# Evaluation of Copper-Based Alloy (C93200) Composites Reinforced with Marble Dust Developed by Stir Casting under Vacuum Environment

^{1}

^{2}

^{3}

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^{5}

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## Abstract

**:**

## 1. Introduction

_{2}O

_{3}, SiC, MgO, and whiskers/fibers are mostly used as a reinforcement to improve mechanical and tribological properties through the proper selection of several parameters, such as the volume/weight percentage, shape and size of the reinforcement particles, and the dispersion of reinforcement in the matrix phase [4]. Poddar et al. [5] studied AZ91D composites reinforced with silicon carbide (SiC) particulates and they observed that the presence of particulates increases the yield strength and Young’s modulus because tiny particle reinforcement results in the improvement of the elastic modulus and yield strength. The mechanism of composite deformation is the load transfer from the matrix to reinforcement and a good bonding between matrix and reinforcement provides better load transfer and an enhancement of the properties. The effect of the particle size on the physical and mechanical properties of SiCp/5210 Al metal matrix composite is studied by Dong et al. [1], who explain that the bending strength of the composites increases with the decreasing particle size.

## 2. Experimental Details

#### 2.1. Materials and Fabrication Details

_{2}, and 3 wt.% other oxides, such as Al

_{2}O

_{3}, Fe

_{2}O

_{3}, SO

_{3}, K

_{2}O, and N

_{2}O. In Table 1 we present the designation and chemical composition of the studied C93200 copper alloy considered as the material matrix. The marble dust added C93200 copper alloy composites for bearing materials were fabricated by using liquid metal stir casting techniques in an induction furnace in a vacuum environment. The furnace consisted of a heating unit, graphite crucible, and a stirrer. Both the base alloy (i.e., C93200 copper alloy) together with the reinforcement (i.e., marble dust) was submitted to preheating (up to 400 °C) separately before starting the fabrication process. A graphite crucible was placed into the induction furnace to heat the base alloy. When the liquid temperature (around 1100 °C) was attained, a specific quantity of preheated marble dust was slowly added into the molten metal by a mechanical, continuous stirrer. The stirrer endured a constant rotational speed of 300 rpm for 2 min. that allowed the homogeneous mixing of filler material with the base (matrix) alloy. The wettability performances were ensured by incorporating, through the stirring process, a specific amount of magnesium (2 wt.%). Next, the mixture of base alloy and marble particles were poured into the graphite mould, which measured 100 × 65 × 10 mm

^{3}, followed by 15 min. cooling in air. Thus, five different weight percentages of marble particulate filled alloy composites were prepared. Thereafter, the casted specimens were polished and used for physical, mechanical, and wear characterization.

#### 2.2. Mechanical and Physical Characterization

_{f}) of fabricated composites can be calculated as following Equation (2).

^{3}(cross-head speed of 2 mm/min) as per ASTM E9-09 standards was obtained on the Universal Testing Machine (UTM). The three-point flexural test of the specified composites was carried out on UTM-Instron (Norwood, MA, USA) conforming to ASTM-E290.

#### 2.3. Wear Test

## 3. The PSI Technique

- Phase I
- Determination of alternatives and criterions
- In the initial state, we search for different numbers of alternatives and criteria for a specific MCDM problem.

- Phase II
- Ordering the alternatives using Preference selection index (PSI) technique
- In this phase, the common steps are as follows:
- Step 1
- Building the decision matrix: At this stage, in the PSI technique, we proceed to create a decision matrix that is derived from the alternatives and criteria imposed by the problem. If m represents the number of alternatives and n represents the numbers of criteria, a decision matrix of order m × n can be assembled and ascribed numerically by Equation (4).$${T}_{m\times n}=\begin{array}{cc}& \begin{array}{cccc}{H}_{1}& {H}_{2}& \cdots & {H}_{n}\end{array}\\ \begin{array}{c}{B}_{1}\\ {B}_{2}\\ \vdots \\ {B}_{m}\end{array}& \left[\begin{array}{cccc}{T}_{11}& {T}_{12}& \cdots & {T}_{1n}\\ {T}_{21}& {T}_{22}& \cdots & {T}_{2n}\\ \vdots & \vdots & \ddots & \vdots \\ {T}_{m1}& {T}_{m2}& \cdots & {T}_{mn}\end{array}\right]\end{array}$$
- Step 2
- Decision matrix normalization: After constructing the decision matrix, their values were normalized in the range of [0,1] as described by Equation (5).$${t}_{ij}=\frac{{T}_{ij}}{{T}_{j}^{\mathrm{max}}},\text{}\mathrm{for}\text{}\mathrm{larger}\text{}\mathrm{is}\text{}\mathrm{good},\text{}\mathrm{and}\text{}{t}_{ij}=\frac{{T}_{j}^{\mathrm{min}}}{{T}_{ij}},\text{}\mathrm{for}\text{}\mathrm{smaller}\text{}\mathrm{is}\text{}\mathrm{good}$$
- Step 3
- Determination of preference variation value: Once the decision matrix was normalized, we established the values of preference variation (Ψ
_{j}) applying a separate criterion that is solved by Equation (6).$${\mathsf{\Psi}}_{j}={\displaystyle \sum _{i=1}^{m}{\left[{t}_{ij}-\frac{1}{m}{\displaystyle \sum _{i=1}^{m}{t}_{ij}}\right]}^{2}}$$ - Step 4
- Determination of deviation for each value from the preference variation: The values of deviation of the preference variation (Ω
_{j}) were determined applying Equation (7).$${\mathsf{\Omega}}_{j}=1-{\mathsf{\Psi}}_{j}$$ - Step 5
- Calculations of the overall preference value: Once the deviation associated with the preference variation was determined is was possible to calculate the overall preference value (Φ
_{j}) by using Equation (8).$${\mathsf{\Phi}}_{j}=\frac{{\mathsf{\Omega}}_{j}}{{\displaystyle \sum _{j=1}^{n}{\mathsf{\Omega}}_{j}}}$$The sum up of the overall preference values detected from the criterions can be described as i.e., $\sum _{j=1}^{n}{\mathsf{\Phi}}_{j}}=1$ - Step 6
- Determination of the values for preference selection index: Once the overall preference values (Φ
_{j}) were computed, the preference selection index (Π_{i}) associated to each alternative was calculated using Equation (9).$${\mathsf{\Pi}}_{i}={\displaystyle \sum _{j=1}^{n}\left({t}_{ij}\times {\mathsf{\Phi}}_{j}\right)}$$Finally, the alternatives were ranked in such way that the alternative with the highest Π_{i}value was determined to be the best.

## 4. Results and Discussion

#### 4.1. Effect of Marble Dust Reinforcement on Criterion-1 and Criterion-2

#### 4.2. Effect of Marble Dust Reinforcement on Criterion-3 and Criterion-4

#### 4.3. Effect of Marble Dust Reinforcement on Criterion-5 and Criterion-6

#### 4.4. Effect of Marble Dust Reinforcement on Criterion-7 and Criterion-8

_{2}B

_{2}O

_{5}whisker reinforced 6061 Al matrix composite, and reported a similar observation in their investigation. Similarly, when composites reinforced with 4.5 wt.% (A-4) marble content were run at a sliding velocity of 2.61 m/s, less wear loss was observed, but when the same composite was run at a higher sliding velocity (4.69 m/s) a slightly higher wear loss was observed. The decrement in the wear loss may be due to the incorporation of hard particulates and good bonding strength at the interface of matrix alloy with the hard particulates, which may promote a higher hardness features and good wear resistance of composites.

#### 4.5. Effect of Marble Dust Reinforcement on Criterion-9 and Criterion-10

#### 4.6. Effect of Marble Dust Reinforcement on Criterion-11 and Criterion-12

#### 4.7. Effect of Marble Dust Reinforcement on Criterion-13 and Criterion-14

#### 4.8. Identification of the Final Rank with Respect to Various Criteria

_{j}) from the preference variation value (Ψ

_{j}) together with the overall preference value (Φ

_{j}), and preference selection index value (Π

_{j}) was computed by using Equations (6)–(9) (listed in Table 5) and the alternative with the highest Π

_{j}value (Table 6) were considered as the best alternative. It was examined that the PSI (Π

_{j}) obtained from the A-4 data reached the maximum (0.9607). The following detected values are represented by the A-3 (0.9008), A-5 (0.8467) and A-2 (0.8326), respectively. The material manufactured for bearing, denoted as A-1, presents the least favored condition that has an index of 0.8059. The final optimal sequence of investigated composites alternative raking is ordered as A-4 (Rank-1) > A-3 (Rank-2) > A-5 (Rank-3) > A-2 (Rank-4) > A-1 (Rank-5).

## 5. Surface Morphology

## 6. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

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**Figure 3.**Variation of criterion-1 together with criterion-2 in respect to the values of alternatives.

**Figure 4.**Variation of criterion-3 together with criterion-4 in respect to the values of alternatives.

**Figure 6.**Evolution of criterion-7 together with criterion-8 in respect to the values of alternatives.

**Figure 9.**Evolution of criterion-13 together with criterion-14 in respect to the values of alternatives.

**Figure 10.**Representative micrographs of selected composite after wear test under 15-N normal load, 4.69 m/s sliding velocity and 1500 m sliding distance of (

**a**) A-1, 0 wt.% MD; (

**b**) A-2, 1.5 wt.% MD; (

**c**) A-3, 3 wt.% MD; (

**d**) A-4, 4.5 wt.% MD; (

**e**) A-5, 6 wt.% MD reinforced composites.

Sample Designateon | Composition |
---|---|

A-1 | C93200 copper alloy + 0 wt.% Marble dust |

A-2 | C93200 copper alloy + 1.5 wt.% Marble dust |

A-3 | C93200 copper alloy + 3 wt.% Marble dust |

A-4 | C93200 copper alloy + 4.5 wt.% Marble dust |

A-5 | C93200 copper alloy + 6 wt.% Marble dust |

Criterions | Test Conditions | Performance Implications | |
---|---|---|---|

C-1: | Density (gm/cc) | Archimedes method | Min |

C-2: | Void content | - | Min |

C-3: | Hardness (Hv) | ASTM E-92 | Max |

C-4: | Compressive strength (MPa) | ASTM E 209-00 | Max |

C-5: | Tensile strength (MPa) | ASTM E 209-00 | Max |

C-6: | Flexural strength (MPa) | ASTM E 209-00 | Max |

C-7: | Wear loss (grams) | 15 N ^{1}; 2.61 m/s ^{2} | Min |

C-8: | Wear loss (grams) | 15 N ^{1}; 4.69 m/s ^{2} | Min |

C-9: | Wear loss (grams) | 10 N ^{1}; 3.66 m/s ^{2} | Min |

C-10: | Wear loss (grams) | 20 N ^{1}; 3.66 m/s ^{2} | Min |

C-11: | COF (µ) | 15 N ^{1}; 2.61 m/s ^{2} | Min |

C-12: | COF (µ) | 15 N ^{1}; 4.69 m/s ^{2} | Min |

C-13: | COF (µ) | 10 N ^{1}; 3.66 m/s ^{2} | Min |

C-14: | COF (µ) | 10 N ^{1}; 3.66 m/s ^{2} | Min |

^{1}Normal load;

^{2}Sliding velocity.

Composite | C-1 | C-2 | C-3 | C-4 | C-5 | C-6 | C-7 | C-8 | C-9 | C-10 | C-11 | C-12 | C-13 | C-14 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|

A-1 | 8.84 | 0.785 | 115.49 | 36.68 | 240.71 | 301.02 | 0.180 | 0.329 | 0.143 | 0.200 | 0.21 | 0.22 | 0.20 | 0.19 |

A-2 | 8.55 | 0.696 | 116.73 | 40.47 | 245.67 | 326.60 | 0.153 | 0.232 | 0.125 | 0.180 | 0.22 | 0.23 | 0.21 | 0.21 |

A-3 | 8.26 | 0.601 | 121.51 | 40.60 | 259.73 | 406.03 | 0.115 | 0.133 | 0.113 | 0.153 | 0.22 | 0.22 | 0.23 | 0.21 |

A-4 | 8.00 | 0.497 | 128.97 | 39.32 | 278.99 | 413.34 | 0.072 | 0.092 | 0.100 | 0.143 | 0.23 | 0.23 | 0.24 | 0.22 |

A-5 | 7.63 | 1.548 | 120.94 | 38.32 | 228.97 | 402.44 | 0.130 | 0.183 | 0.119 | 0.168 | 0.21 | 0.22 | 0.22 | 0.21 |

Composite | C-1 | C-2 | C-3 | C-4 | C-5 | C-6 | C-7 | C-8 | C-9 | C-10 | C-11 | C-12 | C-13 | C-14 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|

A-1 | 0.863 | 0.633 | 0.895 | 0.903 | 0.863 | 0.728 | 0.402 | 0.280 | 0.699 | 0.715 | 1.000 | 1.000 | 1.000 | 1.000 |

A-2 | 0.892 | 0.714 | 0.905 | 0.997 | 0.881 | 0.790 | 0.473 | 0.397 | 0.800 | 0.794 | 0.955 | 0.957 | 0.952 | 0.905 |

A-3 | 0.924 | 0.827 | 0.942 | 1.000 | 0.931 | 0.982 | 0.629 | 0.692 | 0.885 | 0.935 | 0.955 | 1.000 | 0.870 | 0.905 |

A-4 | 0.954 | 1.000 | 1.000 | 0.968 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 0.913 | 0.957 | 0.833 | 0.864 |

A-5 | 1.000 | 0.321 | 0.938 | 0.944 | 0.821 | 0.974 | 0.556 | 0.503 | 0.840 | 0.851 | 1.000 | 1.000 | 0.909 | 0.905 |

**Table 5.**The preference variation (Ψ

_{j}), deviation related to the preference variation (Ω

_{j}), and the overall preference value (Φ

_{j}).

Composite | C-1 | C-2 | C-3 | C-4 | C-5 | C-6 | C-7 | C-8 | C-9 | C-10 | C-11 | C-12 | C-13 | C-14 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|

Ψ_{j} | 0.863 | 0.633 | 0.895 | 0.903 | 0.863 | 0.728 | 0.402 | 0.280 | 0.699 | 0.715 | 1.000 | 1.000 | 1.000 | 1.000 |

Ω_{j} | 0.892 | 0.714 | 0.905 | 0.997 | 0.881 | 0.790 | 0.473 | 0.397 | 0.800 | 0.794 | 0.955 | 0.957 | 0.952 | 0.905 |

Φ_{j} | 0.924 | 0.827 | 0.942 | 1.000 | 0.931 | 0.982 | 0.629 | 0.692 | 0.885 | 0.935 | 0.955 | 1.000 | 0.870 | 0.905 |

Alternative | Preference Selection Index (Π_{j}) | Ranking |
---|---|---|

A-1 | 0.8059 | 5 |

A-2 | 0.8326 | 4 |

A-3 | 0.9008 | 2 |

A-4 | 0.9607 | 1 |

A-5 | 0.8467 | 3 |

Alternatives | Ranking | ||
---|---|---|---|

Proposed | TOPSIS | GRA | |

A-1 | 5 | 4 | 5 |

A-2 | 4 | 3 | 3 |

A-3 | 2 | 2 | 2 |

A-4 | 1 | 1 | 1 |

A-5 | 3 | 5 | 4 |

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## Share and Cite

**MDPI and ACS Style**

Rajak, S.K.; Aherwar, A.; Unune, D.R.; Mia, M.; Pruncu, C.I.
Evaluation of Copper-Based Alloy (C93200) Composites Reinforced with Marble Dust Developed by Stir Casting under Vacuum Environment. *Materials* **2019**, *12*, 1574.
https://doi.org/10.3390/ma12101574

**AMA Style**

Rajak SK, Aherwar A, Unune DR, Mia M, Pruncu CI.
Evaluation of Copper-Based Alloy (C93200) Composites Reinforced with Marble Dust Developed by Stir Casting under Vacuum Environment. *Materials*. 2019; 12(10):1574.
https://doi.org/10.3390/ma12101574

**Chicago/Turabian Style**

Rajak, Santosh Kumar, Amit Aherwar, Deepak Rajendra Unune, Mozammel Mia, and Catalin I. Pruncu.
2019. "Evaluation of Copper-Based Alloy (C93200) Composites Reinforced with Marble Dust Developed by Stir Casting under Vacuum Environment" *Materials* 12, no. 10: 1574.
https://doi.org/10.3390/ma12101574