Experimental Study on Parameter Optimization of CNC End Milling for Composite Material LM6 Al/SiCp

Experimental Study on Parameter Optimization of CNC End Milling for Composite Material LM6 Al/SiCp

Neeraj Kumar1

1. hd Student (Mechanical), Faculty of Engineering, Pacific Academy of Higher Education and Research University, Udaipur

   Prof. K. K. Chhabra2

   Professor, Faculty of Engineering,

   Pacific Academy of Higher Education and Research University, Udaipur

   Abstract In this research work an attempt is made to access the effect of certain control parameters on both surface roughness and material removal rate in cnc end milling on the machinability of LM6/ SiCp metal matrix composites under wet cutting condition. Weight percentage of SiCP in the metal matrix, cutting speed, depth of cut, feed & different diameter of end mill cutter are selected as the influencing parameters. To evaluate the output quality characteristics an experiment is performed on the five factors with three levels and Taguchis Design of Experiment (DoE) with L27 orthogonal array. Analysis of Variance (ANOVA) is used to calculate percentage contribution of individual parameter to explain their influences on output performance characteristics.

   It was observed that most significant parameter is feed in surface roughness and depth of cut is in material removal rate, but it was also observed that higher weight percentage give a higher surface roughness & less material removal rate and one common input parameter end mill cutting tools diameter play significant role in both surface roughness & maximum material removal rate. This experiment result will provide essential guidelines to the manufacturers.

   Keywords Metal matrix composites, Taguchi, ANOVA, Surface roughness, Material removal rate

   1. INTRODUCTION

Composite materials are named as the materials of the future in 1970 when they have been introduced in engineering applications [1]. Metal matrix composites (MMC) have become a large leading material in composite materials. The most common MMC material is an aluminum matrix reinforced with SiC particles [2]. Reinforced aluminium MMCs have received considerable attention due to their excellent engineering properties. These materials are known as the difficult-to-machine materials, because of the hardness and abrasive nature of reinforcement element like silicon carbide (SiC) particles [3]. Aluminium-based SiC particle reinforced MMC materials have become useful engineering materials due to their properties such as low weight, heat- resistant, wear-resistant and low cost [4]. Al/SiC particulate composites are increasingly being used for varieties of engineering applications from automotive to aircraft components. The common applications are bearings, automobile pistons, cylinder liners, piston rings, connecting rods, sliding electrical contacts, turbo charger impellers, space structures, etc [5].

There are numerous methods of manufacture of MMCs. Of these, the stir casting method is most popular due to its unique advantages. In this method the reinforcing particles are introduced into the melt and are stirred thoroughly to ensure their

homogeneous mixing with the matrix alloy. The properties of the particle reinforced metal matrix composites produced this way are influenced to a large extent by the type, size and weight fraction of the reinforcing particles and their distribution in the cast matrix [6]. A problem with MMCs is that they are hard to machine due to the reason of high hardness of the reinforcement materials which in mostly cases are significantly harder than the commonly used high speed steel cutting tools. MMCs reinforced with SiCp particles are extremely difficult to machine (turning, milling, drilling, threading) due to their extreme abrasive. As the presence of hard reinforcement particles makes them extremely difficult to machine as they lead to rapid tool wear [7].

Machinability studies of metal matrix composites has been receiving growing attention from investigators because these studies have mainly focused on optimizing certain influencing parameters so that an output quality characteristic can be controlled. Mostly studies are on Al/SiC-MMC composite machining shows that minimizing the surface roughness because that is very difficult to be controlled.

In the present research work an experiment study is made on parameter optimization of end milling for MMCs composite material LM6 Al/SiCp under wet cutting condition by using some statistical technique for design of experiments(DoE). Weight percentage of SiC, cutting speed, feed, depth of cut and diameter of end mill cutting tool were chosen as the influencing control parameters and a taguchis orthogonal array L27 design of experiments was carried out to collect the experimental data and to analyze the effect of these parameters on surface roughness and material removal rate.

1. EXPERIMENTAL PROCEDURE

   Fabrication of MMCs

   The Metal matrix composites (MMCs) used in present study was carried out with LM6 Aluminum alloy reinforced with silicon carbide particles of mesh size 200 with 5%, 10%, and 15% weight manufactured through stir casting machine is used for experimentation. The composition of LM6 is tabulated in Table1.

   TABLE 1: CHEMICAL COMPOSITION (LM6)

   Elements	Al	Si	Cu	Mn	Mg	Fe	Zn	Cr
   %age	87.33	10.41	0.14	0.35	0.28	0.98	0.38	0.02
   Elements	Ni	Ti	Ca	Pb	V	P	As
   %age	0.01	0.02	0.01	0.01	0.01	0.00 1	0.00 8

   To prepare the specimens the LM6 aluminum alloy was melted in an electric resistance furnace having a clay graphite crucible. The melt was mechanically stirred after addition of silicon carbide particles of mess 200 micron. The processing of the composite was carried out at a temperature of 640oC with a stirring speed of 400 rpm in order to disperse the particles in the melt. The melt was poured into sand mold. The dimension of the work piece was rectangle block (100mm × 100mm × 10mm).

2. PLAN OF EXPERIMENTATION

   The experiments of end milling process on Aluminium based composite material LM6 Al/SiC are to be performed on Jyoti VMC 430 vertical machine centre shown in figure 1. It is installed in AVTS Hi-Tech Training Centre, Tarsali, Vadodara.

   Figure-1: Jyoti VMC 430

   The machining length was approximately 100mm. After performing the machining process, the surface roughness was measured using a HOMMEL surface roughness tester at MS university, vadodara shown in figure-2.

   Figure-2: HOMMEL surface roughness tester

   It was done at seven different locations along machining length and the mean value of these seven reading was used for the purpose analysis.

   In this work the end mill cutting tools are made up of solid carbide tool of different diameters are 6mm, 8 mm & 10 mm shown in figure 3.

   Figure 3: Solid carbide end mill with different diameters

3. TAGUCHI'S TECHNIQUE

In the globalized market, manufacturing companies have to counter the challenges in producing high quality products while simultaneously improving the processes ith a significant slash in time and cost. One of the most efficient tools to counter the challenge is Taguchi method [8]. Taguchi Method was proposed by Dr. Genichi Taguchi in the year 1950. Taguchi defines the quality of a product, in terms of the loss to society caused by a product during its life cycle [9]. Taguchi's technique shown in figure-4 is a powerful tool in quality optimization. Taguchi's technique makes use of a special design of orthogonal array (OA) to examine the quality characteristics through a minimal number of experiments. Taguchi proposed the experimental design, which involves orthogonal arrays to organize the parameters affecting the process and the levels that should be varied [10].

Figure-4: Taguchi method flowchart [11]

The orthogonal array design reduces number of experimental runs in order to obtain the best optimal solution. The numbers of influencing parameters running on experimental are based on Orthogonal Array (OA), which is analyzing data, identifying the optimal condition and conducting the confirmation runs [12].

The experimental results based on the orthogonal array are then transformed into S/N ratios to evaluate the performance characteristics. The S/N ratio transforms several repetitions into one value which reflects the amount of variation present and the mean response. There are several S/N ratios available depending on the type of characteristic: continuous or discrete [13]. Two of the applications in which the concept of S/N ratio is useful are the improvement of quality through variability reduction and the improvement of measurement [14].

The S/N ratio characteristics can be divided into three categories when the characteristic is continuous:

Smaller-is-the-better (Minimize):

1 2

= 10 (1)

=1

Larger-is-the-better (Maximize):

1 1

= 10 2 (2)

=1

Nominal-is-the-best:

1

= 10 2 (3)

=1

Where n is number of replications of each experiment, represents the experimentally observed value of ith experiment, is the average of observed and 2 is the variance of y.

In this present work Taguchi's Design of Experiments is used to design the orthogonal array L27 for 5 control parameters varied through 3 levels. The control parameters and their levels chosen are shown in Table 2.

TABLE 2: CONTROL PARAMETER & THEIR LEVELS

The various combinations of weight percentage of SiC, Cutting Speed, Feed, Depth of cut and diameter of cutting tool, based on orthogonal array L27 are presented in Table 3.

TABLE-3: COMBINATION OF CONTROL PARAMETER WITH ORTHOGONAL ARRAY L27

ANALYSIS OF SURFACE ROUGHNESS:

Metal matrix composite LM6 Al/SiC workpieces subjected to straight grooving operation is shown in Figure 5.

Figure 5: CNC End milled LM6 Al/SiC workpieces at different SiC%

Each experiment has been repeated twice at same trial i.e sample size is two per trial and the average values of Ra has been recorded. The surface roughness (Ra) of the machined surface on the composite materials LM6 Al/SiC workpieces has to be minimize for a given set of input parameters. The surface roughness obtained is used to calculate the signal-to-noise (S/N) ratio to obtain the best setting of the parameters arrangement. Hence, the Smaller-is-the-better condition is chosen as given in Equation-1. Table 4 shows the S/N ratio for surface roughness values measured on the workpiece surface.

TABLE 4 : S/N RATIO FOR SURFACE ROUGHNESS

composite material LM6 Al/SiC with varying weight percentage of SiC . To make the analysis simple and avoid longer procedure of carrying out general linear model of ANOVA and associated errors in calculation, Multi Factor ANOVA technique is used for determining the most contributing parameter on surface roughness with level of significance for individual parameters effect as well as interaction effect of combination of input parameters. It was seen that diameter of cutting tool is play a significant role in order to obtain good surface quality, due to that reason here taken interaction with other control parameters. Analysis of variance is performed and the outcomes are tabulated in Table 6 for surface roughness (Ra).

The best levels of various parameters are identified by calculating S/N ratio, corresponding to every level of parameters of surface roughness, which are consolidated in Table 5.

TABLE 5: RESPONSE TABLE FOR SURFACE ROUGHNESS

From the response table of surface roughness, the optimal parameter levels are identified as, SiC of 5%, speed of 4500 RPM, feed of 50 mm/min, depth of cut of 1 mm and cutter diameter of 10mm. Hence, the optimum condition is represented as A1B3C1D2E3. Along with optimal condition it is also observed that higher weight percentage of SiC give a higher surface roughness but cutter diameter play significant role to obtain good surface quality.

Mean of SN ratios

From the response table of surface roughness, the main effects plot is drawn, as shown in figure 6.

Main Effects Plot for SN ratios

Data Means

SiC % (A ) SPEED (B) (rpm) FEED (C ) (mm/m)

-4.0

-4.5

-5.0

-5.5

-6.0

5 10 15 2500

DEPTH O F C UT(D)(mm)

3500

C utter Dia (mm)

4500 50 75 100

0.5 1.0 1.5 6

8 10

Signal-to-noise: Smaller is better

-4.0

-4.5

-5.0

-5.5

-6.0

Figure 6: Main effects plot for surface roughness

ANOVA for Surface Roughness:

Analysis of variance (ANOVA) for Surface Roughness (Ra) is carried out using MINITAB software for experimental data obtained during CNC End Milling of three workpiece of

From the ANOVA table it is evident that the Feed Rate is the most significant control parameter contributing by 19.61 %, followed by cutter diameter by 19.3%. The contribution of depth of cut towards surface roughness is negligible.

TABLE 6: ANOVA FOR SURFACE ROUGHNESS

S = 0.704230 R-Sq = 98.23% R-Sq(adj) = 88.52%

Figure 7 shows the percentile contribution of various input control parameters over surface roughness values.

Figure 7: Percentile contribution of parameters for surface roughness

ANALYSIS OF MATERIAL REMOVAL RATE

Material Removal Rate (MRR) can be calculated by two methods namely weight wise and volume wise. Weight wise method is more accurate in comparison of volume wise. But in this research work volume wise method is chosen due to numerous cut on the workpiece.

TABLE 8: RESPONSE TABLE FOR MATERIAL REMOVAL

RATE (Larger is better)

MRR in mm3/min = ( ××)

Where L is the machining length, D is the depth of cut and W is the width of cut, and these dimension are measured with digital vernier caliper. In the above MRR relationship t is the machining time in minute.

For a given set of input control parameters, the amount of material removed has to be maximum. Hence, the Larger-is-the- better condition is chosen for this purpose as given in Equation. 2. Table 7 shows the S/N ratio of Material Removal Rate.

TABLE 7: S/N RATIO FOR MATERIAL REMOVAL RATE)

The best levels of various parameters are identified by calculating the average values of S/N ratio, corresponding to each level of parameters and are consolidated in Table 8.

From the response table of MRR, the optimal parameter levels are identified as, SiC% of 5 %, speed of 3500 RPM, feed of 100 mm/min, Depth of cut of 1.5 mm and cutter diameter of 10 mm. Hence, the optimum condition is represented as A1B2C3D3E3 From the response table of MRR, the main effects plot is drawn, as shown in figure 8.

Main Effects Plot for SN ratios

Data Means

0.5

1.0

1.5

6

8

10

Signal-to-noise: Larger is better

95.0

92.5

90.0

87.5

85.0

DEPTH O F C UT(D)(mm)

100

75

50

4500

3500

C utter Dia (mm)

2500

15

10

5

95.0

92.5

90.0

87.5

85.0

SiC % (A ) SPEED (B) (rpm) FEED (C ) (mm/m)

Mean of SN ratios

Figure 8: Main effects plot for Material Removal Rate

ANOVA forMaterial Removal Rate

Analysis of variance is performed and the outcomes are tabulated in Table 9 for material removal rate.

Table 9: ANOVA for Material Removal Rate(mm3/min.)

S = 1.36332 R-Sq = 98.90% R-Sq(adj) = 92.83%

Figure 9 shows the percentile contribution of various input control parameters over material removal rate values.

Figure 9: Percentile contribution of parameters for MRR

VI CONFIRMATORY TEST

The validity of optimum milling parameter levels hasbeen checked through confirmation experiments. The confirmation test values of both surface roughness and material removal rate are justified results of taguchi technique.

VII CONCLUSION

The research in the present study analyzes the influence of certain control parameters on both surface roughness and material removal rate and subsequently Taguchi's technique optimizes the control parameter levels within the range examined based on lower Ra and higher MRR. The outcomes from the experimental investigation and analysis fr straight grooving operation.

1. For surface roughness, the optimal level of input parameters are SiC of 5%, speed of 4500 RPM, feed of 50 mm/min depth of cut of 1 mm and cutter diameter of 10 mm.

2. The best input control parameters are SiC% of 5 %, speed of 3500 RPM, feed of 100 mm/min, Depth of cut of 1.5 mm and cutter diameter of 10 mm.

3. The study also concluded that the effect of different percentage weight SiC on both surface roughness and material removal rate is negligible based on 95 % confidence level.

4. The study also observed that end mill tool cutter diameter play significant role in the both surface roughness and material removal rate.

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