Optimization of Process Parameter to Enhance the Impact Strength of Squeeze Cast Brass Alloy

MIT International Journal of Mechanical Engineering, Vol. 5, No. 1, January 2015, pp. 43-48 ISSN 2230-7680 © MIT Publications 43 Optimization of Pro...
Author: Catherine Woods
0 downloads 1 Views 1MB Size
MIT International Journal of Mechanical Engineering, Vol. 5, No. 1, January 2015, pp. 43-48 ISSN 2230-7680 © MIT Publications

43

Optimization of Process Parameter to Enhance the Impact Strength of Squeeze Cast Brass Alloy



Deepak Singh

Munish Chhabra

Vineet Tirth

Assistant Professor MIT, Moradabad (U.P.), India

Associate Professor MIT, Moradabad (U.P.), India

Professor & Director MIT, Moradabad (U.P.), India

ABSTRACT This paper reports a research in which an attempt has been made to optimize the squeeze casting parameters such as squeeze pressure, die temperature, pouring temperature and squeeze time for obtaining the maximum impact strength of brass alloy using Taguchi’s design of experimentation method. Signal to Noise ratio using higher – the - better method and Analysis of Variance were performed to analyse the resulted experimental data. Based on the experimental investigation the optimum conditions (such as die pressure: 120MPa, die temperature: 100oC, pouring temperature: 1000oC and squeeze time: 45 Sec) were obtained in order to get the maximum value of impact strength of squeeze cast brass alloy. Keywords: Squeeze Casting, Brass alloy, Impact strength.

INTRODUCTION Squeeze casting is a method of forming metal into shapes by using two dies that are squeezed together. Most casting techniques use two dies that are squeezed together before the metal is added but, in squeeze casting, the two are pushed together after the metal is added. This is done with liquid metal, and the upper die is only removed when the metal has cooled. By using this technique, the metal will typically come out stronger, with a better grain and less metallic shrinking. This commonly is done with magnesium, aluminium and their alloys, but many other metals can be used. According to Crouch [1], squeeze casting is now the most popular fabrication route for MMC artefacts. The annual 12±15% growth rate of MMCs in the automotive, aerospace, sport and leisure goods and other markets is a clear indication of better usage of advanced manufacturing routes such as squeeze casting. Generally, the SC-fabricated engineering components are one grained with excellent surface finish and have almost no porosity. They come in a variety of shapes and sizes. The mechanical properties of these parts are significantly improved over those of conventional castings and more sophisticated casting routes of pressure or gravity die-casting. According to Pennington [2], yield strength is improved by 10±15% and elongation and fatigue strength by as much as 50±80%. Dimensional accuracy is similar to those of die-casting; 0.25 mm in 100 mm to 0.6 mm in 500 mm. It is further claimed that SC-fabricated components have superior weld ability and heat treatability. In addition, since squeeze casting may be carried out without any feeding system, runners, gates, etc., and shrinkage compensating units, risers, and the yield is quite high with almost no scrap for recycling. Finally,

in contrast to forging, squeeze cast components are fabricated in a single action operation with lesser energy requirements. Most casting techniques involve the use of two dies, but squeeze casting uses the dies in a different way. The two casts normally are placed together and liquid metal is poured into the case. With a squeeze cast, a pool of liquid metal is placed in the bottom die and an upper die comes in and squeezes the metal into a shape. Pressure is being applied via the upper die, so this is not strictly casting, as it adds forging to create a hybrid technique. Only liquid metal can be used in this application. While materials such as plastic can melt at high temperatures, this technique will not be suitable to cast plastic. After the upper die is set, workers wait until the metal is completely cool. Once cool, the upper die will be released and the required shape will be cast into solid metal. Many researchers have performed research in squeeze casting process [3]. Among all the parameters, the effect of pressure on the mechanical properties of castings has been studied extensively [4]. Most of the research has been carried out in casting of aluminium alloy [5, 6] and magnesium alloy [7, 8]. Very little work has been reported in copper alloy [9]. Therefore an attempt has been made to carry out research work in investigating the effect of important parameters of squeeze casting on the quality characteristic of brass alloy. In present work squeeze casting parameters such as squeeze pressure, die temperature, pouring temperature and squeeze time were optimized for obtaining the maximum impact strength of brass alloy using Taguchi’s design of experimentation method.

MIT International Journal of Mechanical Engineering, Vol. 5, No. 1, January 2015, pp. 43-48 ISSN 2230-7680 © MIT Publications

METHODOLOGY Taguchi’s Design of experimentation methodology was used to perform this research work.

1. Taguchi method Taguchi method [10] is a set of experimentation techniques based on statistical principles and utilizing engineering knowledge developed by Japanese quality expert, Dr. Genichi Taguchi. He developed a method based on “Orthogonal Array” experiments which gives much reduced “variance” for the experiment with “optimum settings” of control parameters. Thus the marriage of Design of Experiments with optimization of control parameters to obtain BEST results is achieved in the Taguchi Method. Orthogonal Arrays (OA) provide a set of well balanced (minimum) experiments and Taguchi’s Signal-to-Noise ratios (S/N), which are log functions of desired output, serve as objective functions for optimization, help in data analysis and prediction of optimum results. The objective of Taguchi’s DOE technique is to simplify the experimentations for investigating the optimum process parameters of a process. The use of lesser number of experiments due to Taguchi’s DOE technique results in reduction in time and costs involved in the experimentation. Taguchi Method treats optimization problems in two categories one is static problem and second is dynamic problem. Generally, a process to be optimized has several control factors which directly decide the target or desired value of the output. The optimization then involves determining the best control factor levels so that the output is at the target value. Such a problem is called as a “Static Problem”. If the product to be optimized has a signal input that directly decides the output, the optimization involves determining the best control factor levels so that the “input signal/output” ratio is closest to the desired relationship. Such a problem is called as a “Dynamic Problem”. In present research work, Taguchi method based on static problem was used to investigate the effect of squeeze casting parameters on quality characteristics of brass casting. The basic procedure of the Taguchi method is as follows [11]: 1. Identify the performance characteristic to be observed. 2. Identify important noise factors and their ranges. 3. Identify the control factors and their levels. 4. Construct inner array and outer array. The inner array is a designed experiment using the control factors, and the outer array using the noise factors. Table 1: Factors and their levels selected for research work Control factors

Designation

Level 1

Die Pressure (MPa)

A

80

120

160

Die Temperature ( C)

B

50

100

150

Pouring Temperature (oC)

C

900

950

1000

Squeeze Time (seconds)

D

15

30

45

o

Level 2 Level 3

44

6. Conduct the designed experiment. If the inner array is made up of m rows and the outer array n rows, then m rows can obtain each n performance characteristics through actual experiments. These n experimental data are used to calculate the S/N ratio for each row of the inner array. 7. Analyze the data and determine optimal levels for each control factor. The optimal parameter settings are then determined by analyzing the S/N ratio data. 8. Perform the verification experiment. A verification experiment is carried out to prove the analysis results.

2. Orthogonal arrays Orthogonal arrays are special arrangements of factor settings widely used in the design of experiments to gain maximum amount of information by using the least number of experiments [12]. Types of orthogonal tables can be identified as Lx (Zy), where y is the number of input parameters, Z is the number of level settings and x is the number of experiments that must be run to complete the matrix. Before selecting a particular OA to be used for conducting the experiments, the following two points must be considered [13].

• The number of parameters and interaction of interest.



• The number of levels for the parameters of interest.

The non-linear behaviour, if exist, among the process parameters can only be studied if more than two levels of the parameters are used. Therefore, each parameter was analyzed at three levels. The number of process parameters and their level values are already decided and are given in Table 1. Based on the selected number of parameters and their level, Taguchi’s method proposed L9 OA. This OA has four columns and nine experiment-runs (Table 2). The four parameters at three levels were assigned to these four columns as shown in Table 3. Table 2: L9 Orthogonal array Run No.

Die Pressure (MPa)

Die Temperature (oC)

Pouring Temperature (oC)

Squeeze Time (sec)

1

1

1

1

1

2

1

2

2

2

3

1

3

3

3

4

2

1

2

3

5

2

2

3

1

6

2

3

1

2

7

3

1

3

2

8

3

2

1

3

9

3

3

2

1

3. Signal to Noise Ratio Analysis Taguchi’s emphasis on maximizing deviation from target resulted to develop measures of the process output that incorporate both the location of the output as well as the variation. These measures are called signal to noise ratios. The signal to noise ratio provides a measure of the impact of noise factors on performance. The

MIT International Journal of Mechanical Engineering, Vol. 5, No. 1, January 2015, pp. 43-48 ISSN 2230-7680 © MIT Publications

larger the S/N ratio, more robust the product is against the noise. The formula for finding larger-the better S/N ratio is as follows:   1  ∑  2    Yi   = −10log    (1) n    



S N ( LTB )

Where,

n = number of trials

Y = The value of the impact strength obtained in i the respective trial

4. Data Analysis Using ANOVA The analysis of variance (ANOVA) was used to investigate which parameters significantly affected the quality characteristic and to determine the percentage contribution of the parameters at 95% confidence level. The F ratio value named Fisher test was used to see which process parameters have a significant effect. The equations and terms related to ANOVA such as sum of total (T), correction factor, and total sum of square (SST), sum of square due to parameter (SSA), variance (V) were used to analyze the experimental results. The details of ANOVA terms presented in literature [14].

Fig. 1: Hydraulic Press

EXPERIMENTATION A 50 ton Hydraulic press shown in Fig.1 was employed for the application of pressure over the molten metal during solidification. o A muffel electrical heater of capacity 400 C was used to preheat the die shown in Fig. 2. The resistance furnace shown in Fig. 3 was used to melt the brass alloy. After degassing the melt, a metered quantity of molten metal alloy was poured into the preheated die cavity. The squeeze pressure was directly applied on the molten alloy through the punch fitted on to the hydraulic press, and the pressure was maintained for predetermined time. The punch was then withdrawn and the casting was separated from the die. Nine experiments were conducted by varying the process parameters at selected levels according to the L9 orthogonal array. After producing the castings, samples of obtaining impact strength were prepared according to standard ASTM A370. The samples prepared for the impact testing are presented in Fig. 4. This standard size is 10mm×10mm×55mm. The V-notch is made in the middle (27.5 mm from any side) of the work piece of 2 mm depth along the length. The test consists of breaking a standard test piece with one flow from a swinging hammer. The test piece is V-notched in the middle and supported at each end. The test piece should be struck by the hammer in the plane of symmetry of the notch and on the side opposite the notch. Impact strength of the material is the energy absorbed per unit volume during the fracture of the material. It is expressed in joules or kilogram force meter. In present research work impact strength was obtained using Impact Testing Machine Make-Enkey Enterprises, Least count-2 joules, striker type-Rectangular V-shape) (shown in Fig. 5 & Fig.6).

Fig. 2: Muffel Furnance used for die heating

Fig. 3: Resistance furnance used for melting brass

45

MIT International Journal of Mechanical Engineering, Vol. 5, No. 1, January 2015, pp. 43-48 ISSN 2230-7680 © MIT Publications

46

Fig. 6: Closed view of impact testing

RESULTS AND DISCUSSIONS Fig. 4: Samples prepared for impact testing

1. Results of S/N Ratio Analysis The S/N ratio values for hardness were calculated using the equation number 1 and the computed values are presented in Table 3. The mean values of computed S/N ratio of impact strength for each factor at levels 1, 2, and 3 are presented in Table 4. The mean of the S/N ratio at each level of various parameters are used to draw the main effect plots. The main effect plots of impact strength are shown in Fig. 7 (a - d). It is clear from the figures that the maximum values of computed S/N ratio are observed at the levels of the factors A2, B2, C3 and D3. Experimental results for impact strength and corresponding S/N ratios considered as the optimum levels for obtaining maximum values of impact strength of brass castings using squeeze casting process.

Fig. 5: Impact testing machine

Table 3: Experimental results for impact strength and corresponding S/N ratios Experiment number

Die Pressure (MPa)

Die Temp. (oC)

Pouring Temp. (oC)

1

80

50

900

Squeeze Impact Strength Time Mean S/N (sec) (J) ratio (dB) 15

36

34.13

2

80

100

950

30

154

46.76

3

80

150

1000

45

166

47.41

4

120

50

950

45

92

42.28

5

120

100

1000

15

164

47.30

6

120

150

900

30

88

41.89

7

160

50

1000

30

46

36.26

8

160

100

900

45

50

36.98

9

160

150

950

15

40

35.05

overall mean of impact strength = 92.88 J overall mean of corresponding S/N value =40.89dB

The optimum conditions within the selected parameter values were found as die pressure (120MPa), die temperature (100oC), pouring temperature (1000oC) and squeeze time (45 Sec).

2. Results of ANOVA The equations and terms related to ANOVA as presented in section were used to analyze the experimental results. Table 5 shows the results of ANOVA based on S/N ration computed for impact strength. From the F distribution table (reference) in relation to present case, F0.05 at 95% confidence level, it is found that the all parameters of squeeze casting selected in present research have significant on the impact strength. Among all parameters, die pressure was the most significant squeeze casting parameter due to the highest percentage contribution (44.31%). Fig. 8 shows that die temperature having next highest percentage contribution equal to 24.23%. Percentage contribution of pouring temperature and squeeze time was 23.05% and 8.30% respectively.

MIT International Journal of Mechanical Engineering, Vol. 5, No. 1, January 2015, pp. 43-48 ISSN 2230-7680 © MIT Publications

47

Table 4: Response Table for S/N ratio for impact strength Level

Die Pressure

Die Temperature

Pouring Temperature

Squeeze Time

L1

42.76

37.55

37.66

38.82

L2

43.82

43.68

41.36

41.63

L3

36.09

41.45

43.65

42.22

7.73

6.13

5.99

3.4

1

2

3

4

Δ (max–min) Rank

L1,L2 and L3 represent average S/N values at levels 1,2 and 3 respectively. *The optimum levels of the control factors are presented in bold.

Fig. 7 (a-d): Main effect plots based on S/N ratio for impact strength

Table 5: Result of ANOVA for Impact Strength Sum of squares (SS)

Variance (V)

Pure sum of squares (SS’)

Contribution (P %)

Rank

2

105.3

52.62

105.26

44.31

1

Die Temp.

2

57.61

28.805

57.57

24.23

2

C

Pouring Temp.

2

54.8

27.4

54.76

23.05

3

D

Squeeze Time

2

19.77

9.885

19.73

8.30

4

2

0.04

0.02

Symbol

Control Factor

A

Die Pressure

B

Error Total

l

DOF

10

0.1 100

MIT International Journal of Mechanical Engineering, Vol. 5, No. 1, January 2015, pp. 43-48 ISSN 2230-7680 © MIT Publications

48

REFERENCES 1. I.G. Crouch, “Aluminium squeeze casting technology a European researches viewpoint”, Australian Conference on Materials for Industrial Development, Christchurch, New Zealand, pp. 24-26 August 1987. 2. J.N. Pennington, “Squeeze-cast parts approach performance of forgings”, Mod. Met. 44 (1) pp. 52, 54-56, 58, 60,1988. 3. M.R. Ghomashchi, A. Vikhrov, “Squeeze casting: An overview”, Journal of Materials Processing Technology 101, pp.1-9, 2000. 4. L.J. Yang, “The effect of casting temperature on the properties of squeeze cast aluminium and zinc alloys”, Journal of Materials Processing Technology 140, pp. 391–396, 2003. Fig. 8: Percentage contribution of factors

CONCLUSIONs The conclusions drawn from this study are summarized as follows:

5. Maleki, A, Shafyei, Niroumand, “Effects of squeeze casting parameters on the microstructure of LM13 alloy” J. Mater. Process. Technol. 209, pp. 3790–3797, 2009. 6. Senthil, K.S. Amirthagadeswaran, “Optimization of squeeze casting parameters for non symmetrical AC2A aluminium alloy castings through Taguchi method” Journal of Mechanical Science and Technology 26 (4), pp. 1141-1147, 2012. 7. Yong, M.S.Clegg, A.J., “Process optimization for a squeeze cast magnesium alloy”, J. Mater. Process. Technol 145, pp.134–141, 2004.

Among the four factor and three levels tested, it was concluded that the die pressure had the most significant effect on the impact strength and the die temperature had the next most significant effect. The effects of pouring temperature and squeeze time are less important when compared to the other factors.

8. Goh, C.S.Soh, K.S., Oon, P.H., Chua B.W., “Effect of squeeze casting parameters on the mechanical properties of AZ91–Ca Mg alloys”. Mater. Des, 31, pp. S50–S53,2010.

Among all parameters, die pressure was the most significant squeeze casting parameter due to the highest percentage contribution (44.31%). Die Temperature having next highest percentage contribution equal to 24.23%. Percentage contribution of pouring temperature and squeeze time was 23.05% and 8.3% respectively.

10. Muhammad Hisyam Lee, Izman Sudin, Goh Eng Ken, Azami Zaharim, “Parameters Optimization of Rotary Ultrasonic Machining of Glass Lens for Surface Roughness Using Statistical Taguchi’s Experimental Design”, Proceedings of the 13th WSEAS International Conference on Applied Mathematics (MATH’08), 2008.

The optimum conditions within the selected parameter values were found as the second level of die pressure (120MPa), second level of die temperature (100oC), third level of pouring temperature (1000oC) and third level of squeeze time (45 Sec).

ACKNOWLEDGEMENTS Authors gratefully acknowledge the financial support provided by Moradabad Educational Trust for conducting this research work.

9. P. Vijian, V.P. Arunachalam, “Experimental study of squeeze casting of gunmetal”, Journal of Materials Processing Technology 170, pp. 32–36, 2005.

11. A. Sreenathbabu, K.P. Karunakaran and C. Amarnath, “Statistical process design for hybrid adaptive layer manufacturing”, Rapid Prototyping Journal, Volume 11 Number 4, pp. 235–248, 2005. 12. G. Z. Yin,“Orthogonal Design for Process Optimization and Its Application in Plasma Etching” Solid State Technology, pp. 127–132,1987. 13. Sudhir Kumar, Pradeep Kumar, H.S. Shan,“Optimization of tensile properties of evaporative pattern casting process through Taguchi’s method” Journal of materials processing technology 204, pp. 59–69,2008. 14. Chhabra and Singh,“Obtaining desired surface roughness of castings” Rapid Prototyping Journal Volume 18, Number 6 pp. 458–47, 2012.

Suggest Documents