi method is proved the effective method for finding out the most influential factor, this particular problem is solved in MATLAB nonlinear program with inequality constraint, and the average error is 5.5%. Reason for error is influence of uncontrollable factor in machining process and human error while unloading the product in NC.

Figure 5. Pie chart: Result of Anova

From ANOVA most influence factor is feed and less influence factor is depth of cut, this is proved both Taguchi as well as ANOVA. From the above description it can be justified that experimental analysis were includes process elements and computational analysis by Matlab. The difference between Matlab value and experimentation value is due to uncontrollable factor and also the effects of interaction of parameters were neglected in the design of experimentations, we can modify the mathematical model by incorporating the suitable constant, which can be obtained from the experimentations.