Materials Today: Proceedings 2 ( 2015 ) 1464 – 1468

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2214-7853 © 2015 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the conference committee members of the 4th International conference on Materials Processing and Characterization.doi: 10.1016/j.matpr.2015.07.071

4th International Conference on Materials Processing and Characterization

Optimization of InfluentialParameters on Mechanical behaviour of AlMg1 SiCu Hybrid Metal Matrix Composites using

Taguchi integrated Fuzzy Approach

M.Vamsi Krishnaa,G.Bala Narasimhab, N.RajeshcAnthony M .Xaviord aDepartment of Mechanical Engineering, M.I.T.S, Madanapalle, A.P, India

b,cDepartment of Mechanical Engineering, S. V. College of Engineering, Tirupathi, A.P, India 4SMBS,VIT,Vellore,Tamil Nadu

Abstract Aluminium alloy materials found to the best alternative with its unique capacity of designing the materials to give required properties. Aluminium alloy Metal Matrix Composites (AMMCs) are gaining wide spread acceptance for automobile, industrial, and aerospace applications because of their low density, high strength and good structural rigidity. In this paper, an attempt is made to examine the effect of influential parameters such as type of reinforcement, size of the reinforcing particle and weight percentage on mechanical properties. Stir casting technique has been employed to prepare the composites. The response parameters were tensile strength, impact strength and density. The design of experiments (DOE) approach using taguchi method was employed to analyze the mechanical behaviour of hybrid composites. Fuzzy approach were used to investigate the optimal combination of influencing parameters on the mechanical behaviour. © 2014 The Authors. Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the conference committee members of the 4th International conference on Materials Processing and Characterization.

Keywords:Hybrid MMCs, DOE, Taguchi, Fuzzy logic

1. Introduction: Conventional monolithic materials have limitations in achieving good combination of Strength, stiffness,

toughness and density. To overcome these shortcomings and to meet the ever Increasing demand of modern day technology, composites are most promising materials of recent interest, one of these being Hybrid Metal Matrix Composites (HMMCs) which possess high specific strength, toughness, impact strength and low sensitivity to temperature changes. As a result, many of the current applications for HMMCs are in aerospace and automobile components, where the service environments are demanding and dynamic loading is common [1].HMMCs typically are made of discontinuous fiber or particle phase that is stiffer and stronger than the continuous matrix phase.

* Corresponding author. Tel.: +0-000-000-0000 ; fax: +0-000-000-0000 . E-mail address:vamsikrishnam@mits.ac.in

© 2015 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the conference committee members of the 4th International conference on Materials Processing and Characterization.

1465 M.Vamsi Krishna et al. / Materials Today: Proceedings 2 ( 2015 ) 1464 – 1468

Aluminum based MMCS are still the subjects of intense studies, as their low density gives additional advantages

in several applications. Among the various useful aluminum alloys, aluminum alloy AlMg1 SiCu (Al 6061) is typically characterized by properties such as fluidity, castability, corrosion resistance and high strength-weight ratio. This alloy has been commonly used as a base metal for MMCs reinforced with a variety of fibers, particles and whiskers [2].In recent years, considerable work has been done on Silicon carbide reinforced metal matrix composites, because of its high strength, and also graphite reinforced metal matrix composites which exhibit low density, low wear rate and excellent antiseizing properties. Mechanical properties of MMCs are affected by the size, shape and weight fraction of the reinforcement, matrix material and reaction at the interface [3]. 2. Literature review:

K.L.meena, et.al [4] investigated their work on Al/SiC Metal matrix composites with Al 6063 as matrix and SiC as reinforcement of various sizes i.e,74μ, 53μ,44μ by the melt stirring technique, and reported that the mechanical properties were increased by decreasing the particle size and increasing the weight fraction of reinforcement. Khalid Mahmood Ghauri et.al [5] investigated their work on mechanical properties of Al/SiC Metal matrix composites with Al 6061 as matrix and SiC as reinforcement of 10μ by varying the weight fraction of the composites, and reported that the mechanical properties were increased by increasing the weight fraction of reinforcement.Prashant.S et.al [6] investigated their work on mechanical properties of Al/Gr Metal matrix composites with Al 6061 as matrix and Graphite as reinforcement by varying the weight fraction of the composites, and reported that the tensile strength increases high upto 9% and increases very slightly more than 9%, because of excess of graphite. S. Dhanalakshmi et.al investigated their work on processing parameters of AlSiC MMC produced by stir casting [7]. Vijaya Kumar and Venkataramaiah [8] have developed a hybrid approach by combining Taguchi, grey relational analysis method and fuzzy logic to reap their advantage in drilling process.

Fuzzy logic: Fuzzy logic has great capability to capture human commonsense reasoning, decision-making and

other aspects of human cognition. It overcomes the limitations of classic logical systems, which impose inherent restrictions on representation of imprecise concepts. Vagueness in the coefficients and constraints may be naturally modeled by fuzzy logic. Modeling by fuzzy logic opens up a new way to optimize cutting conditions and also tool selection importance of integration between fuzzy and ANN-based technique for effective process control in manufacturing. Several applications of fuzzy set theory-based modeling of metal cutting processes are reported in the literature. Hashmi, El Baradie, and Ryan [9] applied fuzzy set theory logic for selection of cutting conditions in machining. Lee, Yang, and Moon[10] used fuzzy set theory-based non-linear model for a turning process as a more effective tool than conventional mathematical modeling techniques if there exists ‘fuzziness’ in the process control variables 3. Experimentation: 3.1Materials and preparation:

The matrix material for present study is AlMg1 SiCu alloy. The reinforcing material selected was silicon carbide particle and graphite of size 3, 18 and 37 μm.In this process, first the aluminium alloy was placed in an electrical resistance furnace and heated to above its liquidus temperature i.e. 7500 c, so that the metal is totally melted. This melt is then cooled down to a temperature between the liquidus and solidus points and kept in a semi solid state. Prior to particle addition, Magnesium powder was added to melt to maintain the Wettability. At this stage, the preheated particles are added in three steps into the melt and mixed with the help of stirrer for 10 mins, and the melt was poured into the gravity die at 7300 - 8000c.

3.2Testing of composites:

The tensile behaviour of all the prepared samples were determined as per ASTM B-557 M “Standard Test Methods of Tension Testing Wrought and Cast Aluminum- and Magnesium-Alloy Products”. The Impact strength of the composites were carried out on charpy impact tester as per ASTM E 23 – 02a “Standard Test Methods for Notched Bar Impact Testing of Metallic Materials” .The densities of all the prepared specimens were investigated experimentally by the “Archimedean’s principle”.

Mechanical Properties tests have been performed on prepared composites by considering different parameter combinations. Tensile strength, Impact strength and Density are selected as indices to evaluate the mechanical

1466 M.Vamsi Krishna et al. / Materials Today: Proceedings 2 ( 2015 ) 1464 – 1468

properties of the MMC. Therefore these are considered as response characteristics in this study. Basically tensile strength and impact strength should be maximized and Density should be minimized for any MMC for better performance.

In the present work, three influential parameters are considered and each parameter is set at three levels. The

parameters and its levels are shown in Table - 1. For full factorial design, the experimental runs required are (levels)(factors) equal to 33 = 27. To minimize the experimental cost, fractional factorial design is chosen, ie.33-1 = 9 runs. Therefore Taguchi experimental design L9 is chosen for conducting experiments (Table - 2). Experiments are performed according to this design and the values of Tensile strength, Impact strength and Density are recorded (Table - 2) for each experimental run.

Table - 1. Influential parameters at three levels

4. Optimization of machining parameters:

The responses Tensile strength, Impact strength and Density are analyzed using fuzzy tool box of Matlab software and overall fuzzy grade values are determined. The optimum levels of influential parameters are determined based on overall fuzzy grade as follows:

Implementation of Fuzzy Logic: Fuzzy logic involves a fuzzy interference engine and a fuzzification -defuzzification module. Fuzzification expresses the input variables in the form of fuzzy membership values based on various membership functions. Governing rules in linguistic form, for example if cutting force is high and machining time is high, then tool wear is high, are formulated on the basis of experimental observations. Based on each rule, inference can be drawn on output grade and membership value. Inferences obtained from various rules are combined to arrive at a final decision. The membership values thus obtained are defuzzified using various techniques to obtain true value.

Table 2 Experimental design and Data

4.1 Determination of overall fuzzy grade: A fuzzy logic unit comprises a fuzzifier, membership functions, a fuzzy rule base, an inference engine and a

defuzzifier. In the fuzzy logic analysis, the fuzzifier uses membership functions to fuzzify the grey relational coefficient first. Next, the inference engine performs a fuzzy reasoning on fuzzy rules to generate a fuzzy value. Finally, the defuzzifier converts the fuzzy value into a fuzzy grade. The structure built for this study is a three input- one-output fuzzy logic unit as shown in Fig. 1. The function of the fuzzifier is to convert outside crisp sets of input data into proper linguistic fuzzy sets of information.

The input variables of the fuzzy logic system in this study are Tensile strength, Impact strength and Density.

They are converted into linguistic fuzzy subsets using membership functions of a triangle form, as shown in Fig. 2, and are uniformly assigned into three fuzzy subsets—Low (L), Medium (M), and High (H) grade. The fuzzy rule base consists of a group of if-then control rules to express the inference relationship between input and output. A

Parameters Design Process Parameters Level - 1 Level - 2 Level – 3

Type of reinforcement SiC Graphite SiC/Graphite Size of particle (μm) 3 18 37 Weight Percentage(%) 5 10 15

Exp. Run No.

Input Responses

Type of reinforcement

Size (μ)

Wt. (%)

Tensile strength (Mpa)

Impact strength (Joules)

Density (gm/cc)

1 SiC 3 5 161.24 26 2.77 2 SiC 18 10 155.83 27 2.75 3 SiC 37 15 150.95 30 2.74 4 Graphite 3 10 144.61 21 2.58 5 Graphite 18 15 143.75 19 2.64 6 Graphite 37 5 144.26 16 2.66 7 SiC/Graphite 3 15 192.45 43 2.59 8 SiC/Graphite 18 5 184.32 26 2.62 9 SiC/Graphite 37 10 177.23 29 2.61

1467 M.Vamsi Krishna et al. / Materials Today: Proceedings 2 ( 2015 ) 1464 – 1468

typical linguistic fuzzy rule called Mamdani is described as Rule 1: if x1 is A1, x2 is B1 then y is E1elseRule 2: if x1 is A2 , x2 is B2 ,then y is E2 else …………………………………………… …………………………………………….Rule n: if x1 is An, x2 is Bn, then y is Enelse In above Ai, Bi are fuzzy subsets defined by the Corresponding membership functions i.e., α/4Ai, α /4Bi. The output variable is the Fuzzy grade yo, and also converted into linguistic fuzzy subsets using membership functions of a triangle form, as shown in Fig. 3.

Fig. 1 Three input and one out output fuzzy logic unit Fig. 2 Input parameters for fuzzy

Fig. 3 Membership functions for output values

Unlike the input variables, the output variable is assigned into relatively nine subsets i.e., very verylow (VVL), very low (VL), low (L) Medium low (ML) medium low, medium (M), medium high (MH) high (H), very high (VH), very very high (VVH) Then, considering the conformity of three performance characteristics for input variables, 9 fuzzy rules are defined. The fuzzy inference engine is the kernel of a fuzzy system. It can solve a problem by simulating the thinking and decision pattern of human being using approximate or fuzzy reasoning. In this paper, the max-min compositional operation of Mamdani is adopted to perform calculation of fuzzy reasoning. 4.2 Optimal levels of Machining Parameters

After determining the overall fuzzy grade values as listed inTable-3, the effect of each process parameter is separated based on overall Fuzzy grade at different levels. Basically, large Fuzzy grade means it is close to the product quality, thus, a higher value of the Fuzzy grade is desirable. From the table 4, it shows that the experiment run 7 has highest fuzzy grade i.e, 0.9153 and it indicates the optimal process parameters with the best level are type of reinforcement is SiC/Gr (hybid) at level - 3, size 3μ at level-1 and weight percentage 15% at level- 3. The optimal levels for the controllable parameters obtained from this methodology are verified by the conformation test shown in Table-4. Table 3: Overall fuzzy grade

Exp. Run 1 2 3 4 5 6 7 8 9 Overall

fuzzy grade 0.2714 0.2908 0.301 0.4088 0.2832 0.2067 0.9153 0.6339 0.6642

1468 M.Vamsi Krishna et al. / Materials Today: Proceedings 2 ( 2015 ) 1464 – 1468

Table 4. Conformation test results

Type of reinforcement

Size (μ)

Wt. (%)

Tensile strength (Mpa)

Impact strength (Joules)

Density (gm/cc)

3 3 15 192.39 44 2.59

5. Conclusions:

The experiments have been conducted on various levels and parameters and obtained data has been analyzed using Fuzzy logic. The influence of type of reinforcement, size and weight percentage of theparticle on mechanical behaviour of composites was studied. Optimum influencing parameter combination has been found at the size of 3μ, combined SiC/Graphite of 15 % using fuzzy logic technique which yields good results in high strength and low density.

6. Reference

[1]C.J. Tong, et.al: Metall, Mater.Trans.A36 ,2005 (p.1263–1271.) [2]M.K. Surappa: Aluminium matrix composites: Challenges and opportunities, Sadhana, Vol. 28,No. 1&2, pp. 319 34, 2003. [3]Journals of material engineering and performance,springer, Dec 2011, vol 20 , issue 9. [4] K.L.meena, et.al: American Journal Of Mechanical Engineering, 2013, vol 1 (p 14 -19. ) [5]Khalid Mahmood Ghauri: Pak. J. Engg. & Appl. Sci. Vol. 12, Jan., 2013 (p. 102-110) [6]Prashant.S et.al: IntJ.MechEng&Rob.Res,Vol 1, No.3 (p. 85-95) [7]S. Dhanalakshmiet.al :Journal of Materials, Feb 2012 (p. 8-15) [8] G. Vijaya Kumar, et.al :Elixir Mech. Engg. 45 , 2012, (p7831-7839) [9]Hashmi K., et.al: Computers and Industrial Engineering, 35(3–4),1998 (p. 571–574) [10]Lee Y.H et.al, International Journal of Production Research, 37(4), 1999 (p.835–847). [11]P.Shaileshet.al.procedia material science 6(2014) pp812-820.