International Journal of Engineering and Techniques - Volume 2 Issue 4, July – Aug 2016
RESEARCH ARTICLE
OPEN ACCESS
Modeling and Analysis of Machining Characteristics of Metal Matrix Composite in Milling Process 1, 2, 3
N.Keerthi1, N.Deepthi2,N.Jaya Krishna3
Mechanical Engineering, Annamacharya Institute of Technology and sciences Autonomous and Rajampet
I.INTRODUCTION
In the area of globalization manufacturers are facing the challenges of higher Quality and productivity are two important . Productivity can be interpreted in terms of material removal rate in the machining operation and quality represents satisfactory yield in terms of product characteristics as desired by the customers. but conflicting criteria in any machining operations. In order to ensure high productivity, extent of quality is to be compromised. It is, therefore, essential to optimize quality and productivity simultaneously. Dimensional accuracy, form stability, surface smoothness, fulfillment of functional requirements in prescribed area of application etc. are important quality attributes of the product. Increase in productivity results in reduction in machining time which may result in quality loss. On the contrary, an improvement in quality results in increasing machining time thereby, reducing productivity. Therefore, there is a need to optimize quality as well as productivity. Optimizing a single response may yield positively in some aspects but it may affect adversely in other aspects. The problem can be overcome if multiple objectives are optimized simultaneously. It is, therefore, required to maximize material removal rate (MRR), and to improve product quality simultaneously by selecting an appropriate (optimal) process environment. To this end, the present work deals with multi-objective optimization philosophy based on Taguchi-Grey
ISSN: 2395-1303
relational analysis method applied in CNC end milling operation.
II. STIR CASTING PROCESS: In a stir casting process, the reinforcing phases are distributed into molten matrix by mechanical stirring. Stir casting of metal matrix composites was initiated in 1968, hen S. Ray introduced alumina particles into aluminum melt by stirring molten aluminum alloys containing the ceramic powders. Mechanical stirring in the furnace is a key element of this process. The resultant molten alloy, with ceramic particles, can then be used for die casting, permanent mold casting, or sand casting. Stir casting is suitable for manufacturing composites with up to 30% volume fractions of reinforcement. The cast composites are sometimes further extruded to reduce porosity, refine the microstructure, and homogenize the distribution of the reinforcement. A major concern associated with the stir casting process is the segregation of reinforcing particles which is caused by the surfacing or settling of the reinforcement particles during the melting and casting processes.The final distribution of the particles in the solid depends on material properties and process parameters such as the wetting condition of the particles with the melt, strength of mixing, relative density, and rate of solidification.The distribution of the particles in the molten matrix depends on the geometry of the mechanical stirrer, stirring parameters, placement of the mechanical stirrer in the melt, melting temperature, and the characteristics of the particles added.
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International Journal of Engineering and Techniques - Volume 2 Issue 4, July – Aug 2016
III. COMPOSITE MATERIAL PREPARATION: For composite material selection of Matrix and reinforcement are of prime importance. For this research work we had selected material as follows. Matrix
Aluminium alloy 2000, 6000 and 7000 series are used for fabrication of the automotive parts. PAMC under study consist of matrix material of aluminium alloy Al6082 whose chemical composition is shown in the Table. An advantage of using aluminium as matrix material is casting technology is well established, and most important it is light weight material. Aluminium alloy is associated with some disadvantages such as bonding is more challenging than steel, low strength than steel and price is 200% of that of steel. But with proper reinforcement and treatment the strength can be increased to required level.
Table size
360mm*132 mm
Spindle motor capacity
0.4 kw
Spindle nose taper
BT 30
Spindle
Programmable spindle speed Accuracy
Positioning
150-3000rpm
0.010 mm
Repeatability
+_0.005 mm
Programmable feed rate X Y Z axis
0-1.2 mm/min
Control system
PC based 3 Axis continuous path
Feed Rate
CNC controller
Power source
230V, single phase, 50 Hz
Reinforcement
Particles of Al2O3, magnesium and zinc are used as reinforcement.
Table 1.Specifications Of Cnc Milling Machine
Technical specifications Travels X axis
225 mm
Fig 1.Expermential set up ( CNC Machnie)
Z axis
115 mm
IV. WORK MATERIALPREPARATION
Y axis
Distance between Table top and spindle nose
ISSN: 2395-1303
150 mm 70-185 mm
The work material is cut as required sizes of 90x90x12 mm from Al6082-Mg-Zn alloy matrix raw stock to perform milling operation on them. These work materials are prepared by using the stir casting process.
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International Journal of Engineering and Techniques - Volume 2 Issue 4, July – Aug 2016
Fig 3 Strining of metals
Milling operation is performed on Al 6082-Cu-Zn Zn alloy work material according to full ull factorial design using CNC milling machine. The surface roughness values are measured using Talysurf meter . The Metal removal rate is calculated by means of formula is given by
Table 2. Process parameters and their levels Fig 4 Melting of alloys
Symbol
The required work materials are prepared by using the stir casting process with three different compositions of aluminum aluminum-copperzinc alloy matrix.
Fig 6 Talysurf meter
V. EXPERIMENTAL PROCEDURE
The Input parameters of the milling process and their levels (each input parameter has three levels) are listed based on previous works (Table 1.2).
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Unit
Level1
Level2
Level3
Feed
Mm/min
50
75
100
A
Spindle speed
C
Depth of cut
B
Fig .5Pouring Pouring of molten metal into mould
Machining parameter
rpm mm
1400 0.5
1600 0.75
1800 1
VI. Results from ANN
Table 3. Experimental data Speed 1800
Feed
Depth of cut MRR
Ra
75
0.75
557.413
2.494
1400
75
0.5
369.003
2.325
1400
100
0.75
744.909
1.469
1600
75
1
757.95
2.774
1600
100
0.5
502.26
1.399
1400
50
0.5
249.79
0.866
1400
50
0.75
377.99
2.46445
1400
75
1
738.91
4.1435
1600
75
0.5
376.175
1.0125
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International Journal of Engineering and Techniques - Volume 2 Issue 4, July – Aug 2016 1600
75
0.5
376.175
1.0125
1800
50
0.75
373.567
0.912
1400
100
1
1013.34
2.304
1600
50
0.75
375
.8245
1600
50
1
499.583
1.88
1400
50
1
499
1.85
1800
50
0.5
246.79
0.9055
1800
100
0.75
749.375
1.405
1600
50
0.5
248.18
0.9975
1400
100
0.5
503.94
1.435
1800
75
1
750.469
2.858
1600
100
0.75
751.252
1.169
1800
50
1
508.345
2.6935
1800
100
1
998.17
1.441
1800
75
0.5
370.461
1.3735
1800
100
0.5
492.935
1.3645
1600
75
0.75
562.5
1.368
1600
100
1
1021.27
1.6585
1400
75
0.75
565.82
2.5195
Actual Predicted Actual Predicted MRR MRR Ra Ra 557.413
510.65
2.494
2.152
744.909
706.395
1.469
1.568
502.26
461.857
757.95
323.63
710.652
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2.325 2.774 1.399
220.36
0.866
1.095
2.1435
2.895
377.99
312.265
2.46445
376.175
325.822
1.0125
1013.34
995.495
738.91
373.567 375
685.32
315.236
0.912
1.624
499.375
425.963
2.888
749.375
702.965
246.79 248.18 503.94
750.469
213.262
0.9055
224.586
0.9975
706.95
2.858
449.56
751.252
680.569
998.17
945.562
492.935
482.62
508.345
1.236
1.435
1.125
1.169
335.26
1.3735
562.5
521.354
1.368
565.82
524.52
978065
2.235 1.0312
2.6935
1021.27
1.645
1.405
487.95
370.461
1.125
1.88
1.8245
436.87
1.231 2.0135
304.23
499.583
2.124
2.304
1.441
1.3645 1.6585 2.5195
VI.RESULTS FROM TAGUCHI:
Table 4. Comparison between Experimental and values
369.003
249.79
1.321 2.452 1.523 2.158 1.875 1.468 1.568 1.647 1.425 2.145
From the graph the results predicted are
Graph for MRR
2.048 2.568
1.5144
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International Journal of Engineering and Techniques - Volume 2 Issue 4, July – Aug 2016
Optimum input parameters are
Feed:100mm/min
Speed;1400rpm Doc:1mm
Graph for Ra Main Effects Plot for SN ratios A
-2
Data Means
Source
B
Mean of SN ratios
-4 -6 -8
1
2 C
-2
3
1
2
3
-4 -6 -8
1
2
Signal-to-noise: Smaller is better
The MRR is mostly influenced by DOC about 62.29 % of MRR is influenced by DOC This is because by increasing the DOC the volume of material removed is increased.
Table 6. ANOVA For surface roughness: DF
SS
MS
Speed
2
1.338
0.064
0.253
9.54
Feed
6
2.46 75
0.6591
0.07
0.163
3.99
Doc
18
10.4 205
0.5789
0.579
0.761
86.47
3
The optimum set of input parameters are:
Speed;1400rpm Feed: 50mm/min Doc:0.5mm
RESULTS FROM ANOVA:
Anova method is used to find the effect of input parameters on output parameters. The effect is individually find out are
DF
SS
MS
Speed
2
125.3480
Feed
6
642023.8 445
62.67 40
Doc
18
702851.6 382
10700 3.974 1 39047 .3132
From the table it is found that ISSN: 2395-1303
VARIEN CE
St.Dev
St.ev
% TOTAL
Ra is mostly effected by Depth of cut .it is almost effected by 87% We already know that surface roughness is more if we remove more amount of material in single cut.
VII. CONCLUSIONS
Table 5.Anova For MRR Source
3.95 46
VARI ENCE
% TOTAL
1882.367
10.253
2.10
22652.22 0
150.507
35.71
39047.31 3
197.604
62.29
In the present work an Artificial Neural Network (ANN) model has been developed to predict the response (output) parameters surface roughness, and material removal rate in Milling process. The controllable parameters such as cutting speeds, feed rate and depth of cut which influence the responses are identified and analyzed. The optimum combinations of (input) process parameters are determined by Taguchi method. For producing low value of surface roughness, the optimum parameter values are spindle speed (V) 1400, feed (f) 50, Depth of cut (t)0.5.
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International Journal of Engineering and Techniques - Volume 2 Issue 4, July – Aug 2016
For high value of material removal rate, the optimum parameter values are spindle speed (V) 1400, feed (f) 1, depth of cut (t) 1. The analysis of variance (ANOVA) is also employed to find the contribution of input parameters on output parameters. Surface roughness is mostly affected by Depth of cut. Material removal rate is mostly affected by Depth of cut.
VIII. FUTURE SCOPE
Similar type of techniques is used for engineering materials like different processes. The Artificial Intelligence Fuzzy logic can also be used for prediction of machining responses. ANFIS can also be used for prediction of machining responses.
REFERENCES
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4. Yang and Chen (2001) attempted to determine optimal machining parameters for improving surface roughness performance of machined Al 6061 in end-milling operation, Ann CIRP , 1993 42(1):107–109. 5. Kadirgama- Optimization of surface roughness in aluminum alloys uing RSM and RBFN. J Mater Process Techno, 1995 48:291–297. 6. N.Deepthi, P.Sivaiah, K.Nagamani Optimization and analysis of parameters for multi-performance characteristics in drilling of Al6061 by using Taguchi grey relational analysis and ANOVA analysis, volume 1, issue 4, July 2013 7. A. Al-Refaie, L. Al-Durgham, and N. Bata-optimizing the proposes of an approach for Optimizing multiple responses in the Taguchi method using regression models and grey relational analysis. 8. S. R. Karnik, V. N. Gaitonde and J. P. Davim [12] - performs a comparative study of the Artificial Neural Network (ANN) and Response Surface Methodology (RSM) modeling approaches for predicting burr size in drilling 9. Ashok Kr. Mishra, Rakesh Sheokand and Dr. R K Srivastava-optimized the Tribological behavior of aluminum alloy Al6061 reinforced with silicon carbide particles (10% & 15%weight percentage of SiCp) fabricated by stir casting process was investigated. 10. Oktem H., Erzurumlu T. and Kurtaran H., 2005. Application of response surface methodology in the optimization of cutting conditions for surface roughness, Journal of Material Processing Technology, Vol. 170, No. 1-2, pp. 11-16.
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