Optimization of machining parameters With Coated Carbide Tool Using PSO method

Raghunath.K1, Rajkumar.D

2, Rajesh.S

3

1, 2, Third Year Mechanical Engineering, Kalasalingam University, Krishnankoil.

3: Assistant Professor, Mechanical Engineering, Kalasalingam University, Krishnankoil.

Contact No:+91 94861237641, +91 9486225660

2.

Mail ID: ragunathkrishnan@gmail.com1, kdkumar2001@gmail.com

2

Abstract:Globalization of world market creates a challenge in products marketing. Due to the high

competition induces the manufactures to produce the better quality products within a short period of time

as well as low cost. Précised product could be produced while utilizing the machines at optimum working

condition. Optimum machining parameters are of great concern in the manufacturing environments,

where the economy of the machining operation plays a key role in competitiveness in the market. This

project is based on Particle Swarm Optimization (PSO) method to optimize turning operation with

multiple performance characteristics using MAT LAB Program. Solving multiple performance

characteristics problem with conventional technique like Taguchi would be difficult one. In order

alleviate this problem, in this work attempt has been made to optimize the machining parameters with the

help of PSO. In this present work, Surface roughness and material removal rate were taken as output

parameters to optimize the important input parameters like speed, feed and depth of cut. The result revels

that, the PSO technique is very useful to optimize the turning parameters.

Key words: speed, feed, Depth of Cut, Particle swarm optimization, MATLAB, Surface roughness,

material removal rate

I. Introduction

Quality and productivity play significant role in

today’s manufacturing market. From customers’

viewpoint quality is very important because the

extent of quality of the procured item (or

product) influences the degree of satisfaction of

the consumers during usage of the procured

goods. Therefore, every manufacturing or

production unit should concern about the quality

of the product. Apart from quality, there exists

another criterion, called productivity which is

directly related to the profit level and also

goodwill of the organization. Every

manufacturing industry aims at producing a

large number of products within relatively lesser

time. In this project EN8 material is taken work

piece material and titanium coated tungsten

carbide tool is taken for optimization of process

parameters.

EN-8 finds wide varieties of application not only

for forgings, castings, axle, shafts, crankshafts

and connecting rods but also used as low cost

die material in tool and die making industries.

This steel can be hardened and tempered to

provide a greater strength and wear resistance in

comparison to low carbon steels. Optimization is

the process of finding the conditions that gives

the maximum and minimum value of the

objective functions. This study investigated the

optimization of CNC turning operation

parameter for AISI 1040 steel using the Particle

swam Optimizations method. The controllable

input parameters were the speed (rpm), feed

(mm/rev), and depth of cut (mm). Nine

experimental runs based on Particle swam

Optimization method were performed. The

property of surface finish is selected as the

quality targets or the response variables. An

optimal parameter combination of the turning

operation was obtained via Particle swam

Optimizations method. By analyzing the matrix,

the degree of influence for each controllable

process factor onto the individual quality targets

can be found. The optimal parameter

combination is then tested for accuracy of

conclusion with a test run using the same

parameters.

II. Experimental Procedure

The purpose of this study is to establish

a relationship between the machining parameters

and its performance during the machining which

includes surface roughness. The turning

experiments were carried out in order to obtain

experimental data in the dry condition on a CNC

Industrial Lathe, which have a maximum spindle

speed of 6000 rpm, maximum turning diameter

150 mm and a maximum power of 10 hp. The

cut material was the EN 8 steel in the form of

round bars with 50 mm diameter and 40 mm

cutting length. EN8 steel Chemical composition

in weight % 0.36%C, 0.02%Mo, 0.20%Ni,

0.27%Si, 0.22%Cu, 0.020%P0.66%Mn,

0.06%Al, 0.21%Cr, 0.016%S, 0.06%.The

cutting tool used was as Titanium Coated

Tungsten Carbide Tool. The physical and

mechanical properties of Titanium Coated

Tungsten Carbide Tool mixed are 2473K

melting point, 3.98g/cm3 in density, 21W/mK

thermal conductivity, 0.01Ωm electrical

resistance, 882mPa bending strength, and

3000HV hardness. Titanium Coated Tungsten

Carbide Tool (VBMT 16 04 08) tool type, was

clamped onto a tool holder specially

manufactured for holding the cutting tool

geometry. The geometry of the insert is as

follows: 5 relief angle, 9.525 mm inscribed

circle and 0.8 mm inch nose radius. The

experimental set up was shown in the Fig 1. In

this study, surface roughness was considered as

the criterion and would affect the results of

cutting process.

Fig 1 Experimental set up

Besides, the measurements of the surface

roughness for being machined surface were

performed by using a Surfcom 130 A with a cut-

off length of 40 mm and sampling length of

30mm. The average surface roughness (Ra) was

used to evaluate the surface roughness of

machined surface.

Table: 1- Factor- Level table

Control factors Level 1 Level 2 Level 3

Speed (rpm) 950 1450 1950

Feed rate (mm/rev) 0.08 0.19 0.30

Depth of cut (mm) 0.8 1.0 1.2

Table-2: orthogonal array L9 (34) for conducting experiment

S. No Speed

(rpm)

Feed

(mm/rev)

Depth of cut

(mm)

Surface roughness (µm)

1 950 0.08 0.8 1.932

2 950 0.19 1.0 2.062

3 950 0.3 1.2 4.693

4 1450 0.08 1.0 0.839

5 1450 0.19 1.2 3.441

6 1450 0.3 0.8 7.015

7 1950 0.08 1.2 1.365

8 1950 0.19 0.8 8.847

9 1950 0.3 1.0 3.029

The table 1 and 2 shows the factor level

table and L9 orthogonal table used for this

experiment. In this study, L9 Taguchi standard

orthogonal array is adopted as the experimental

design. The most suitable array is L9, which

needs 9 runs and has 8 degrees of freedoms

(DOF). 2 and 3 stand for the values of the

factors. The experimental parameters used and

the corresponding responses are given in Table2.

The first column of the table is assigned to the

speed (n), the second to the feed (f), and the

third to the negative back rake angle (dc). The

roughness measurement results are given in the

right and column.

The machining parameters have been

selected based on the data book available for the

cutting insert. In this experiment, Titanium

Coated Tungsten Carbide make sandvik cutting

insert has been used and the recommended

cutting condition is taken for experiment as a

minimum and maximum value of cutting speed.

The machining operation done with help of CNC

turning machine and necessary data for surface

has been measure online with the help of

perthometer as per the ASTM standard (D99).

The values Ra have been measured for every 6

mm of the machined surface. For each

machining the Ra values have been measured at

4 places at average value is shown in the table 2

right column.

III. Result and Discussion

Based on the outcome of the

experimentation result it is decided to use

particle swarm optimization technique to find

optimal parameter for EN 8 material. For this

purpose mat lab code has been developed and it

is used to find the optimal parameter. The output

of the program result that the optimum

parameters are The optimum cutting parameters

obtained in Table III are found to be speed of

1950 rpm, feed rate of 0.3 mm/rev. and depth of

cut 0.80 mm. The fig 2 shows the output of the

PSO

Program and its iterations. The

optimized values derived from the mat lab have

been utilized for conducing the confirmation

experiment. The outcome of the confirmation

shows that result obtained for this parameter

contains 7.8% error. Thus the developed model

will very closer to the expected value.

IV. Conclusion

From the experimentation and

optimization it is evident that the increase in

speed will increase the surface roughness where

as decrease in feed rate and depth of cut will

decrease the surface roughness. It is evident that

if we take many numbers experimentation PSO

will provide better solution than limited of

experiment.