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

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

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 8.902. 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.

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 .

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.

MATERIAL COMPOSITION OF UDIMET Cr 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

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

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

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.

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.

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

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

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.

Methods Taguchi method Anova table ANN method

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.

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.

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).

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

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

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

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.

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).

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

Tool Wear

Surface Roughness

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.

REFERENCES Antony J., (2000), “Multi-response optimization in industrial experiments using Taguchi’s quality loss function and Principal Component Analysis”, Quality and Reliability Engineering International, Volume 16, pp.3-8. Ahmed S. G., (2006), “Development of a Prediction Model for Surface Roughness in Finish Turning of Aluminium”, Sudan Engineering Society Journal, Volume 52, Number 45, pp. 1-5. 3. Abburi N. R. and Dixit U. S., (2006), “A knowledge- based system for the prediction of surface roughness in turning process” Robotics and Computer-Integrated Manufacturing, Volume 22, pp. 363–372. Al-Ahmari A. M. A., (2007),”Predictive machinability models for a selected hard material in turning operations”, Journal of Materials Processing Technology, Volume 190, pp. 305–311.

5. Choudhury S. K. and Bartarya G 5. Choudhury S. K. and Bartarya G., (2003), “Role of temperatureand surface finish in predicting tool wear using neural network and design of experiments”, International Journal of Machine Tools and Manufacture, Volume 43, pp. 747– 753. 6. Chien W.-T. and Tsai C.-S., (2003), “The investigation on the prediction of tool wear and the determination of optimum cutting conditions in machining 17-4PH stainless steel”, Journal of Materials Processing Technology, Volume 140, pp. 340–345. 7. Biswas C. K., Chawla B. S., Das N. S., Srinivas E. R. K. N. K., (2008), “Tool Wear Prediction using Neuro-Fuzzy System”, Institution of Engineers (India) Journal (PR), Volume 89, pp. 42-46. Doniavi A., Eskanderzade M. and Tahmsebian M., (2007), “Empirical Modeling of Surface Roughness in Turning Process of 1060 steel using Factorial Design Methodology”, Journal of Applied Sciences, Volume 7, Number17, pp. 2509- 2513.

Datta S. , Bandyopadhyay, A. and Pal, P. K Datta S., Bandyopadhyay, A. and Pal, P. K., (2008), “Application of Taguchi Philosophy for Parametric Optimization of Bead Geometry and HAZ Width in Submerged Arc Welding Using Mixture of Fresh Flux and Fused Slag”, for InternationalJournal of Advanced Manufacturing Technology, Volume 36, pp. 689-698. Feng C. X. (Jack) and Wang X., (2002), “Development of Empirical Models for Surface Roughness Prediction in Finish Turning”, International Journal of Advanced Manufacturing Technology, Volume 20, pp. 348–356. Fnides B., Aouici H., Yallese M. A., (2008), “Cutting forces and surface roughness in hard turning of hot work steel X38CrMoV5-1 using mixed ceramic”, Mechanika, Volume 2, Number 70, pp. 73-78.

Thanking you