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Experimental Investigation and Process Parameter

Optimization on En353 with PCBN Inserts
Manickavasaham G 1 Sivakumar P 2,
P.G. Student, Assistant Professor,
Department of Mechanical Engineering, Department of Mechanical Engineering,
University College of engineering, BIT Campus, University College of Engineering, BIT Campus,
Tamil Nadu, India. 1 Tamil Nadu, India. 2

Dr Senthilkumar T 3, Dr Kumaragurubaran B4
Dean, Assistant professor,
University College of Engineering, BIT Campus, Department of Mechanical Engineering,
Tamil Nadu, India. 3 University College of Engineering, BIT Campus,
Tamil Nadu, India.4

Abstract - Machining of materials by super hard tool like PCBN and quality of products in each production phase has to be
is to reduce tool wear to obtain dimensional accuracy, smooth monitored and good corrective actions have to be taken in
surface and more number of parts per cutting edge. Wear of case of deviation from desired output. Surface roughness
tools inevitable due to rubbing action between work material measurement presents an important task in many engineering
and tool edge. However, the tool wear can be minimized by
applications. Many life attributes can be also determined by
using super hard tools by enhancing the strength of the cutting
inserts. Extensive study has been conducted in the past to how well surface finish is maintained.
optimize the process parameters in any machining process to
Surface roughness is also a vital measure as it may
have the best product. Current investigation on turning process
is a Taguchi optimization technique applied on the most influence frictional resistance, fatigue strength or creep life
effective process parameters i.e. feed, cutting speed and depth of machined components. As far as turned components are
of cut while machining the work piece with tool. The concerned, better surface finish (low surface roughness) is
experiments were carried out by a CNC lathe, using PCBN tool important as it can reduce or even completely eliminate the
for the machining of EN 353 steel. The Taguchi technique and need of further machining. Many researchers have found that
ANOVA were used to obtain optimal Turning parameters in surface roughness has bearing on heat transmission, ability
the Turning of SS420 under wet conditions. The optimal factor to hold lubricant, surface friction, wear etc. Despite the fact
for Surface Roughness-A1(Speed - 1500)B2(Feed – that surface roughness plays a very important role in the
0.04)C3(DOC – 0.75), Machining Timing-A1(Speed-
utility and life of a machined component due to its
1500)B2(Feed 0.04)C3(DOC 0.75), Material Removal Rate-
A2(Speed-1750)B1(Feed 0.02)C3(DOC 0.75). The Percentage of dependence on several process parameters and numerous
contribution for each Process parameter is Surface Roughness- uncontrollable factors machining process has no complete
Speed 38.59%, Machining Timing - Speed 35.98%, Material control over surface finish obtained. So the venture of
Removal Rate- Feed 29.83%. controlling process parameters so as to produce best surface
finish is an on-going process varying from various materials
Keywords: Turning, EN 353 steel, PCBN inserts, Surface to tool combinations and the machining conditions. The
Roughness, MRR, and Machining time present work is aimed at studying the influence of the three
major process parameters in a turning operation namely,
1. INTRODUCTION speed, feed and depth of cut and surface roughness for a
Metal cutting is one of the vital processes and predefined combination of material and tool under the given
widely used manufacturing processes in engineering set of machining conditions.
industries. Highly competitive market requires high quality
2. RELATED WORK
products at minimum cost. Products are manufactured by the
Literature is very rich in terms of turning operations
transformation of raw materials. Industries in which the cost
owing to its importance in metal cutting. The three important
of raw material is a big percentage of the cost of finished
process parameters in this research are speed, feed and depth
goods, higher productivity can be achieved through proper
of cut. Surface roughness of a turned work-piece is
selection and use of the materials. To improve productivity
dependent on these process parameters and also on tool
with good quality of the machined parts is the main
geometry. In addition, it is also depends on the several other
challenge of metal industry; there has been more concern
exogenous factors such as: work piece and tool material
about monitoring all aspects of the machining process.
combination and their mechanical properties, quality and
Surface finish is an important parameter in manufacturing
type of the machine tool used.
engineering and it can influence the performance of
mechanical parts and the production costs. The ratio of costs

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Published by : International Journal of Engineering Research & Technology (IJERT)
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Vol. 5 Issue 05, May-2016

Sujan Debnath, Moola Mohan Reddy and Qua Sok surface recognition system. Kirby et al. [18] developed the
Yi [1] studied the effect of various cutting fluid levels and prediction model for surface roughness in turning operation.
cutting parameters on surface roughness and tool wear.
R.K.Bharilya and Ritesh Malgaya [2] investigated the Ozel and Karpat [19] worked on the prediction of
optimization of machining parameters for turning operation surface roughness and tool flank wear by utilizing the neural
of given work piece, the material being carburized Mild steel network model in comparison with regression model. Kohli
(hard material), Aluminium alloys and Brass (soft material) and Dixit [20] proposed a neural network based
which were machined on CNC machine and analysed methodology with the acceleration of the radial vibration of
through the cutting force dynamometer. V.Kryzhaniskyy and the tool holder as feedback. Pal and Chakraborty [21]
V.Bushlys [3] studied the cutting tool temperature that studied on development of a back propagation neural
develops during rough turning of hardened cold-work tool network model for prediction of surface roughness in turning
steel is modeled on the basics of experimental data. operation and used mild steel work piece with HSS as the
Wojciech Zebala and Robert Kowalczyk [4] research the cutting tool for performing a large number of experiments.
cutting forces (Ff, Fp, Fc) when machining of sintered Sing and Kumar [22] studied on optimization of feed force
carbides WC-Co (25% Co) with tools made of through setting of optimal value of process parameters
polycrystalline diamond PCD. Jinming Zho and Volodymyr namely speed, feed and depth of cut in turning of EN24 steel
Bushlya [5] analysis of subsurface microstructural with TiC coated tungsten carbide inserts. Ahmed [23]
alterations and residual stresses caused by machining developed the methodology required for obtaining optimal
significantly affect component lifetime and performance by process parameters for prediction of surface roughness in A1
influencing fatigue, creep, and stress corrosion cracking turning. Zhong et al. [24] predicated the surface roughness of
resistance. Rachid M Saoubi and Tobias Czotscher [6] turned surfaces using networks with seven inputs namely
focused on machinability of power metallurgy steel using tool inserts grade, work piece material, tool nose radius, rake
PcBN inserts. Dipti Kanta Das and Ashok Kumar Sahoo [7] angle, depth of cut, spindle speed, feed rate.
investigated on surface roughness during hard machining of 3. RESEARCH METHODOLOGY
EN 24 steel with the help of coated carbide insert. Harsh Y The research is basically a hypotheses testing research
Valera and Sanket N Bhavsar [8] done an experimental study making use of design of experiments based on Taguchi
of power consumption and roughness characteristics of method. Hypotheses have been constituted for testing the
surface generated in turning operation of EN-31 alloy steel main effect of the cutting parameters based on the literature
with TiN+Al203+TiCN coated tungsten carbide tool under review.
different cutting parameters. S.A.Khan and S.L.Soo [9] done
an experimental work on tool wear/life evaluation when 3.1 Machine and the Material
finish turning Inconel 718 using PCBN tooling. Dr.C.J. Rao The turning operation was conducted using LMW Smarturn
and Dr.D. Nageswara Rao [10] investigated the influence of Industrial type CNC lathe machine with a range of spindle
speed, feed and depth of cut on cutting force and surface speed from 50 rpm to 3500 rpm, and a 10 KW motor drive.
roughness while working with tool made of ceramic with an The cutting tool is PCBN insert, which is designated by
Al2O3+TiC matrix (KY1615) and the work material of AISI KB5610. The material used was EN 353 steel (hardness of
1050steel (hardness of 484 HV). V.Bushlya and J.Zhou [11] 64 HRC). These bars (32mm in diameter and 75mm in
studied the tool life, tool wear and surface integrity of length) were machined under wet condition. The work
superalloy Inconel 718 when machined with coated and material bars were turned, centred and cleaned by removing
uncoated PCBN tools, aiming on increased speed and a 1mm depth of cut from the outside surface, prior to the
efficiency. SU Honghua and LIU Peng [12] investigated the actual machining tests.
performance and wear mechanism of the tools (PCD and
PCBN) for machining the TA15 alloy. J.Guddat and R.M 3.2 Surface roughness measurement
Saoubi [13] investigating the effect of wiper PCBN inserts The instrument used to measure surface roughness was
on surface integrity and cutting forces by hard turning of Qualitest TR200. For a probe movement of mm, surface
through hardened AISI 52100. Lin et al. [14] adopted an roughness readings were recorded at three locations on the
abdicative network to construct a prediction model for work piece and average value is used for analysis.
surface roughness and cutting force. Feng and Wang [15]  Ra Range: 0.01 – 40 μm
investigated the influence on surface roughness in finish  Tracing Length Lt: (1 – 5 cut-off) + 2 cut-off
turning operation by developing an empirical model through  Detector: Diamond tip radius 5 μm
considering exogenous variables: work piece hardness, feed, 3.3 Cutting conditions and experimental procedure
cutting tool point angle, depth of cut, spindle speed, and Among the speed, feed rate, and depth of cut combinations
cutting time. Suresh et al. [16] focused on machining mild available on the lathe, three levels of cutting parameters
steel by Tic coated tungsten carbide cutting tools for were selected based on similar earlier studies (Table-1)
developing a surface roughness prediction model by using
response surface methodology. Lee and Chen [17] have used
ANN using sensing technique to monitor the effect of
vibration produced by the motions of the cutting tool and
work piece during the cutting process developed an on-line

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Table-1: Factors and their Levels

Level 3
Factor Level 1 Level 2

A: Speed (rpm) 1500 1750 2000

0.06
B: Feed (mm/rev) 0.20 0.04

C: Depth of Cut (mm) 0.25 0.50 0.75

Taguchi design L-9 for three levels and three factors yielded 9 experiments were carried out. The experimental data is
given in table-2.
Table-2: Experimental data
Weight Weight Material Surface
Sl Speed Feed Depth of Machining Before After Removal Roughness
Designation
no (rpm) (mm/rev) Cut (mm) Time (sec) Machining Machining Rate (microns)
(g) (g) (g/sec)
0.947
01 A1B1C1 1500 0.02 0.25 1.47 393 380 8.84

02 A1 B2 C2 1500 0.04 0.50 1.36 392 380 8.82 0.452

0.854
03 A1 B3 C3 1500 0.06 0.75 1.12 394 391 11.60

04 A2 B1 C2 1750 0.02 0.50 1.34 392 391 8.20 0.194

0.428
05 A2 B2 C3 1750 0.04 0.75 0.38 392 391 28.94

06 A2 B3 C1 1750 0.06 0.25 1.23 392 391 8.94 0.656

0.336
07 A3 B1 C3 2000 0.02 0.75 1.23 392 391 8.94

08 A3 B2 C1 2000 0.04 0.25 0.47 393 391 25.53 0.376

0.659
09 A3 B3 C2 2000 0.06 0.50 0.35 393 391 33.33

4. RESULT ANALYSIS
4.1 Surface Roughness analysis
The response table for Signal to Noise ratio results is very clear to support the optimum control factors A1, B2 and C3 (table 3).
This can be seen in the main effect plot for SN ratio (figure 1). The analysis of variance (ANOVA) result gives the percentage
contribution of process parameter for speed as 38.59% (table 4).

Table-3: Response Table for Signal to Noise Ratios

Smaller is better
Level Speed Feed DOC
1 2.914 8.063 4.210
2 8.426 7.588 8.254
3 7.197 7.588 6.072
Delta 5.512 5.178 4.044
Rank 1 2 3

Table-4: ANNOVA for Surface Roughness

Source DF Seq SS Adj SS Adj SS F P Percentage of contribution

SPEED 2 0.19302 0.19302 0.09651 2.41 0.293 38.59
FEED 2 0.15125 0.15125 0.07563 1.89 0.346 30.24
DOC 2 0.07584 0.07584 0.03792 0.95 0.514 15.16
Error 2 0.08007 0.08007 0.04003 - - 16.01
Total 8 0.50018 - - - - 100
S = 0.200083 R-Sq = 83.99% R-Sq(adj) = 35.97%

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Main Effects Plot for SN ratios

Data Means

SPEED FEED
9.0

7.5

6.0
Mean of SN ratios

4.5

3.0
1500 1750 2000 0.02 0.04 0.06
DOC
9.0

7.5

6.0

4.5

3.0
0.25 0.50 0.75

Signal-to-noise: Smaller is better

Figure – 1 Main Effects Plot for SN Ratios

4.2 MRR Analysis

The response table for Signal to Noise ratio results is very clear to support the optimum control factors A2, B1, and C3 (table 5).
This can be seen in the main effect for SN ratio (figure 2). The analysis of Variance (ANOVA) result gives the percentage
contribution of process parameter for Feed as 29.83% (table 6).

Table-5: Response Table for Signal to Noise Ratios

Larger is better
Level Speed Feed DOC
1 19.17 18.74 22.03
2 22.18 25.43 22.55
3 25.87 23.59 23.18
Delta 6.17 6.68
Rank 2 1 3

Table-6 ANOVA for MRR

Source DF Seq SS Adj SS Adj SS F P Percentage of Contribution

SPEED 2 248.9 248.9 124.4 0.75 0.571 29.59

FEED 2 251.0 251.0 125.5 0.76 0.569 29.83

DOC 2 9.8 9.8 4.9 0.03 0.971 1.17

Error 2 331.5 331.5 165.8 39.41

Total 8 841.2 100

S = 12.8753 R-Sq = 60.59% R-Sq(adj) = 0.00%

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Main Effects Plot for SN ratios

Data Means

Speed Feed
26

24

22
Mean of SN ratios

20

1500 1750 2000 0.02 0.04 0.06

DOC
26

24

22

20

0.25 0.50 0.75

Signal-to-noise: Larger is better

Figure – 2 Main effect plots for SN ratio

4.3 Machining Time Analysis
The response table for Signal to Noise ratio results is very clear to support the optimum control factors A 1, B2 and C3 (table 7). This
can be seen in the main effect plot for SN ratio (figure 3). The analysis of variance (ANOVA) result gives the percentage
contribution of process parameter for speed as 35.98% (table 8).

Table-7: Response Table for Signal to Noise Ratios

Smaller is better
Level Speed Feed DOC
1 -2.3338 -2.5622 0.4712
2 1.3547 4.0972 1.3019
3 4.6262 2.1121 1.8740
Delta 6.9600 6.6594 1.4028
Rank 1 2 3

Table-8 ANOVA for Machining Time

Source DF Seq SS Adj SS Adj SS F P Percentage of Contribution

SPEED 2 0.6022 0.6000 0.3011 1.37 0.422 35.98
FEED 2 0.5983 0.5983 0.2991 1.36 0.433 35.75
DOC 2 0.0345 0.0345 0.0172 0.08 0.927 2.06
Error 2 0.4388 0.4388 0.2194 26.21
Total 8 1.6738 100
S = 0.468413 R-Sq = 73.78% R-Sq(adj) = 0.00%

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Main Effects Plot for SN ratios

Data Means
SPEED FEED
4

0
Mean of SN ratios

-2

1500 1750 2000 0.02 0.04 0.06

DOC
4

-2

0.25 0.50 0.75

Signal-to-noise: Smaller is better

Figure-3 Main effect plots for SN ratios

5. CONCLUSION
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