1. OPTIMIZATION OF MACHINING
PARAMETER
IN NICKEL BASE ALLOY BY
S.THAMIZH SELVAN
External Guide
Mr.V.Anbarasan. M.E., (Ph.D)
Assistant Professor
Rajiv Gandhi College of Engineering
Internal Guide
Mr.M.ELANGO. M.E.,
Sr.Lecturer
Rajalakshmi Engineering College

2. INTRODUCTION
LITERATURE SURVEY
OBJECTIVE OF THE PROJECT WORK
PROCESS PARAMETERS AND CUTING TOOL
ANN
EXPERIMENTATION
RESULT AND DISCUSSION
CONCLUSION
REFERENCES

3. INTRODUCTION
Nickel based super alloy are finding wide application in the hot portions of jet turbines
such as blades, vanes, combustion chamber, etc.,
These materials posses high temperature, they place the cutting tools under tremendous
heat, pressure and abrasion leading to rapid flank wear, crate wear and tool notching at
the tool nose etc., and make them highly difficult to machine.
High toughness, ductility, High oxidation resistance and Good corosion resistance
The melting point of nickel is 1453°C, boiling point is 2732°C, specific gravity is
Nickel is a silvery white metal that takes a high polish. Nickel is hard, ductile, malleable,
and somewhat ferromagnetic. It is a fair conductor of heat and electricity.

4. LITERATURE SURVEY
The integrity of the machined surfaces and tool life are the most important considerations
during machining are discussed in the machinability of nickel based alloy paper. Its
published on the year of 1999 by the E.O. Ezugwu, Z.M. Wang and A.R. Machado.
Heat treatment of UDMET 720Li: the effect of microstructure on properties by M.P.
Jackson and R.C. Reed in the year 1998.
The High Temperature low cycle fatigue behavior of UDMIT 720 has been investigated
at 700º C in vacuum and air environments under strain control tests. Its published in the
year of 2002 by M. Marchionni, G.A. Osinkolu and G. Onofrio.
The fatigue life of the alloy is practically independent of HT in both environments at
high strain ranges. The Strain induced γ precipitation in nickel base super alloy UDMIT
720 using a stress relaxation based technique by H. Monajati, F. Zarandi and M. Jahazi .

5. OBJECTIVE OF THE PROJECT WORK
Study the surface topography of the UDIMET alloy.
Study the surface roughness by varying various process parameters like speed,
feed and depth of cut.
Optimize the machining process to improve the surface roughness using ANN
method.

6. MATERIAL COMPOSITION OF UDIMETCr Co Mo W Ti Al B Zr Ni
0.025 18
14.75 3
1.25 5
2.5
0.030
0.035
bal
MECHANICAL PROPERTIES OF UDIMET
Tensile strength
στ (MPa)
Yield strength
Σy (MPa)
Elongation
Є(%)
Reduction of area φ (%)
Vickers Hardness HV
1385
1025 10
11.9
466

7. CUTTING TOOL MATERIALS
Carbon steels
High speed steels (HSS)
Cast Cobalt alloys
Carbides
Cermets
Ceramics
Silicon Nitride
Cubic Boron Nitride (CBN)
Diamond

8. PROCESS PARAMETERS CUTTING SPEED FEED DEPTH OF CUT TOOL WEAR
SURFACE ROUGHNESS

9. SPEED
The speed v is defined as the magnitude of the velocity v, that is the derivative of the
position r with respect to time
Speed always refers to the spindle and the work piece.
When it is stated in revolutions per minute (rpm) it tells their rotating speed.
v = (πDN)/1000 m.min־¹
Here, v is the cutting speed in turning, D is the initial diameter of the work piece in mm, and N is the spindle speed in RPM.

10. FEED Feed always refers to the cutting tool.
It is the rate at which the tool advances along its cutting path.
The feed rate is directly related to the spindle speed and is expressed in mm
(of tool advance) per revolution (of the spindle), or mm/rev.
F = f.N m.min־¹
Here, F is the feed in mm per minute, f is the feed in mm/rev and
N is the spindle speed in RPM.

11. DEPTH OF CUT
It is the thickness of the layer being removed (in a single pass) from the work
piece or the distance from the uncut surface of the work to the cut surface,
expressed in mm.
It is important to note, though, that the diameter of the work piece is reduced
by two times the depth of cut because this layer is being removed from both
sides of the work
dc = (D-d)/2 mm
Here, D and d represent initial and final diameter (in mm) of the job

12. TOOL WEAR
Tool wear in machining is defined as the amount of volume loss of tool material on the contact surface due to the interactions between the tool and work piece.
Flank Wear
Notch Wear
Crater Wear
Chipping
Ultimate failure

13. SURFACE ROUGHNESS
Surface roughness is a measure of the texture of a  surface.
It is quantified by the vertical deviations of a real surface from its ideal form
Direct measurement methods
Comparison based techniques
Non contact methods
On process measurement.

15. Methods
Taguchi method
Anova table
ANN method

16. ARTIFICIAL NEURAL NETWORK(ANN)
An artificial neural network (ANN), usually called neural network (NN), is a 
Mathematical model or computational model that is inspired by the structure and/or
functional aspects of biological neural network.
A neural network consists of an interconnected group of artificial neurons, and
it processes information using a connectionist approach to computation.

17. EXPERIMENTATION
Checking and preparing the Centre Lathe ready for performing the machining
operation.
Cutting UDIMET bars by power saw and performing initial turning operation in Lathe
to get desired dimension of the work pieces.
Performing straight turning operation on specimens in various cutting environments
involving various combinations of process control parameters like: spindle speed, feed
and depth of cut.
Using ANN method to analyze the result.
Measuring surface roughness and surface profile with the help of a portable stylus-type
profilometer, Talysurf (Taylor Hobson, Surtronic 3+, UK)
Measuring cutting tool flank wear in tool makers microscope.

18. ANN Analyze
The input parameters of the neural network are the cutting conditions, namely cutting speed, feed rate, cutting time and the coolant delivery pressure. The output parameters are seven of the most important process parameters, namely component forces (tangential or cutting force, Fz and axial or feed force, Fx); spindle motor power consumption, machined surface roughness, and tool wear (average and maximum flank wear as well as nose wear).

19. Process of ANN Collection of Data Analysis and pre processing of data
Design of the network
Training and Testing of the network
Simulation and results

20. Results
Cutting speed (m/min) at constant feed rate of 0.25(mm/rev), coolant pressure of 110 bar
and cutting time of 312s.

21. Feed rate (mm/rev) at constant cutting speed of 30m/min, coolant pressure of 110 bar
and cutting time of 312s.

22. Coolant Pressure (bars) at constant feed rate of 0
Coolant Pressure (bars) at constant feed rate of 0.25(mm/rev), cutting speed 30(m/min)
and cutting time of 774s.

23. Cutting Time (sec) at constant feed rate of 0
Cutting Time (sec) at constant feed rate of 0.25(mm/rev), coolant pressure of 110 bar
and cutting speed of 30(m/min).

24. Cutting speed (100m/min), feed rate of 0.2(mm/rev), and depth of cut 0.4mm

25. Tool Wear

26. Surface Roughness

27. CONCLUSION
The optimum cutting speed at which minimum process parameters were obtained is in the
range of 25 – 35 m/min, while the optimum feed rate, corresponding to the minimum
surface roughness and nose wear, is within 0.27 and 0.28 mm/rev.
A consistent reduction in cutting force was achieved with increase in coolant pressure
due to reduction in tool-chip contact length as a result of the hydraulic wedge created by
the coolant jet at the tool-chip interface. The effect of coolant pressure on tool performance
is more pronounced on the maximum flank wear than other wear modes.
Flank wear are noticed as acceptable results at lower cutting depth with high cutting speed
and moderate fed rate.
Highest flank wear results recorded with increasing of cutting depth and cutting depth.
The best tool life obtained with PVD cutting tools at low and moderate cutting depth.

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31. Thanking you