# Performance Assessment of Minimum Quantity Castor-Palm Oil Mixtures in Hard-Milling Operation

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## Abstract

**:**

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Machining Operation

_{a}) was calculated using a 3D Profilometer (Taylor Hobson, India). In this study, surface roughness was calculated from five different points, and the mean of these values was considered as average surface roughness. A Kistler-9257-BA dynamometer (Kistler Instrument, India) was attached to the workpiece. LabVIEW

^{®}software was used for capturing the cutting force data-base. Here, an amplifier was used to amplify the signals received from the dynamometer at a sampling frequency of 7000 Hz. The machining forces subjected by the workpiece were divided into three components. The resultant of these forces were calculated by applying Equation (1). Subsequently, the specific energy was calculated by using Equation (2). Finally, a scanning electron microscope (SEM) (Carl ZEISS, India) was utilized to observe the flank wear (V

_{B}) profile of the end-mill. Figure 1 shows the overall framework of the present study.

#### 2.2. TOPSIS Based Ranking Strategy with Entropy Weight

- $\mathcal{A}\text{}\left(coolection\text{}of\text{}alternatives\right)=\left\{{A}_{i}:i\in \mathcal{I}\right\}$ where $\mathcal{I}=\left\{1,2,\dots ,m\right\}$
- $\mathcal{C}\text{}\left(Set\text{}of\text{}Criteria\right)=\left\{{\mathcal{C}}_{j}:j\in \mathcal{T}\right\}$ where $\mathcal{T}=\left\{1,2,\dots ,n\right\}$
- The weight vector: $\left({w}_{1},\text{}{w}_{2},\dots ,{w}_{n}\right)$ where, ${w}_{j}\text{}\ge 0\text{}\forall \text{}j$ and ${{\displaystyle \sum}}_{j=1}^{n}{w}_{j}$ = 1
- The decision matrix (D) is shown in Equation (3):$$\text{}{\mathcal{C}}_{1}\text{}{\mathcal{C}}_{2}\text{}\dots \text{}{\mathcal{C}}_{n}\phantom{\rule{0ex}{0ex}}\mathbf{D}\text{}=\text{}\begin{array}{c}{A}_{1}\\ {A}_{2}\\ \vdots \\ {A}_{m}\end{array}\left(\begin{array}{cccc}{a}_{11}& {a}_{12}& \cdots & {a}_{1n}\\ {a}_{21}& {a}_{22}& \cdots & {a}_{2n}\\ \vdots & \vdots & \cdots & \vdots \\ {a}_{m1}& {a}_{m2}& \cdots & {a}_{mn}\end{array}\right)$$
**Step 1**: Normalization of the D using Equation (4):$${r}_{ij}=\text{}\frac{{a}_{ij}}{\sqrt{{{\displaystyle \sum}}_{i=1}^{m}{a}_{ij}^{2}}}$$**Step 2**: An essential component of MCDM methods is determining the weight of each criterion. Previous literature has suggested different techniques to generate criteria weights. They can be categorized into the following groups:- (a)
**Subjective approach:**In this approach, the weight of any criteria is defined based on the decision maker’s preferences.- (b)
**Objective approach:**in this approach, weight is directly calculated from the decision matrix.

In the present study, an entropy objective approach has been applied to determine the weightage of the machining responses. The idea of entropy is utilized extensively to calculate the uncertainty related to the information [20,21,22]. The entropy-based weight can be determined from the normalized decision matrix by applying the following steps: - Calculation of entropy by applying Equation (5):$${E}_{j}=\text{}-\frac{1}{\mathrm{ln}n}{\displaystyle \sum}_{ij}^{m}{r}_{ij}\mathrm{ln}{r}_{ij}\text{}\forall \text{}j$$
- The diversification strength of the information provided by the outcome under criterion $j$ is calculated by Equation (6):$$\text{}{d}_{j}=1-{E}_{j}\text{}\forall \text{}j$$
- When the decision-maker has no extra preference information over the criteria, the principle of insufficient reason infers the best weights of the criteria are given in Equation (7):$${w}_{j}=\frac{{d}_{j}}{{{\displaystyle \sum}}_{j=1}^{n}{d}_{j}}\text{}\forall \text{}j$$

**Step 3**: Calculation of PIS and NIS (Equations (9) and (10))

**Step 4:**The separation measure for the alternative ${A}_{i}$ from PIS is computed by using Equation (11):

**Step 5**: Computation of the relative closeness coefficient (RC

_{i}) to the ideal solutions by applying Equation (13):

**Step 6**: Finally, alternatives are ranked with the principle that the best alternative has a maximum RC

_{i}value.

## 3. Results and Discussion

#### 3.1. Variation of Machining Responses with the Castor-Palm Ratio

_{a}for different castor-palm mixtures were as follows: castor-palm oil mixture (1:0.5), 0.375 µm; castor-palm oil mixture (1:1), 0.341 µm; castor-palm oil mixture (1:1.5), 0.389 µm; castor-palm oil mixture (1:2), 0.322 µm; castor-palm oil mixture (1:2.5), 0.361 µm; castor-palm oil mixture (1:3), 0.421 µm.

^{2}), whereas the specific cutting energy of the castor-palm oil mixture decreases to varying degrees relative to the mixture volume of castor and palm oil. The specific cutting energy of castor-palm (1:2.5) was least (0.3526 N/mm

^{2}).

#### 3.2. Result of the Proposed MCDM Model

_{a}, E

_{sp}, and V

_{B}are considered. The calculated weight factor of R

_{a}, E

_{sp}, and V

_{B}was found to be 0.3329, 0.3336, and 0.3335, respectively. Furthermore, the most suitable volume ratio of castor-palm oil mixture was calculated by the TOPSIS approach. From Figure 3, it is apparent that the 4th run obtains the first rank.

#### 3.3. Molecular Structure of Green Mixtures

^{−}) presented in the vegetable oil. Due to strong attraction and high adsorption energy, the mixture of castor-palm oil is easily adsorbed on the metal surface, thus developing a strong lubrication film. As the proportion of castor oil decreased in the mixture, the -OH groups in the mixture became nearly equal to that of saturated (palmitic acid), mono-saturated (oleic acid), and polyunsaturated (Linoleic acid) fatty acid. Due to the high amount of cutting temperature, esterification took place between the -OH groups and fatty acids, hence offering efficient lubrication performance. Once the proportion of castor oil in the mixture was minimal (1:3), insufficient ricinoleic acid molecules could persuade esterification. Due to high palmitic, oleic, and linoleic acid content on the mixture, the oil film density reduced and could not provide desirable lubrication performance.

#### 3.4. Viscosity of Green Mixtures

_{sp}, and V

_{B}. This outcome may be associated with the low-heat transfer capacity of the castor-palm mixture. From Figure 4b, it was also evident that the castor-palm oil mixture (1:3) showed the maximum cutting temperature. As the castor-palm volume ratio varied from 1:0.5–1:2.5, the cutting temperature was comparatively low. After 1:2.5, the cutting temperature rose significantly. In actuality, lubrication film’s desorption phenomenon took place under a higher temperature; thus, lubricants can’t provide effective lubrication [32,33].

#### 3.5. A Comparative Study

## 4. Conclusions

_{a}, E

_{sp}, and V

_{B}). The authors subsequently applied Shannon’s entropy coupled TOPSIS technique for selecting the preferable volume ratio of castor-palm oil mixtures. To this end, a comparative study was performed to check the supremacy of the proposed green lubricant in the MQL milling environment. Thus, the following conclusions have been made:

- With the increasing volume fraction of palm oil, the value of surface roughness was decreased first, then upsurged. The minimum value of surface roughness was achieved at the castor-palm volume fraction (1:2). Conversely, the maximum value was achieved at 1:3. The minimum specific energy was attained when the castor-palm volume mixture is 1:2.5. Furthermore, the minimum tool wear was founded at 1:1.5.
- To improve the machining economy and efficiency, the selection of proper lubricant is a crucial concern. In this context, Shannon’s entropy-based TOPSIS approach was applied to determine the best castor-palm volume ratio. The ranking of Shannon’s entropy-based TOPSIS conferred that Castor-palm volume fraction (1:2) is best for minimizing machining responses.

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## Nomenclature

CNC | Computer numerical control |

MQL | Minimum quantity lubrication |

SEM | Scanning electron microscope |

OM | Optical microscope |

R_{a} | Surface roughness |

F_{r} | Resultant cutting force |

E_{sp} | Specific energy |

V_{B} | Tool wear |

MCDM | Multi-criteria decision-making method |

TOPSIS | Technique for order preference by similarity to ideal solution |

PIS | Positive ideal solution |

NIS | Negative ideal solution |

RC_{i} | Relative closeness coefficient |

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Specification | Description |
---|---|

Base metal | Fine-grained cemented carbide |

Diameter | 6 mm |

Flutes | 4 |

Length | 83 mm |

Rake angle | 6° |

Helix angle | 30° |

Clearance angle | 15° |

Grain size | 1 µm |

**Table 2.**Chemical composition of castor oil [17].

Acid Name | Average Range (%) |
---|---|

Ricinoleic acid | 84.5–94 |

Oleic acid | 3–7 |

Linoleic acid | 1.5–6 |

α-Linolenic acid | 0.4–1 |

Stearic acid | 0.4–1 |

Palmitic acid | 0.4–1 |

Dihydroxystearic acid | 0.25–0.6 |

Others | 0.25–0.6 |

**Table 3.**Chemical composition of palm oil [18].

Acid Name | Average Range (%) |
---|---|

Myristic acid | 1 |

Palmitic acid | 43.5 |

Stearic acid | 4.3 |

Oleic acid | 36.6 |

Linoleic acid | 9.1 |

Others | 5.5 |

Specification | Description |
---|---|

Pumping elements | 2 (two) |

Capacity of reservoir | 5 L |

Operating source | Compressed air |

Type of spray | Mist spray |

Functional temperature | −25 °C–75 °C |

Air pressure | 5–15 Bar |

Flow rate | 0–300 mL/h |

Kinematic Viscosity | 25–150 Cst. |

Parameters | Values |
---|---|

Cutting speed | 140 m/min |

Feed | 0.2 mm/tooth |

Depth-of-cut | 1.0 mm |

Flow rate of lubricant | 120 mL/h |

Nozzle distance | 30 mm |

Nozzle angle | 15° |

Pressure | 0.8 MPa |

Experiment No. | Castor-Palm Oil Volume Ratio | Lubrication Condition |
---|---|---|

1 | 1:0.5 | Minimum Quantity Lubrication |

2 | 1:1 | |

3 | 1:1.5 | |

4 | 1:2 | |

5 | 1:2.5 | |

6 | 1:3 |

Machining Performances | Castor Oil | Standard Deviations | Palm Oil | Standard Deviations | Castor-Palm Mixture (1:2) | Standard Deviations |
---|---|---|---|---|---|---|

R_{a} (µm) | 0.351 | 0.005 | 0.384 | 0.042 | 0.322 | 0.074 |

E_{sp} (N/mm^{2}) | 0.403 | 0.007 | 0.414 | 0.064 | 0.381 | 0.043 |

V_{B} (mm) | 0.409 | 0.004 | 0.412 | 0.012 | 0.399 | 0.021 |

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**MDPI and ACS Style**

Sen, B.; Gupta, M.K.; Mia, M.; Pimenov, D.Y.; Mikołajczyk, T.
Performance Assessment of Minimum Quantity Castor-Palm Oil Mixtures in Hard-Milling Operation. *Materials* **2021**, *14*, 198.
https://doi.org/10.3390/ma14010198

**AMA Style**

Sen B, Gupta MK, Mia M, Pimenov DY, Mikołajczyk T.
Performance Assessment of Minimum Quantity Castor-Palm Oil Mixtures in Hard-Milling Operation. *Materials*. 2021; 14(1):198.
https://doi.org/10.3390/ma14010198

**Chicago/Turabian Style**

Sen, Binayak, Munish Kumar Gupta, Mozammel Mia, Danil Yurievich Pimenov, and Tadeusz Mikołajczyk.
2021. "Performance Assessment of Minimum Quantity Castor-Palm Oil Mixtures in Hard-Milling Operation" *Materials* 14, no. 1: 198.
https://doi.org/10.3390/ma14010198