370

5th International & 26th All India Manufacturing Technology, Design and Research Conference (AIMTDR 2014) December 12th–14th, 2014, IIT

Guwahati, Assam, India

370-1

An Experimental Investigation of Hot Machining Performance

Parameters using Oxy-Acetylene gas setup

Venkatesh Ganta1*, D Chakradhar2

1*Dept. of Mechanical Engg., NITK Surathkal, 575025, [email protected]

2Dept. of Mechanical Engg., NITK Surathkal, 575025, [email protected]

Abstract

This paper focuses on optimizing the cutting conditions for the average surface roughness (Ra) and metal removal

rate (MRR) obtained in hot machining of 15-5PH martensitic stainless steel with 40 HRC. Hot machining

experiments were performed on lathe machine using K313 carbide tool insert. Experiments were conducted based

on Taguchi L18 orthogonal array. The statistical method of signal-to-noise (S/N) ratio and the analysis of variance

(ANOVA) were employed to investigate the optimum process parameters like speed feed, depth of cut and

workpiece temperature and their effect on the performance characteristics i.e., surface roughness and metal removal

rate. The results of the study indicate that feed rate has the most significant effect on surface roughness. Cutting

speed and feed rate has the most significant effect on material removal rate.

Keywords : Hot machining, Surface roughness, MRR, Taguchi.

1 Introduction

Precipitation hardenable martensitic stainless steel

15-5PH has incredible advantages in aerospace

industries (particularly in actuator parts for modern

aircrafts), nuclear industries, chemical, petrochemical,

gears, pumps, food processing, paper and general

metalworking industries (Alexandre et al., 2013). These

materials provide an outstanding mechanical properties,

high strength and hardness, good corrosive resistance,

weldability, low distortion. Precipitate hardened

stainless steels exhibit high strengths at temperatures up

to 315oC like other martensitic stainless steels (Ashok

Kumar et al., 2013). The hardness of 15-5PH stainless

steel is 40 HRC and it is also one of the difficult to cut

materials.

The main aim of machining is to produce the parts

most economically. An unsuitable decision causes

expensive production cost and decreases the machining

quality (Ozler and Tosun 2002). The materials having

mechanical properties like high strength and hardness,

good corrosive resistance, weldability, low distortion,

which are frequently used in aerospace and nuclear

industries, are generally difficult to cut materials.

Conventional machining of these materials have

problems like low speeds, feeds, poor surface finish,

high tool wear, less tool life low production, in another

aspect unconventional machining is often employed for

machining of these materials. But, the unconventional

machining of these materials involves a high capital cost

and offers a low material removal rate (MRR). To

overcome these problems hot machining is one of the

most potential techniques developed to machine

difficult to cut materials.

In hot machining a part or whole workpiece is

heated before or during machining (Lei et al., 2001).

Heating of the material makes high hardness of the

material become soft, resulting in improved

machinability, high production rate, low power

consumption from all these advantages hot machining is

extremely used full to machine hard to cut materials like

ceramics (Ozler et al., 2001). Many researchers have

used different heating techniques like laser heating,

plasma heating, induction heating, electrical heating and

they were proved that these heating techniques are

expensive. Several researchers reported that there is an

improvement in both surface finish and tool life in hot

machining (Akasawa et al., 1987, Ueharaet al 1986.,

Hinds et al 1980, Raghuram et al., 1979, Chen et al.,

1973, Pal et al., 1969). In hot machining it was

observed that the cutting mechanism of the ceramics

changes from brittle fracture type to plastic deformation

type (Ueharaet al., 1986). Hinds et al. (1980) suggested

that the shape of the heat source and positioning of the

torch also affects overall efficiency. Materials of

different hardness’s were machined using different

grades of carbide tools, over a range of cutting speeds

and heating current. Chen et al.(1973) and Uehara et al.

An Experimental Investigation of Hot Machining Performance Parameters using Oxy-Acetylene gas setup

370-2

(1983) improved the cutting performance by the using

coated carbide tools in electric hot machining,

suggesting new possibilities in the field of the

machining of low machinability metals. Raghuram et

al. (1979) tool life is observed to increase if a magnetic

field is applied during machining. Thandra et al. (2010)

they conducted experiments in both conventional and

hot machining and they observed that hot machining

was effective in bringing down the cutting forces,

surface roughness and flank wear by about 34%. Tosun

et al. (2004) heated high manganese steel with liquid

petroleum gas flame and showed that cutting speed and

feed rate were the dominant variables on multiple

cutting performance characteristics like tool life and

surface roughness. The cooling method of the cutting

tool is very effective for reducing the tool wear in the

hot machining process (Akasawa et al., 1987). In

plasma hot machining cutting forces is decreasing by

machining high hardness materials (Kitagawa et al.,

1990). Hot machining is mainly used in turning. But

some researchers used it for shaping and milling also

(Pal et al., 1971). Based on the literature review,

research work carried on hot machining on difficult to

cut materials is to improve the machinability such as

cutting forces, surface roughness and tool wear. After

an extensive research and survey of existing heating

techniques (Kitagawa et al., 1990; Uehara et al.,1983) it

has been concluded that the oxy-acetylene heating set-

up will be in expensive compared to others techniques.

2 Experimental setup

A 15-5PH martensitic stainless steel rod with 32

mm diameter as workpiece used in these experiments.

The chemical composition of 15-5PH stainless steel

with 40 HRC is shown in Table 1. K313 tungsten

carbide insert is specified as SNMG 120408 is used as a

cutting tool. The process parameter ranges were

decided on the basis of machine capability and pilot

experiments. The selected ranges of process parameters

are shown in Table 2. In this paper L18 orthogonal array

is employed to analyze experimental results of

machining obtained from 18 experiments for hot

machining individually varying four parameters speed,

feed rate, depth of cut, and workpiece temperature.

For conducting experiments, an oxy acetylene

heating setup was used to heat the work piece material.

Oxy acetylene heating is one of the best choices for hot

machining it requires low cost equipment the heat

transfer to the workpiece is very low, although the gross

heat available and the energy transfer density will be

low and metallurgical damage of the workpiece is low

(Larine and Martynow., 1966). The flame was

generated through the nozzle of torch. A special

attachment was used to move the torch mounted on

carriage to provide a flexible movement of heat source

while machining. During all the experiments, the

distance between the torch and workpiece is 25 mm.

Flow rates for acetylene and oxygen were adjusted by

pressure regulator and kept constant to get a neutral

flame, which was used throughout the machining.

Fig.1. Experimental setup for hot machining operation



370-3

Table 1 Chemical composition of 15-5PH stainless steel

Element C Mn P S Si Cr Ni Cu Ta

% 0.07 1.00 0.040 0.030 1.00 14-15.50 3.5-5.5 2.5-4.5 0.15-0.45

Table 2 Parameters and their levels of cutting experiments

Symbol Cutting parameters Levels Units I II III

A Cutting speed 31 77 120 m/min B Feed rate 0.2 0.3 0.4 mm/rev C Depth of cut 0.4 0.6 0.8 mm D Workpiece Temp. 150 250 350

oC

3 Design of Experiments

Taguchi method is a powerful tool for the design

of high quality systems. It provides a simple,

efficient and systematic approach to optimize design

for performance, quality and cost. To study effect of

four process parameters like speed, feed, depth of cut,

workpiece temperature on two important

performance characteristics surface roughness, MRR.

Based on the degree of freedom of process

parameters, L18 orthogonal array is selected.

Accordingly, 18 experiments were carried out to

study the effect of machining process parameters on

performance characteristics. In all tests surface

roughness was measured by Mitutuyo surface tester

and metal removal rate is calculated by the ratio of

weight loss of the workpiece to the time. The

specimen was weighed before and after machining

using digital weighing machine. The time was

measured using a digital stopwatch. Parametric

design study involves control and noise factors.

Measure of interactions between these factors with

regard to robustness is signal-to-noise (S/N) ratio

3.1 S/N ratio to compute hot machining

characteristics

The surface roughness of the hot machined parts

has been considered as smaller the better, whereas the

metal removal rate, is considered as higher the better.

These considerations have been taken for good

quality characteristics of interest.

For the present study S/N ratio for metal removal rate

is calculated using the formula.

2

i

110 og (1 / y )

nlη = − ∑ (1)

S/N ratio for surface roughness of the machined parts

is calculated using the formula

2

i

110 og (y )l

nη = − ∑ (2)

4 Results and discussion

The quality of machined surface formed in hot

turning is evaluated in terms of average surface

roughness (Ra) values and metal removal rate MRR.

From the results it was determined that Ra values

obtained by the carbide cutting tool at all cutting

conditions. Minimum surface roughness value

obtained in machining of the work piece material was

determined as 0.49 µm from the experiments

performed at 120 m/min cutting speed, 0.2 mm/rev

feed rate, 0.4 depth of cut and 250oC temperature.

The maximum metal removal rate was obtained at

120 m/min cutting speed, 0.4 mm/rev feed rate,

0.6 depth of cut and 250oC temperature as shown in

Table 3.

S/N ratios of the Ra and MRR data obtained from

the experimental results, which will be used to

determine the optimal levels of each variable, were

calculated according to the Eq. (1&2) Table 3.

Figure 2 and Figure 3 illustrates the graphs of S/N

ratios that will be used to reach the optimal Ra value

and their values are determined as A3, B1, C2 and D3.

The optimum process parameters to maximize the

material removal rate are A3, B3, C3, D2. .


370-4

Table 3 Experimental results of hot machining

S.N0 Speed

(m/min)

Feed

(mm/rev)

doc

(mm)

Temp oC

Ra

(µm)

MRR

(gm/min)

S/N

ratio (Ra)

S/N

ratio MRR

1 31 0.2 0.2 150 1.45 10.21 -3.23 10.18

2 31 0.3 0.4 250 1.51 14.32 -3.58 11.08

3 31 0.4 0.6 350 1.72 18.42 -4.71 13.46

4 77 0.2 0.4 350 1.5 12.23 -3.52 10.94

5 77 0.3 0.6 150 1.24 23.41 -1.87 5.43

6 77 0.4 0.2 250 2.21 32.32 -6.89 16.76

7 120 0.2 0.2 350 0.62 17.63 4.15 12.37

8 120 0.3 0.4 150 1.68 29.14 -4.51 13.08

9 120 0.4 0.6 250 2.41 68.45 -7.64 17.66

10 31 0.2 0.6 150 1.4 14.56 -2.92 9.32

11 31 0.3 0.2 250 2.08 19.15 -6.36 16.07

12 31 0.4 0.4 350 2.04 22.65 -6.19 15.84

13 77 0.2 0.6 250 1.58 29.1 -3.97 11.98

14 77 0.3 0.2 350 2.14 32.42 -6.61 16.40

15 77 0.4 0.4 150 2.35 40.14 -7.42 17.41

16 120 0.2 0.4 250 0.49 15.83 6.20 15.84

17 120 0.3 0.6 350 1.47 41.37 -3.35 10.49

18 120 0.4 0.2 150 2.64 55.79 -8.43 18.52

Fig. 2. Main effect plot of S/N ratios of Ra Fig. 3. Main effect plot of S/N ratios of MRR

Table 4 ANOVA for S/N ratios of Ra

Source Degree of

freedom(DF)

Sum of

squares(SS)

Mean of

squares (MS) F ratio P value

%

Contribution

Speed 2 26.10 13.0 1.33 0.312 10.51

Feed 2 122.01 61. 6.22 0.020 49.12

Depth of cut 2 5.97 2.9 0.30 0.745 2.408

Workpiece

Temperature 2 6.00 3.0 0.31 0.744 2.421

Error 9 88.26 9.8

Total 17 248.36 100

1207731

0.0

-1.5

-3.0

-4.5

-6.0

0.40.30.2

0.60.40.2

0.0

-1.5

-3.0

-4.5

-6.0

350250150

Speed

Mean of SN ratios

Feed

doc Temp

Main Effects Plot for SN ratios

Data Means

Signal-to-noise: Smaller is better

1207731

32

30

28

26

24

0.40.30.2

0.60.40.2

32

30

28

26

24

350250150

Speed

Mean of SN ratios

Feed

doc Temp

Main Effects Plot for SN ratios

Data Means

Signal-to-noise: Larger is better



370-5

Table 5 ANOVA for S/N ratios of MRR

Source Degree of

freedom(DF)

Sum of

squares(SS)

Mean of

squares (MS)

F

ratio

P

value

%

Contribution

Speed 2 123.830 61.915 11.42 0.003 34.78

Feed 2 154.623 77.311 14.26 0.002 43.43

Depth of cut 2 23.969 11.985 2.21 0.166 6.732

WorkpieceTemperature 2 4.814 2.407 0.44 0.655 1.352

Error 9 48.788 5.421

Total 17 356.024 100

Table 6 Comparison between experimental value and predicted value

4.1 ANOVA Analysis

The purpose of ANOVA analysis is to find the effect

of design parameters on quality characteristics (Yang

and Tarng., 1998). The results of ANOVA which was

done to determine the significant level of variable on the

Ra obtained during the hot turning of 15-5PH stainless

steel is given in Table 4. The ANOVA table it is

analyzed that significance level of α=0.05, i.e. for the

confidence level of 95% it has been acceptable that P

value less than 0.05 is indicated that the performance of

the parameter statistically significant and more than

0.05 less significant. F ratios and their percentage

contribution was taken in to consideration to identify the

significance level of the variables. Table 4 indicates that

the most efficient variable on the Ra value is the feed

rate 49.12%. The other variables that have effect on Ra

are cutting speed 10.51%, workpiece temperature

2.42%, and depth of cut 2.40%.

The results of ANOVA which was done to determine

the significant level of variable on the MRR occurred.

during the hot turning of 15-5ph stainless steel is given

in Table 5. F ratios and their percentage contribution

was taken in to consideration to identify the significance

level of the variables. Table 5 indicates that the most

efficient variable on the MRR value is the feed rate

43.43%. The other variables that have effect on MRR

are cutting speed 34.78%, 6.73%, depth of cut,

workpiece temperature 1.35 %.

4.2 Confirmation Experiments

In Taguchi method optimization is verified by

confirmation experiments after the determination of

variable levels that will give the optimal results. The

confirmation experiment results performed at the

optimum variable levels (A3, B1, C2, D3) determined for

Ra and MRR the variable levels (A3, B3, C3, D2) were

evaluated by taking in to consideration. Table 6 gives

the comparison of the results of the confirmation

experiments which were conducted according to the

optimum levels. Predicted values are calculated

according to the Minitab 16 software. The difference

between experimental values and predicted values for

surface roughness is 0.037 while the metal removal rate

is 7.23 are shown in table 6

5. Conclusions

In this study the statistical method of signal-to-noise

ratio (S/N) and (ANOVA) were applied to evaluate the

effect of processes parameters on surface roughness

and metal removal rate and to find the optimal processes

parameters to minimize the surface roughness and to

maximize the metal removal rate during hot machining

Parameter Confirmatory

experimental results

Predicted values Differences

Surface roughness Raexp

0.982

S/Nexp

2.34

Racal

0.945

S/N cal

2.117

Raexp- Racal

0.037

S/Nexp - S/N cal

0.223

Metal removal rate MRRexp

64.45

S/N exp

34.98

MRRcal

57.22

S/N cal

35.66

MRRexp-MRRcal

7.23

S/Nexp - S/N cal

0.68


370-6

of 15-5PH stainless steel. The results obtained from this

study are given below:

• The smallest Ra values occurred during hot

machining of 15-5PH stainless steel are obtained

as 0.49 µm from the experiments performed at

120 m/min cutting speed, 0.2 mm/rev feed rate,

0.4 depth of cut and 250oC temperature.

• The highest MRR values occurred during hot

machining of 15-5PH stainless steel are obtained

as obtained at 120 m/min, 0.4 mm/rev feed rate,

0.6 depth of cut and 250oC temperature.

• The effects of the variables on surface roughness

and metal removal rate were determined by

ANOVA. The most significant parameters were

found that feed rate and cutting speed while the

workpiece temperature and depth of cut are the

least significant parameters.

References

Alexandre Mondelin, Valiorgue F, J. Rech, M. Coret,

E.Feulvarch. (2013), Modelling of surface dynamic

recrystallization during the finish turning of the 15-5PH

steel, 14th

CIRP conference on modelling of machining

operations (CIRP CMMO) Vol.8, pp. 311-315.

Aravindan S, Naveen Sait A, NoorulHaq A, (2008), A

machinability study of GFRP pipes using statistical

techniques, International Journal of Advanced

Manufacturing Technology, Vol. 37, pp. 1069–1081.

Ashok kumar, Balaji Y, Eswara Prasad N, Gouda, K

Tamilmani, (2013), Indegenous development and

airworthiness certification of 15-5PH precipitation

hardenable stainless steel for aircraft applications,

Indian Academy of Sciences, pp. 3-23.

Pal D. K., Basu S. K. (1971), Hot machining of

austenitic manganese steel by shaping, International

Journal of Machine Tool Design Research, Vol. 11,

pp. 45–61.

Madhavulu G, Basheer Ahmed, (1994), Hot Machining

Process for improved Metal Removal Rates in turning

operation, Journal of Material Process Technology,

Vol. 44, pp. 199-206.

Hinds B.K, and Almeida S.M.D, (1981),Plasma arc

heating for hot machining, International Journal of

Machine Tool Design Research, Vol. 21, pp.143–152.

Hsiao YF, Tarng YS, Huang WJ. (2010), Optimization

of plasma arc welding parameters by using the Taguchi

method with the grey relational analysis. Journal of

Material Manuf acturingProcess Vol. 23(1) pp. 51–58

Lopez de Lacalle, L.N., Sanchez, J.A., Lamikiz,

A.Celaya.(2004), Plasma assisted milling of heat-

resistant super alloys, Journal of Manufacturing Science

& Engineering, Vol. 126, pp.274–285.

.Maitya,K.P, Swain P., (2008), An experimental

investigation of hot-machining to predict tool life,

journal of materials processing technology Vol. 198,

pp. 344–349.

Uehara Kunio, Hideo Takeshita.(1986) .Cutting

Ceramics with a Technique of Hot Machining.Annal

CIRP, Vol. 32, pp. 55–58.

Ozler,L., Inan,A C. Ozel. (2001), Theoretical and

experimental determination of tool life in hot machining

of austenitic manganese steel, International Journal of

Machine Tools & Manufacture Vol. 41, pp.163-172

Ragunath N, Pandey PM.(2007), Improving accuracy

through shrinkage modeling by using Taguchi method

in selective laser sintering. International Journal of

Machine Tools Manufuring Vol. 47, pp. 985–995

Lei,Y.C. Shin, F.P. Incropera (2001), Experimental

investigation of thermo-mechanical characteristics in

laser assisted machining of silicon nitride ceramics,

Journal ofManufacturing Science & Engineering,

Vol. 123, pp.639–646.

Thandra S.K. and Choudhury S.K..(2010), Effect of

cutting parameters on cutting force, surface finish and

tool wear in hot machining, International Journal of.

Machining and Machinability of Materials, Vol. 7, pp.260 - 273 Kitagawa,T., .Maekawa K. (1990), Plasma Hot

Machining for new Engineering materials. Wear, Vol.

139, pp.251–26

Kitagawa T .and Maekawa K (1988), Plasma hot

machining for high hardness metals, Bulletin of Japan

Society of Precision Engineering, Vol. 22, pp.145–151.

Chen, N.N.S, Lo K.C., 1974. Factors affecting tool life

in hot machining of alloy steels, International Journal

of Machine Tool Design Research, Vol. 14, pp.161–173.

Tosun Nihat, Latif Ozler. (2002), A study of tool life in

hot machining using artificial neural networks and

regression analysis method, Journal of Materials

Processing Technology Vol. 124, pp. 99-104.

Tosun N. and Ozler L. (2004), Optimization for hot

turning operations with multiple performance

characteristic, International Journal of Advanced

Manufacturing Technology Vol. 23, pp. 777-782.

Patel Pulak KM,Pandy M, VenkateswaraRao

P.,(2010),Optimisation of process parameters for multi-

performance characteristics in EDMof Al2O3 ceramic

composite, International Journal of Advanced

Manufacturing Technology Vol. 47, pp. 1137–1147

Raghuram V, M.K. Muju.(1979), Improving tool life by

magnetization in hot machining, International Journal

of Machine Tool Design Research, Vol. 20, pp.87–96.

Yang W.H., Tarng, Y.S. (1998), Design optimization of

cutting parameters for turning operations based on the

Taguchi method, Journal of Material.

Process.Technology. Vol. 84, pp. 122–129

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