STUDY OF THE EFFECTS OF TURNING PARAMETERS IN ALUMINIUM METAL MATRIX COMPOSITES

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1 Volume 118 No , ISSN: (printed version); ISSN: (on-line version) url: ijpam.eu STUDY OF THE EFFECTS OF TURNING PARAMETERS IN ALUMINIUM METAL MATRIX COMPOSITES 1 D.Manikandan, 2 Balamurugan.N., 2 Dhinesh Kumar.B., 3 Arunkumar.R., 4 Madhavakannan.K., 1 Assistant Professor, 2,3,4 Student, Final Year B.E., Department of Mechanical Engineering, Karpagam College of Engineering, Coimbatore. 1 maniddr@gmail.com Abstract: Aluminium Composites have appreciable usage in our daily life. In this aspect our research contributes of finding feasible solutions on the preparation a composite with Aluminium LM0 with flyash and Aluminium oxide also its effects on machining characteristics. The aim of this paper is to study the effect of turning parameters such as cutting speed, feed rate, depth of cut and cutting tool nose radius on the surface roughness of hybrid metal matrix composite. The surface roughness will be tested on composites turned by a high speed CNC Centre Lathe. Analysis Of Variance (ANOVA) will be performed to predict the significant parameters of the composites. Here, stir casting process will be used to incorporate Aluminium oxide and fly ash particles into pure Aluminium. The right combination of Aluminium (LM0), Al 2 O 3 (Aluminium Oxide) and fly ash giving the best performance is to be found out. This paper consists of the analysis of output characteristics using Taguchi optimization algorithm technique with various combinations of Inputs characteristics. Keywords: Stir casting, Aluminium LM0, Fly Ash, Surface roughness, High Speed Turning 1. Introduction A composite material is a mixture of two or more distinct constituents, all of which are present in reasonable proportions (>5%) and have different properties so that the composite property is noticeably different from that of each of the constituents. It is not soluble in each other. One constituent is called the reinforcing phase and the one in which it is embedded is called the matrix. The reinforcing phase materials may be in the form of fibers, particles, flakes and whiskers. Metal matrix composites (MMC) are widely used in many industries such as aerospace, automotive, electronics and medical industries due to their desirable properties like high strength, low weight, high module, low ductility, and high wear resistance, high thermal conductivity and low thermal expansion. Aluminium, titanium and magnesium alloys are commonly used as metal matrix and silicon carbide (SiC), aluminium oxide (Al 2 O 3 ) and boron carbide (B4C) are commonly used as reinforcements during the production of MMCs. Now a day, Aluminium matrix composites adopt for the light weight to high strength alloy criteria. Due to progression of research in this field, the inclusion particulate matter in composites involves the usage of the byproducts from various sources into a usable manner. Even though the performance characteristics founds affected by these inclusion a thought on compromise made between effective usages of waste material. The materials taken are Aluminium LM0, with reinforcement of Aluminum oxide and Fly ash. Stir casting technique is employed for the preparation of composites. A. Objective 2. Objective and Scope The main objectives of the project are To familiarize on the selection of materials for composites. To prepare specimen using stir casting techniques. To identify machining conditions and to measure the response. Optimisation techniques for finding feasible solutions. B. Scope This project focuses on the preparation of composites with the utilization of the waste product from an Industry in an effective manner. Its extended scope applies to: It is convenient to divide the applications of metal matrix composites into aerospace and nonaerospace categories. Reduction in the weight of a component is a major driving force for any application in the aerospace field. 1555

2 A.Stir Casting Setup: 3. Experiment Details A 900 gm of commercial aluminum (Grade: LM0) was melted in a resistance induction furnace. The melt was degassed by purging hexachloro ethane tablets when the melt Temperature reached to C. Simultaneously, 50 gm of Aluminium oxide (150µm) and 50 gm of fly ash (150µm) particles were preheated to C with the aid of muffle furnace. Then the preheated Aluminium oxide particles were added with the melt and stirred using a mild steel stirrer. The preheated fly ash particles were then added to the melt at the time of formation of vortex due to stirring. The melt temperature was maintained at C C during the addition of the preheated particles. Then the melt was cast into cylindrical shape using a metallic die of SG400 spheroidal graphite iron. Then the component is allowed to cool at room temperature for few hours. Figure 3.Turned components Condition Details Work piece Al- 5% wt Al 2 O 3-5%wt material flyash powder Geometry of work 25 mm diameter piece 100 mm long Lathe used CNC Lathe Insert used Uncoated Carbide Insert Measuring Profilometer (surftest) instrument Mitutoyo SJ-210 Environment Dry Table 1.Operating conditions 4. Design of Experiments Figure 1.Stir casting setup B.High Speed Turning Operation: High speed turning operation was carried out on the Cylindrical work piece of hybrid (Al-5%wt Al 2 O 3-5%wt Fly ash) composite material with help of a CNC lathe shown in Fig.2. Uncoated tungsten carbide insert was used as cutting tool for turning operation and no cutting fluid was applied at the time of conducting the experiments. Surface roughness was measured on the turned surface of the work pieces shown in Fig.3 with the help of profilometer. The turning operation conditions are given in the Table 1. Figure 2.High speed CNC turning lathe Four important control parameters namely cutting speed (A), feed rate (B), depth of cut (C), and nose radius (D), each at four levels were considered in this study and are listed in Table 2. An orthogonal array L16 (4) 5 was selected for the conduct of experiments. Surface roughness was treated as output response with the category of quality characteristics smaller the better. The S/N ratio for this response can be estimated by using the Eq. (1). S N(dB)= 10log10(1\n i=1ri 2 ) (1) Where i=1, 2,, n (here n=4) and Ri is the response value for an experimental condition. Parameter Cutting speed(rpm) Feed rate(mm/re v) Depth of cut (mm) Nose Radius (mm) Table 2. Parameter and their levels Levels Notation A B C D

3 5. Result And Discussion A.Optimum Condition By Taguchi Method: The S/N ratio was calculated for each experimental condition given in Table 3. In order to find optimum level of the turning parameters, average S/N ratio response was estimated for every level of each parameter and the corresponding details are given in Table 4. Based on the highest value of S/N ratio, an optimum level for each parameter (A: 2rd level; B: 1th level; C: 4st level and D: 3nd level) was noted. The optimum turning condition A2 B1 C4 D3 (cutting speed of 3250 rpm, feed rate of 0.05 mm/rev, depth of cut of 0.5 mm and nose radius of 0.6 mm) was noted. Table 3. Experiments and S/N ratio Ex.no A B C D Surface Roughness (µm) S/N Ratio db for Ra R 1 R 2 R 3 R a Level A B C D Delta Rank Table 4. Optimum response Therefore optimized parameters are A2, B1, C4, D3 Speed3250 rpm, feed rate of 0.05 mm/rev, 0.5 mm depth of cut and 0.6 mm nose radius B. Optimum Condition By Analysis Of Variance: ANOVA is defined as the separation of the variance ascribable to one group of causes from the variance ascribable to other groups. Analysis of variance (ANOVA) is a collection of statistical models and their associated procedures (such as "variation" among and between groups) used to analyze the differences among group means. Analysis of variance was performed on S/N ratios to find the significance of turning parameters and their contribution towards surface roughness. By using non-linear regression analysis, the effect of control parameters on average surface roughness (Ra) was modeled as follows. SurfaceRoughness(Ra)= A_ A_ A_ A_ B_

4 B_ B_ B_ C_ C_ C_ C_ D_ D_ D_ D_0.8 For this model, it was found that r2 = where r is correlation coefficient. The value of r2 indicates the closeness of the model representing the process. Since r2 is nearing unity, this model can be taken as an objective function for the application of ANOVA through which better parameter settings can be found MINITAB genetic algorithm tool was used to find the optimum parametric condition for the minimization of surface roughness in this study. The mathematical model given in the above equation was used as fitness function. The bound for all process parameters (A, B, C and D) were inputted. The optimum parametric condition in the final generation was noted (cutting speed of 3750 rpm, feed rate of 0.05 mm/rev, depth of cut of 0.5 mm and nose radius of 0.8 mm). C.Confirmation Experiments: Confirmation experiments were conducted for the optimum parametric conditions suggested by Taguchi Method and ANOVA. Average surface roughness (predicted and tested) values are given in Table 5. It is evident that there is a good agreement between the predicted and actual surface roughness since the error is less than 5%. The optimum setting for feed rate (0.2 mm/rev) and depth of cut (0.2 mm) was noted to be same in the Taguchi method and ANOVA. From ANOVA, it is evident that the effect of nose radius ( mm) on surface roughness is negligible compared to the other parameters. With respect to cutting speed, the optimum setting of Taguchi method is greater than the setting of ANOVA. It is expected that the increase in cutting speed beyond 3250 rpm could result in vibrations during machining, which would cause poor surface finish. From the confirmation experiments, it is proved that ANOVA would give better result than Taguchi method in the aspect of surface quality and also indirectly in the aspects of energy savings and production time. Table 5. Optimum parametric conditions S.No Optimization Tool Optimum Parametric Conditions Average Surface roughness Ra (µm) Parameters Coded Uncoded Predicted Tested % error Cutting speed rpm Feed rate mm/rev 1 Taguchi method 2 ANOVA Depth of cut mm Nose radius mm Cutting speed rpm Feed rate mm/rev Depth of cut mm Nose radius mm The following are the conclusions drawn based on the surface roughness test conducted on hybrid metal matrix (Al-5%wt Al 2 O 3 5% wt Fly ash) composite during high speed turning operation with uncoated carbide insert. i) From the results obtained, a regression model has been developed for surface roughness. From the model equation, the value of surface roughness can be predicted if the values of cutting speed, feed and depth of cut are known. ii) From ANOVA, it can be concluded that cutting speed has a greater influence on the surface roughness followed by feed rate and depth of cut. Nose radius has least influence on surface roughness. iii) The validation experiment confirmed that the error occurred was less than 5% between the model and tested value. iv)the optimal settings of turning process parameters for optimal surface roughness can be used wherever hybrid metal matrix (Al-5%wt Al 2 O 3 5% wt Fly ash) composites require high degree of surface finish. v) From the confirmation experiments, it is clear that ANOVA exhibits better result than Taguchi method in the aspects of surface quality and energy savings and production time. 1558

5 vi) These optimum turning conditions can also be used when the hybrid metal matrix composites are turned for the typical applications like bearings, automobile pistons, cylinder liners, piston rings, connecting rods, sliding electrical contacts, turbo charger impellers, space structures, etc. References [1] Dr.E.,Baburaj state the effect of high speed turning operation on surface roughness of hybrid metal matrix (Al-SiC P -Fly ash ) composite. [2] M K Surappa, Aluminium matrix composites: Challenges and Opportunities, Sadhana, Vol. 28, Parts 1 & 2, February/April 2003, pp [3] M. N. Wahab, A. R. Daud and M. J. Ghazali, Preparation And Characterization Of Stir Cast- Aluminium Nitride Reinforced Aluminium Matrix Composites,International Journal of Mechanical and Materials Engineering (IJMME), Vol. 4 (2009),No. 2, [4] Johny James. S, Venkatesan. K, Kuppan.P and Ramanujam.R, Hybrid Aluminium Metal Matrix Composite Reinforced With SiC and TiB2, Procedia Engineering 97 ( 2014 ) [5] M.Sureshkumar, M.Mohana priya, C SathishKumar and, Dr.V.baskaran, Surface Roughness Optimization Of Metal Matrix Composite Using Taguchi Technique, International Journal of Engineering Research & Technology (IJERT) Vol. 2 Issue 3, March [6] Bharat Admile, S. G. Kulkarni, S. A. Sonawane, Review on Mechanical & Wear Behavior of Aluminum-Fly Ash Metal [7] Matrix Composite, International Journal of Emerging Technology and Advanced Engineering, Volume 4, Issue 5, May [8] S. Balasivanandha Prabu, L. Karunamoorthy, S. Kathiresan, B. Mohan, Influence of stirring speed and stirring time on distribution of particles in cast metal matrix composite, Journal of Materials Processing Technology 171 (2006) [9] Keneth Kanayo Alanemea, Idris B. Akintunde, Peter Apata Olubambi, Tolulope M. Adewale, Fabrication characteristics and mechanical behavior of rice husk ash Alumina reinforced Al Mg Si alloy matrix hybrid composites, Journal of Materials Reasearch Technology, 2013;2(1): [10] R. K. Swamy, A. Ramesha, G.B. Veeresh Kumar, J. N. Prakash, Effect of Particulate Reinforcements on the Mechanical Properties of Al6061- WC and Al6061-Gr MMCs,Journal of Minerals & Materials Characterization &Engineering, Vol. 10, No.12, pp , 2011 [11] H.C. Anilkumar, H.S. Hebbar and K.S. Ravishankarstate, Mechanical Properties Of Fly Ash Reinforced Aluminium Alloy (Al6061)Composites, International Journal of Mechanical and Materials Engineering(IJMME), Vol.6 (2011), No.1, [12] T. Padmapriya and V. Saminadan, Priority based fair resource allocation and Admission Control Technique for Multi-user Multi-class downlink Traffic in LTE-Advanced Networks, International Journal of Advanced Research, vol.5, no.1, pp , January [13] S.V.Manikanthan and T.Padmapriya Recent Trends In M2m Communications In 4g Networks And Evolution Towards 5g, International Journal of Pure and Applied Mathematics, ISSN NO: , Vol- 115, Issue -8, Sep

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