OPTIMIZATION OF REAMMING PROCESS USING PARTICLE SWARM OPTIMIZATION

OPTIMIZATION OF REAMING PARAMETERS TO IMPROVE SURFACE FINISH OF EN1A PRESS TOOL

11ME001ARIVAZHAGAN .SGuided byDr. P. ASHOK VARDHANAN., ME., Ph.D

INTRODUCTIONSurface roughness is one of the most important parameters to determine the quality of a product.Surface roughness consists of the fine irregularities of the surface texture, including feed marks generated by the machining process.Several factors will influence the final surface roughness in a CNC reaming operation controllable factors such as (spindle speed, feed rate and depth of cut) and uncontrollable factors (tool geometry and material properties of both tool and work piece).MACHINING PARAMETERSDepth of CutCutting SpeedFeed RateNumber of flutesHelix angleNumber of passesRake angel

PROPERTIESWork piece properties:HardnessthicknessDiameterCutting Tool properties:Tool MaterialTool ShapeRun out ErrorsOUTPUT PARAMETERS1.Surface roughness (Other output parameters)2. Material removal rate3. Tool life4. Productivity5. Quality6.Machining time7. Machining costOPTIMIZATION TECHINIQUESExperimental methodSA (Simulated annealing)RSM (Response Surface Methodology)PSO (Particle Swarm optimization)GA (Genetic Algorithm)TS (Tabu Search)ACO (Ant Colony Optimization)EXPERIMENTAL DESIGNExperimental method is very old technique used for optimization of output results.In this method required number of experiments are done to know the optimum results. It is like a trial and error method.It doesnt give the best optimum results compare to other modern optimization techniques like RSM, PSO, GA, etc.,

RSMResponse surface methodology (RSM) is a collection of mathematical and statistical techniques for empirical model building. By careful design of experiments, the objective is to optimize a response (output variable) which is influenced by several independent variables (input variables). An experiment is a series of tests, called runs, in which changes are made in the input variables in order to identify then reasons for changes in the output response.REGRESSION MODELINGThe measurement of surface roughness in reaming in relation to the independent variables commonly investigated is expressed mathematically as followsRa= c v k f l d m

Output Result in the form of first order model or second order model = y - = b0x0+ b1x1+ b2x2+ b3x3, x0=1 is a dummy variable. x1, x2 and x3 are the cutting condition values.9PARTICLE SWARM OPTIMIZATIONEach particle keeps track of its coordinates in the solution space which are associated with the best solution (fitness) that has achieved so far by that particle. This value is called personal best , pbest.Another best value that is tracked by the PSO is the best value obtained so far by any particle in the neighborhood of that particle. This value is called gbest.The basic concept of PSO lies in accelerating each particle toward its pbest and the gbest locations.

LITERATURE SURVEY1. APPLICATION OF GA TO OPTIMIZE CUTTING CONDITIONS FOR MINIMIZING SURFACE ROUGHNESS IN END MILLING MACHINING PROCESS

Work Piece : Annealed alpha-beta titanium alloyTool : Uncoated , TIAlN , SNTRMachining parameters : Cutting Speed, Feed rate, Rake angleOutput result: Surface Roughness

REGRESSION MODELR uncoated = 0. 451- 0.00267x1+5.671x2+0.0046x3R TIAIN =0.292-0.000855x1+5.383x2-0.00553x3R SNTR = 0.237-0.00175x1+8.693x2-0.00159x3x1= cutting speed, x2= feed rate, x3= Rake angleIn this Journal they done 24 experimental trials Develop regression model for 3 different tools from the experimental data.

From the 3 regression model the best regression model is determined

Then using GA predicted surface roughness value, it is expected to be lower than the minimum Ra vale of the experimental and regression model.The GA results are compared with the RSM of (Mohruni ,2008)

They conclude that GA technique estimates the lower value of the best surface roughness value compared to the RSM technique. OPTIMIZATION OF CUTTING CONDITIONS IN END MILLING PROCESS WITH THE APPROACH OF PARTICLE SWARM OPTIMIZATION

Work Piece : Annealed alpha-beta titanium alloyTool : Uncoated , TIAlN , SNTRMachining parameters : Cutting Speed, Feed rate, Rake angle, Depth of cutOutput result : Surface Roughness

REGRESSION MODELR uncoated = 0. 451- 0.00267x1+5.671x2+0.0046x3+0.469x4R TIAIN =0.292-0.000855x1+5.383x2-0.00553x3+0.469x4R SNTR = 0.237-0.00175x1+8.693x2-0.00159x3+0.469x4

x1= cutting speed, x2= feed rate, x3= Rake angle, x4= depth of cut

In this journalThey used regression equation to establish a relationship between four selected input variables, such as speed, feed, depth of cut and rake angle and the output variable, surface roughness.PSO is used to optimize the predicted models given above. PSO is one of the soft computing which is extensively used in research work.The program code is write in MATLAB 10 version. The computational time is less than one minute.It is really very easy to apply and time saving technique.The results obtained by PSO are superior to the results obtained by GA used by Zain et al., and it indicates that PSO is more suitable for this category.3. EFFECTS OF MACHINING PARAMETERS WHEN REAMING ALUMINIUM-SILICON (SAE 322) ALLOYThis paper evaluates the dimensional stability (diameter, roundness and cylindricity) and surface roughness of reamed cylindrical holes using K10 cemented carbide welded blade reamers on aluminium-silicon cast alloy at various cutting conditions in order to optimize the cutting process.

Eleven experiments are conducted by 11 different reamers.

The 11 reamers are vary by diameter, Margin, Rake face finishing, Helix angle, Number of blades.RESULTSReaming at lower depths of cut gave the results in overall terms of accuracy, surface finish, roundness and cylindricity of the holes produced.

The best dimensional stability, surface finish as well as the lowest power consumption can be achieved when reaming the aluminium-silicon alloy at a lower cutting speed.

Reamers with more blades produced better hole diameter accuracy, surface finish and roundness at the expense of holes with a poorer cylindiricity and power consumption.

MODELLING AND OPTIMIZATION OF PROCESS PARAMETERS DURING END MILLING OF HARDENED STEELIn this study, the average surface roughness values obtained when milling EN24 grade steel with a harness of 260 BHN using solid coated carbide tools.Cutting parameters such as cutting speed (v), feed rate (f) and depth of cut(d) are optimized by using RSM technique.Sufficient numbers of experiments were run and second order quadratic model is designed.RSM has been proven to be an efficient method to predict the surface finish during end-milling of EN24 alloy steel. It is also reduces the number of experiments.Increment of cutting speed and decrement of feed will result in better surface quality.

METHODOLOGY

Study the real machining experimental set data to examine the cutting conditions used (Cutting speed, feed and depth of cut) that contribute to the surface roughness results. For this purpose, the machining experiment in conducting this work involves reaming EN1A leaded material using experiment set data for uncoated tool under flood conditions.A response surface model was designed and analyzed using JMP software the analysis of Variance ANOVA was carried out to determine the effect of cutting parameters on the surface roughness. The optimum cutting parameters to minimize surface roughness were obtained by maximizing the overall desirability function.

3.Develop the machining model to describe the relationship between independent machining variables (cutting conditions) and dependent machining variables (surface roughness) by using the regression technique. By the t-test, the best regression model is determined as the choice for the fitness function (objective function) in the PSO optimization technique.4.Find the optimal set value off independent variables to present the minimum objective function using the PSO technique. The objective function or fitness function of PSO leads to the minimum (lower) value of surface roughness. 5.Evaluate the PSO solution. The optimal cutting conditions that give minimum surface roughness values generated from PSO are compared to the experimental sample data, RSM model and regression model. From the compared result the optimum parameter to minimize surface roughness is taken.

OBJECTIVESThe objective of the present work is to optimize the cutting parameters to minimize the surface roughness of EN1A leaded material.A new set of research is carrying out by reaming EN1A leaded material with two different types of cutting tools and optimization of cutting parameter is done by PSO technique.The various cutting parameters which mainly affect surface roughness are to be optimized:Cutting speed (m/min)Feed rate (mm/tooth)Depth of cut (mm)Determine the surface roughness of the work piece by surface roughness testing instrument. Comparing the results of Experimental sample data, RSM, Regression model and PSO results

EXPERIMENTAL METHODMACHINE MODEL

In this research the cutting experiments are conducted on a Vertical Machining Center.AMS MODELMCV-450Capacity800 x 450 x 500Max Spindle Speed8000 RPM

With 4th axis Nikken CNC Rotary Table Model 180FA and Hydraulic power pack.

TOOLTwo different types of coated tools will be used in this experiment TiA1N and SNTR. Generally Coated tools improve the tool life, dimensional accuracy and surface roughness. Cutter diameter = 12.025 mmFluted length = 50 mmHelix angle = 00 (straight flute)Number of flutes = 5

MACHINING PARAMETERSLEVELCutting speed (m/min)Feed Rate (mm/rev)Depth of Cut (mm)1280.10.2152320.150.3153360.20.415CONCLUSION

In this phase, a brief literature survey had been done and maximum and minimum value of cutting parameters such as Cutting speed, Feed rate and depth of cut are noted by machining the EN1A leaded material. TiA1N coating is done on uncoated straight flute reamer with 5 number of blades.Using JMP software DOE is done and RSM, Regression model also created.Best regression model is selected by t-test and the best model is taken for PSO technique.FUTURE WORKSNTR coating will be done on uncoated tool.Using DOE number of experiments will select.By the help of JMP software optimum parameter will obtain by RSM technique.Regression model is also develop by using JMP software.Best model is selected and PSO will apply to obtain the best optimum parameters fro reaming.REFERENCEAzalan ohd Zain, Habibollah Haron, Safian Sharif, Application of Ga to optimize cutting conditions for minimizing surface roughness in end milling machining process, Expert Systems with Application 37 (2010) 4650-4659.

Vikas Pare, Geeta Agnihotri & C.M. Krishna, Optimization of Cutting Conditions in End Milling Process with the Approach of Particle Swarm Optimization , International Journal of Mechanical and Industrial Engineering (IJMIE), ISSN No. 2331-6477, Volume-1, Issue-2 2011.Anayt Ullah Patwari, A.K.M. Nurul Amin, Muammer D.Arif, Optimization of Surface Roughness in End Milling of Medium Carbon Steel by coupled Statistical Approach with Genetic Algorithm, The First International Conference on Interdisciplinary Research and Development, 31 May - 1 June 2011.U. Deepak, Optimization of Milling Operation Using Genetic and PSO Algorithm Bonfring International Journal of Software Engineering and Soft Computing, Vol. 1, Special Issue, December 2011K.Kadirgama, M.M.Noor, N.M.Zuki.N.M, M.M. Rahman, M.R.M. Rejab, R. Daud, K. A. Abou-El-Hossein, Optimization of Surface Roughness in End Milling on Mould Aluminium Alloys (AA6061-T6) Using Response Surface Method and Radian Basis Function Network, Volume 2, Number 4, December. 2008 ISSN 1995-6665 Pages 209- 214.

Turnad L. Ginta , A.K.M. Nurul Amin, H.C.D. Mohd Radzi, Mohd Amri Lajis, Development of surface Roughness Models in End Milling Titanium Alloy Ti-6Al-4V Using uncoated Tungsten Carbide Inserts, European Journal of Scientific Research, ISSN 1450-216 X vol.28 No.4 92009)VVK Lakshmi, K Venkata Subbaiah, Modelling and Optimization of Process Parameters during End Milling of Hardness Steel Internatinal Journal of Engineering Research and Applications (IJER), ISSN: 2248-679, Voume-2, Issue 2, March-Apr 2012.

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