R.V.R &J.C.College of Engineering, Guntur, , Abstract

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1 5 th International & 26 th All India Manufacturing Technology, Design and Research Conference (AIMTDR 2014) December 12 th 14 th, 2014, IIT Guwahati, Assam, India Effect of Carburizing Flame and Oxidizing Flame on Surface Roughness in Turning of Al/SiC MMC and Teaching-Learning-Based Optimization of Process Parameters N. V. V. S.Sudheer 1*, K. KarteekaPavan 2 1* R.V.R &J.C.College of Engineering, Guntur, , nvvssudheer@yahoo.co.in 2 R.V.R &J.C.College of Engineering, Guntur, , karteeka@yahoo.com Abstract This paper presents an experimental probe into the effects of carburising and oxidising flame on the surface finish in turning of Aluminium metal matrix composite. The experimental design is performed by using 3 3 full factorial designs. From the measured values of surface finish, the effects of different parameters like speed, feed and depth of cut are estimated. Observation of results proved that carburising flame cutting operation yields better surface finish compared to dry & oxidising flame cutting. From the measured values the mathematical power models are developed. These models are subjected to Teaching-Learning-Based Optimization (TLBO) technique for finding global values of speed, feed, and depth of cut for minimizing surface roughness and maximizing material removal rate. Key words:- Carburising flame, Oxidising flame, Al-MMC, BUE. 1 Introduction In the recent past Aluminium Metal Matrix Composites (Al-MMC) became the most preferred elements by the manufacturers because of their improved mechanical strength, thermal properties and lower specific weight. Taya and Arsenault (1989) stated that the Al metal matrix composites pose many problems to the industry in machining since the particles present in MMC are harder than the HSS tools and carbide tools. Manna and Battacharya (2001), Loony et al (1992), Weinter and Konig (1993) and Quan and Zehua (2000) observed that the hard silicon particles, which intermittently come into contact with the cutting tool edge, which in due course becomes worn out by abrasion, resulting in the formation of very poor surface finish. Manna and Battacharya (2002) stated that when soft Al- MMC job slides over a hard cutting tool edge during turning, because of high temperature, friction and pressure the built-up edge (BUE) is formed which produces a very poor surface finish. Hence generation of good surface finishes for Al-MMC jobs during turning is a challenge to manufacturing Industry. El Baradie (1996) and Diniz and Micaroni (2002) observed that the cutting fluids perform as coolant and lubricant, the coolant effect reduces temperature in cutting zone and lubricant action decreases cutting forces. Thus friction coefficient between tool and chip becomes lower in comparison to dry machining. Unfortunately, conventional cutting fluids cause environmental and health problems. The current attention to the environmental regulations has been forcing manufacturers to reduce or eliminate the amount of pollutants. Therefore in theory, it seems to be better options to eliminate cutting fluid usage. Tash et al (2006) observed that the machinability of Al-MMC can be improved by different treatments. Heat treatments, which increase hardness, will reduce the built-up edge (BUE) tendency during machining. Roy et al (2009) stated that, in the case of dry machining, the major problems encountered are the BUE at low cutting speeds and sticking at high cutting speeds, hence the need for special methods. Sun et al. (2010) observed that the heat energy reduces the yield strength and hardness and makes brittle material have ductile materials. Attia et al. (2010) compared Laser assisted machining with conventional machining, he observed that surface finish of Laser assisted turning is improved by more than 25% and the material removal rate is increase by approximately 800%. Riaz Muhammad et al. (2012) used a new hybrid machining technique hot ultrasonically assisted turning was used for the machining of β Ti alloy to investigate the benefits of the machining process. This technique offers better results in terms of cutting forces and surface roughness when compared to conventional turning. Yongho Jeon et al. (2013) stated that the external energy assistance enables the machining of hard to cut materials and improve the quality of machining. However, they are still at the beginning stage of research and require the extensive studies for the basic understandings in the mechanisms and optimal processes and the systems. Recently Rao and Kalyankar (2012) and Rao and Patel (2012)have introduced the Teaching-Learning- Based Optimization (TLBO) algorithm which does not require any algorithm specific parameters. TLBO is developed based on the natural phenomena of teaching and learning process of a class room

2 Effect of Carburizing Flame and Oxidizing Flame on Surface Roughness in Turning of Al/SiC MMC and Teaching-Learning-Based Optimization of Process Parameters According of Rao & Savsani (2012) TLBO contains two phases as teacher phase and learning phase. As in any population based algorithms the TLBO is also contains population. Solution vectors are the learners and dimensions of each vector is termed as subjects. Best learner in the population is a teacher. In this study, the effect of Carburising Flame (combined progressive spin-hardening) and Oxidizing Flame on surface roughness in turning of Al-MMC has been investigated and the results were compared with dry machining. The experiments were conducted by varying cutting speed, feed and depth of cut in three levels each and test results were analyzed. In three conditions the mathematical models (power type) were developed by using multiple regression. These mathematical models are subjected to Teaching-Learning-Based Optimization (TLBO) technique for finding the optimal values of speed, feed and depth of cut for minimizing surface roughness and maximizing material removal rate (MRR). 2 Work piece material The Al- MMC of 75mm diameter is used for experimentation. The chemical composition of the material is shown in the Table 1. Table 2 shows the mechanical properties of the work material. Table 3 shows the details of cutting tool and tooling system used for experimentation. Table 1: Chemical composition of the material Type of MMC Discon tinuous MMC Size of reinforc ed Particle s 25µm %Si %M g %Cu %Fe 0.7 %Ti %Cr %Zn %Mn %SiC %Al remaining 3 Experimentation Al/SiC-MMC of 75mm diameter is used for experimentation. The cutting experiments have been carried out on TMX-2030 engine lathe which has a maximum spindle speed of 1200 rpm and considering cutting speed (v), feed (f) and depth of cut (d) as parameters. These parameters are changed in three levels each as shown in Table 4 and twenty seven experiments were conducted separately in the dry condition, the Carburizing flame heating and oxidizing flame heating in two replicates and a total of 162 (3x3x3x2x3) experiments were conducted. Machining at each experimental condition has been carried out for approximately for a cutting length of 10-15mm. The Al-MMC bar stocks of 300mm length and 75mm diameter have been used, and on each bar stock 10 to 12 tests have been performed. The machined surface was measured by using surftest (Mitutoyo make) at three different positions on circumference at an angle of and average value was taken for analysis. Table 4 Levels for process parameters Levels v(m/min) f(mm/rev) d(mm) Accomplishment of Carburising Flame Heating and Oxidising Flame Heating While turning process is going on, the Carburizing flame (acetylene rich flame) is supplied to the work-piece by maintaining a gap of 4cm between work-piece and torch tip and a gap of 5cm arc length between flame and workpiece and tool interface. After the flame heating the work-piece is drenched by cooling water before it is cut. This arrangement was shown in Figure1.The same procedure is adopted for oxidising flame (oxygen rich flame) heating process. Table 2 Physical and Mechanical properties of Al MMC Material Hardness BHN Density gm/cm 3 Al6061/SiC Table 3 Details of cutting tool and tooling system used for experimentation Tool holder ISO code Tool insert ISO code PSDNN 2525 M12 SNMG TN2000 Figure1 Flame Heating 556-2

3 5 th International & 26 th All India Manufacturing Technology, Design and Research Conference (AIMTDR 2014) December 12 th 14 th, 2014, IIT Guwahati, Assam, India 4 Mathematical model for surface roughness The power model used to find the effect of process parameters on surface roughness. The general form of power model for three parameters was used in this experimentation is Surface Roughness = k. v f d (1) Where v = velocity in m/minute a f = feed mm/rev. d= depth of cut in mm In this work power model was developed for dry condition, the Carburizing flame heating and oxidizing flame heating individually. 4.1 Mathematical models from results and Analysis The experimental results obtained from the carburizing flame turning are used for developing the power model. The model is shown in the equation (2) S. R. = v f d b c 0.06 In the above equation the power of feed (f), is higher compared to v and d. Hence f is the dominating factor. The experimental results obtained from the oxidizing flame turning are used for developing the power model. The model is shown in the equation (3) S. R. = v f d In the above equation the power of feed (f), is higher compared to v and d. Hence f is the dominating factor. The experimental results obtained from the dry cutting are used for developing the power model The model is shown in the equation (4) S. R. = v f d 0.06 (4) In the above equation the power of feed (f) is higher compared to v and d. Hence f is the dominating factor. 5 Teaching Learning Based Optimization (TLBO) Procedure : According to Rao & Savsani (2012) TLBO is a recent evolutionary algorithm which providing competitive solutions for various applications and does not require any program specific parameters compared to other existing evolutionary algorithms. The process of TLBO is as follows Initialization: The population X, is randomly initialized by a given data set of n rows and d columns using the following equation. min max min, j( 0) = X j + rand(1) *( X j X j ) (5) X i,j Creation of a population of learners or individuals. The i th learner of the population X at current generation t with d subjects is as follows, X i ( t) = [ X i, 1( t), X i,2 ( t),..., X i, d ( t) ] (6) Teacher phase The mean value of each subject, j, of the population in generation t is given as M t) = [ M ( t), M ( t),..., M ( ) ( 1 2 d t (7) The teacher is the best learner with minimum objective function value in the current population. The Teacher phase tries to increase the mean result of the learners and always tries to shift the learners towards the teacher. A new set of improved learners can be generated by adding a difference of teacher and mean vector to each learner in the current population as follows. ( t + 1) = ( t) + r *( Xbest( t) TF M( t)) (8) T F is the teaching factor with value between 1 and 2, and riis the random number in the range [0, 1]. The value of TF can be found using the following equation (9) (2) T F = round( 1+ rand(1)) (9) Learner phase: The knowledge of the learners can be increased by the interaction of one another in the class. For a learner, i, another learner is selected, j, randomly from the class. ( t) + r*( ( t) Xj( t)), iff(( ( t)) < f( Xj( t)) ( t+ 1) = ( t) + r*( Xj( t) ( t)), iff(( Xj( t)) < f( ( t)) (10) (3) where f( ) is (7) the objective function to be minimized. The two phases are repeated till a stopping criterion has met. Best learner is the best solution in the run. The stopping criteria: The stopping criteria in the present work is Stop by convergence or stagnation. The convergence of the algorithm is based on the fitness value of the fittest individual. The difference of fitness value of fittest individuals in any two successive generations is less than , is the stopping criteria. 6 Comparison From the experimental results the level means were calculated for roughness (Ra) and the individual graphs are drawn between surface roughness (Ra) and velocity (v), feed (f) and depth of cut (d) in three different conditions shown in Table 5, Table 6 and Table 7 and Figure2, Figure 3 and Figure 4. From the Figure 2 it is observed that the values of surface roughness obtained in the Carburising flame condition was lower than the remaining two conditions based on the velocity variation

4 Effect of Carburizing Flame and Oxidizing Flame on Surface Roughness in Turning of Al/SiC MMC and Teaching-Learning-Based Optimization of Process Parameters Veloc -ity m/mi n Table 5 Level mean Ra values for various velocities in different environments Dry environment Carburizing flame condition Oxidizing Flame Condition Surface roughness Ra, µm Figure 2 Velocity Vs Surface Roughness in different Environments From Table 5 it is observed that at velocity of 34 m/min in Carburizing flame condition the percentage of reduction in surface roughness is compared to Dry environment. At same velocity in Oxidizing flame condition the percentage of reduction in surface roughness is compared to Dry environment. At velocity of 64 m/min in Carburizing flame condition the percentage of reduction in surface roughness is compared to Dry environment. At same velocity in Oxidizing flame condition the percentage of increase in surface roughness is 7.09 compared to Dry environment. At velocity of 94 m/min in Carburizing flame condition the percentage of reduction in surface roughness is compared to Dry environment. At same velocity in Oxidizing flame condition the percentage of increase in surface roughness is 8.96 compared to Dry environment. Table 6 Level mean Ra values for various Feeds in different environments Feed mm/rev Velocity m/min. carburizing flame oxidizing flame dry cutting Dry environment Carburizing flame condition Ra µm for Oxidizing Flame Condition From the Figure 3, it is observed that the values of surface roughness obtained in the Carburising flame condition was lower than the remaining two conditions based on the Feed variation Surface Roughness µm Feed mm/min Carburizing Flame Oxidizing flme Dry Condition Figure 3 Feed Vs Surface Roughness in different Environments In the all conditions the surface roughness values are increases as feed increases. The values of Surface roughness for Dry condition are in between Carburising and Oxidising conditions up to certain limit. At higher value of feed in dry cutting condition the value of surface roughness obtained was higher than the other two conditions. From Table 6 it is observed that at feed of mm/rev. in Carburizing flame condition the percentage of reduction in surface roughness is compared to Dry environment. At same feed in Oxidizing flame condition the percentage of increase in surface roughness is compared to Dry environment. At feed of 0.178mm/min in Carburizing flame condition the percentage of reduction in surface roughness is compared to Dry environment. At same feed in Oxidizing flame condition the percentage of increase in surface roughness is compared to Dry environment. At feed of 0.249mm/rev. in Carburizing flame condition the percentage of reduction in surface roughness is compared to Dry environment. At same feed in Oxidizing flame condition the percentage of reduction in surface roughness is compared to Dry environment. Table 7 Level mean Ra values for various Depth of cuts in different environments Depth of cut mm Dry environment Carburizing flame condition Oxidizing Flame Condition From the Figure 4, it is observed that the values of surface roughness obtained in the Carburising flame condition was lower than the remaining two conditions based on the Depth of cut variation

5 5 th International & 26 th All India Manufacturing Technology, Design and Research Conference (AIMTDR 2014) December 12 th 14 th, 2014, IIT Guwahati, Assam, India Surface roughness µm Figure 4 Depth of Cut Vs Surface Roughness in different Environments From Table 7 it is observed that at depth of cut of 0.25mm in Carburizing flame condition the percentage of reduction in surface roughness is compared to Dry environment. At same depth of cut in Oxidizing flame condition the percentage of reduction in surface roughness is compared to Dry environment. At depth of cut of 0.5 mm in Carburizing flame condition the percentage of reduction in surface roughness is 16.2 compared to Dry environment. At same depth of cut in Oxidizing flame condition the percentage of increase in surface roughness is compared to Dry environment. At depth of cut of 0.75mm in Carburizing flame condition the percentage of reduction in surface roughness is 21.5 compared to Dry environment. At same depth of cut in Oxidizing flame condition the percentage of decrease in surface roughness is 2.82 compared to Dry environment. 7 Optimization The TLBO optimization technique is used for global optimization for the objective functions as shown below. The optimization values of parameters obtained from TLBO is common values for minimum surface roughness in carburizing flame turning, oxidizing flame turning, and dry cutting. The objective function is Minimization of S. R1. = v f d (11) S. R2. = v f d S. R3. = v f d Maximization of MRR. = Depth of Cut mm Carburizing flame Oxidizing flame Dry condition v f d Subjected to constraints (12) (13) (14) 34 v f d 0.75 The proposed TLBO based multi-objective solution contains a set of learners as a population. Each learner is a single dimensioned vector to represent the optimal values of v, f, and d. The problem is posed as one of multi-objective optimizations and the defined objective function is the minimization of f = S. R1 + S. R2 + S. R3 + 1/ MRR (15) Table 8 The optimal values in first 10 sample runs of 100 different independent runs S.no. v f d fitness The experiments are conducted with population size 50.The stopping criteria is based on the fitness value of the fittest individual. The difference of fitness value of fittest individuals in any two successive generations is less than , is the stopping criteria. The TLBO algorithm is executed 100 times and the fittest chromosome values are recorded. The average values of v, f, and d are m/min, mm/rev, and mm. The optimal values in first 10 sample runs of 100 different independent runs are tabulated in Table 8. 6 Conclusions The following conclusions were made from this work 1. It has been found out that cutting performance of Carburizing flame heating condition is better compared to Oxidizing flame heating and Dry machining. It is due to the fact that Carburizing flame provides case hardness to the work piece. This reduces the continuous chip formation, reduces Built-up edge, improves chip- tool interaction and maintains sharpness of the cutting edge. 2. From the experimental results it is observed that the values of surface roughness obtained in the Carburizing flame heating were lower than the 556-5

6 Effect of Carburizing Flame and Oxidizing Flame on Surface Roughness in Turning of Al/SiC MMC and Teaching-Learning-Based Optimization of Process Parameters remaining two environments based on the velocity variation, Feed Variation and depth of cut Variation. The values of Surface roughness for Dry cutting are in between Carburizing flame heating and Oxidizing flame heating. 3. From the experimental results it is observed that in carburizing flame condition the overall percentage of reduction in surface roughness is compared to dry cutting. In oxidizing flame condition the overall percentage of increase in surface roughness is The variation of surface roughness values are very high in feed variation compared to velocity variation and depth of cut variation. Hence feed is observed to be more dominant factor compared to cutting speed and depth of cut. References Attia, H., Tavakoli, S., Vargas, R., Thomson, V., (2010), Laser-assisted High-speed Finish Turning of Superalloy Inconel 718 under Dry Conditions, CIRP Annals - Manufacturing Technology, Vol. 59, No. 1, pp Diniz,A,E. and R. Micaroni. (2002), Cutting conditions for finish turning process aiming: the use of dry cutting, International Journal of. Machine Tools and Manufacturing, Vol. 42. pp El Baradie, M.A. (1996) Cutting fluids. Part I. Characterisation, Journal of Material. Processing. Technology. 56, Loony, L.A., Monaghan, J.M., O Reilly, P. and Toplin, D.R.P. (1992), The turning of Al/SiC Metal Matrix composite, Journal of Material Processing 33pp Manna, A. and Battacharya, B. (2002), A study on different tooling systems during machining of Al/SiC- MMC, Journal of Material Processing technology, 123, pp Manna, A. and Battacharya, B.(2001) Investigation for effective tooling system to machine Al/SiC MMC. Proceedings of national conference of recent advances in material processing (RAMP 2001), 7-8 September 2001, Dept. of prod. Engg, Annamali university, pp Quan, Y. and Zehua, Z. (2000), Tool wear and its mechanism for cutting SiC particle-reinforced aluminum metal matrix composites, Journal of Material Processing, 100, pp Rao, R.V. & Kalyankar, V.D. (2012), Parameter optimization of modern machining processes using teaching learning-based optimization algorithm, Engineering Applications of Artificial Intelligence, Rao, R.V. & Patel, V. (2012), An elitist teachinglearning-based optimization algorithm for solving complex constrained optimization problems, International Journal of Industrial Engineering Computations,vol. 3no.4, Rao, R.V. & Patel, V. (2012). Multi-objective optimization of combined Brayton and inverse Brayton cycle using advanced optimization algorithms, Engineering Optimization, doi: / X Rao, R.V. &Savsani, V.J. (2012), Mechanical design optimization using advanced optimization techniques, Springer-Verlag, London. Rao, R.V., Savsani, V.J. &Vakharia, D.P. (2012). Teaching-learning-based optimization: A novel optimization method for continuous non-linear large scale problems. Information Sciences, vol.183, no.1, Riaz Muhammad., Agostino Maurotto., Anish Roy., Vadim V. Silberschmidt., (2012), Hot ultrasonically assisted turning of β-ti alloy, 5 th CIRP Conference on High Performance Cutting, Procedia CIRP 1 (2012), Roy P., Sarangi S.K., Ghosh. A., Chattopadhyay A.K. (2009), Machinability study of pure aluminium and Al 12% Si alloys against uncoated and coated carbide inserts, International Journal of Refractory Metals & Hard Materials, volume 27, issue 3, Sun, S., Brandt, M., Dargusch, M. S., (2010). Thermally Enhanced Machining of Hard-to-machine Materials - A review, International Journal of Machine Tools & Manufacture, Vol. 50, No. 8, pp Tash M., Samuel F.H., Mucciardi F., Doty H.W., Valtierra S, (2006), Effect of metallurgical parameters on the machinability of heat-treated 356 and 319 aluminum alloys, Journal of.materials Science and Engineering, Vol. 434, pp Taya, M. and Arsenault, R. (1989), Metal Matrix composites, Oxford Pergamon press, pp.1-9. Weinter, K. and Konig, W. (1993), A consideration of tool wear mechanism metal matrix composite, Ann CIRP, 42 pp Yongho Jeon., Hyung Wook Park., Choon Man Lee., (2013), Current Research Trends in External Energy Assisted Machining International Journal of precision Engineering and manufacturing, Vol 14, No. 2, pp