#
Multi-Objective Optimization for Grinding of AISI D2 Steel with Al_{2}O_{3} Wheel under MQL

^{1}

^{2}

^{3}

^{4}

^{5}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Methodology

#### Experimental Setup

^{3}was used as the coolant for conventional wet grinding. A flow rate of 6 L/min was maintained throughout wet grinding. For experiments involving grinding under MQL condition Accu-Lube 6000 with specific gravity 0.92, flash point 214 °C, and density 8.9 CSt special cutting oil was used at variable flow rates between 50 and 50 mL/h. During the whole process the air pressure was 6 bar, with a nozzle angle of 15°, and nozzle distance of 30 mm, which were kept constant. The schematic diagram for surface grinding process along with responses measurement has been shown in Figure 2.

## 3. Results and Discussion

#### 3.1. Normal Forces

^{2}, Adj. R

^{2}and predicted R

^{2}is close to ‘1’ indicating the adequacy of resulting model for all considered environments. The results of ANOVA indicate that the model is significant with a p-value of less than 0.05.

#### 3.2. Temperature

#### 3.3. Surface Roughness

_{a}) for dry, wet, and MQL grinding environments are presented in Figure 8a–c, it is evident from these figures that the surface roughness has a direct relation to table speed and cutting speed, i.e., an increase in speed also increases surface roughness. The impact of changing table speed is more at a low level of cutting speed as compared to the high level of cutting speed. Similarly, the impact of table speed on surface roughness is higher at a low level of table speed as compared to the high level of table speed.

#### 3.4. Surface Topography for MQL-Assisted Grinding

## 4. Formulation and Validation of Multi-Objective Optimization

#### 4.1. Evaluation of Optimal Parameters for MQL Grinding

_{i}(k) is known as deviation sequence and it is the absolute value. The minimum and maximum values of ∆0

_{i}(k) are denoted by ${\u2206}_{min}$ and ${\u2206}_{max}$ respectively. ${\mathsf{\gamma}}_{i}$ represents the grey relational grade of each experiment. The values of $\mathsf{\xi}$ lies between 0 and 1 and it is called distinguishing coefficient, normally its value is taken as 0.5. ${w}_{k}$ represents the normalized weight of factor $k$. Table 5 consists of GRC and GRG of MQL grinding.

#### 4.2. ANOVA for Grey Relational Grade

^{2}) and table speed (${{v}_{w}}^{2})$, were identified as the significant model terms. The ANOVA results comprising of significant model terms along with adequacy measures R

^{2}, adjusted R

^{2}and predicted R

^{2}are shown in Table 6. The values of adequacy measures R

^{2}, adjusted R

^{2}and predicted R

^{2}close to 1 indicate the adequacy of the resulting models. The empirical models for the prediction GRG has been provided in Equation (14).

#### 4.3. Confirmatory Test

^{3}-${v}_{w}$

^{1}-${v}_{s}$

^{3}-Q

^{3}) to the optimum cutting condition (${a}_{p}$

^{1}-${v}_{w}$

^{1}-${v}_{s}$

^{3}-Q

^{5}) was 3.18 and these results are in good agreement with the results from the literature. Pawade and Joshi [55] employed the Taguchi grey relational analysis (TGRA) method in the turning process and they achieved 4.11% achievement in optimum GRG when compared with initial conditions.

## 5. Conclusions

- In all grinding conditions, the surface roughness is profoundly affected by the table speed, and the surface roughness values achieved in wet and MQL conditions are comparable.
- Depth of cut has been found as the critical process parameter for temperature in all cutting conditions. Comparison results for temperatures show that overall minimum temperature was achieved in wet grinding followed by MQL-assisted grinding. Moreover, it was noted that MQL grinding results in a temperature reduction of 55.18 to 67.4% as compared to dry grinding.
- Cutting forces were found increasing as the depth of cut was increased. Comparative results for forces have shown that minimum cutting forces were achieved in MQL environment. When compared with dry grinding, 37.86–79.26% reduction in forces was found in MQL-assisted grinding.
- During the experiments, it was observed that MQL flow rates values used in the grinding process did not cause in the dispersion of mist, which is environmentally friendly. More interesting to mention, the MQL maintained the cutting zone clean like dry grinding, and it produced a comparable, and sometimes better, result when compared to wet grinding.
- The modeling and optimization of the MQL-assisted grinding process can be applied in the green manufacturing process. It was noted that RSM prediction is well matched with the experimental results. Also, research findings and a mathematical model developed in this study can be used in the industry by the machinist to predict the surface roughness, temperature, and normal forces for grinding of AISI D2 steel.
- The work surface quality in MQL is comparable to wet condition. Therefore, MQL is recommended over flood cooling due it MQL’s economic and clean production perspective. However, considerable further research is needed to fully exploit its potential.

_{2}is still not exploded very well. Finally, further research should be carried out on how these newly developed technologies can become sustainable regarding economic and environmental aspects.

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

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**Figure 2.**Schematic diagram showing minimum quality lubrication (MQL) working principle and response measurements.

**Figure 4.**3D Surface plots showing the effects of depth of cut and table speed on forces for (

**a**) Dry, (

**b**) Wet, and (

**c**) MQL conditions. Units of table speed and depth of cut are ‘m/min’ and ‘µm’, respectively.

**Figure 6.**Surface plots showing the effects of depth of cut and table speed on temperature for (

**a**) Dry, (

**b**) Wet, (

**c**) and MQL conditions. Units of table speed, depth of cut, and MQL flow rate are ‘m/min’, ‘µm’, and ‘mL/h’, respectively.

**Figure 8.**3D Surface plots showing the effects of cutting speed and table speed on surface roughness for (

**a**) Dry, (

**b**) Wet, and (

**c**) MQL conditions. Units of table speed and cutting speed are ‘m/min’ and ‘m/s’, respectively.

**Figure 9.**(

**a**) General view of 3D optical profilometer S neox (Sensofar, Barcelona, Spain), (

**b**) micrography of the ground surface, and (

**c**) surface topography of the ground surface.

Element | C | Si | Mn | Cr | Mo | V | P | S | Ni | Fe |
---|---|---|---|---|---|---|---|---|---|---|

% | 1.56 | 0.30 | 0.40 | 11.90 | 0.78 | 0.80 | 0.023 | 0.015 | 0.05 | Balance |

SL. No. | Parameter (s) | −α | Low (−1) | Centre (0) | High (+1) | +α |
---|---|---|---|---|---|---|

Dry, Wet, MQL | ||||||

1 | Depth of cut: ${a}_{p}$, (µm) | 15 | 20 | 25 | 30 | 35 |

2 | Table speed: ${v}_{w}$, (m/min) | 3 | 5 | 7 | 9 | 11 |

3 | Cutting Speed: ${v}_{s}$, (m/s) | 15 | 20 | 25 | 30 | 35 |

For MQL only | ||||||

4 | MQL flow rate: Q, (mL/h) | 50 | 100 | 150 | 200 | 250 |

Exp. No. | Process Parameters | Response Measurements | |||||||
---|---|---|---|---|---|---|---|---|---|

Dry Grinding | Wet Grinding | ||||||||

${\mathit{a}}_{\mathit{p}}$ | ${\mathit{v}}_{\mathit{w}}$ | ${\mathit{v}}_{\mathit{s}}$ | F | T | ${\mathit{R}}_{\mathit{a}}$ | F | T | ${\mathit{R}}_{\mathit{a}}$ | |

(µm) | (m/min) | (m/s) | (N) | (°C) | (µm) | (N) | (°C) | (µm) | |

1 | 20 | 5 | 20 | 82 | 245 | 0.5 | 60 | 69 | 0.29 |

2 | 30 | 5 | 20 | 115 | 270 | 0.58 | 70 | 83 | 0.34 |

3 | 20 | 9 | 20 | 83 | 240 | 1.4 | 54 | 80 | 0.58 |

4 | 30 | 9 | 20 | 117 | 291 | 1.51 | 81 | 84 | 0.63 |

5 | 20 | 5 | 30 | 85 | 245 | 1.12 | 46 | 58 | 0.48 |

6 | 30 | 5 | 30 | 92 | 297 | 0.89 | 71 | 94 | 0.46 |

7 | 20 | 9 | 30 | 110 | 248 | 1.45 | 47 | 71 | 0.59 |

8 | 30 | 9 | 30 | 103 | 310 | 1.49 | 80 | 92 | 0.51 |

9 | 15 | 7 | 25 | 82 | 225 | 0.81 | 40 | 62 | 0.51 |

10 | 35 | 7 | 25 | 119 | 319 | 1.07 | 90 | 109 | 0.56 |

11 | 25 | 3 | 25 | 86 | 268 | 0.46 | 55 | 74 | 0.25 |

12 | 25 | 11 | 25 | 105 | 300 | 1.6 | 65 | 82 | 0.65 |

13 | 25 | 7 | 15 | 111 | 262 | 1.01 | 61 | 70 | 0.40 |

14 | 25 | 7 | 35 | 98 | 276 | 1.4 | 49 | 80 | 0.55 |

15 | 25 | 7 | 25 | 90 | 262 | 1.35 | 56 | 69 | 0.42 |

16 | 25 | 7 | 25 | 91 | 260 | 1.39 | 55 | 70 | 0.43 |

17 | 25 | 7 | 25 | 90 | 268 | 1.42 | 54 | 68 | 0.41 |

18 | 25 | 7 | 25 | 93 | 260 | 1.41 | 58 | 71 | 0.42 |

Exp. No. | Process Parameters | Response Measurements | |||||
---|---|---|---|---|---|---|---|

${\mathit{a}}_{\mathit{p}}$ | ${\mathit{v}}_{\mathbf{w}}$ | ${\mathit{v}}_{\mathbf{s}}$ | Q | F | T | ${\mathit{R}}_{\mathit{a}}$ | |

(µm) | (m/min) | (m/s) | (mL/h) | (N) | (°C) | (µm) | |

1 | 20 | 5 | 20 | 100 | 36 | 104 | 0.39 |

2 | 30 | 5 | 20 | 100 | 50 | 121 | 0.4 |

3 | 20 | 9 | 20 | 100 | 65 | 114 | 0.58 |

4 | 30 | 9 | 20 | 100 | 71 | 131 | 0.6 |

5 | 20 | 5 | 30 | 100 | 35 | 109 | 0.39 |

6 | 30 | 5 | 30 | 100 | 37 | 121 | 0.51 |

7 | 20 | 9 | 30 | 100 | 62 | 117 | 0.62 |

8 | 30 | 9 | 30 | 100 | 58 | 133 | 0.66 |

9 | 20 | 5 | 20 | 200 | 28 | 82 | 0.33 |

10 | 30 | 5 | 20 | 200 | 37 | 95 | 0.395 |

11 | 20 | 9 | 20 | 200 | 55 | 85 | 0.56 |

12 | 30 | 9 | 20 | 200 | 67 | 97 | 0.61 |

13 | 20 | 5 | 30 | 200 | 28 | 85 | 0.35 |

14 | 30 | 5 | 30 | 200 | 31 | 99 | 0.44 |

15 | 20 | 9 | 30 | 200 | 53 | 94 | 0.53 |

16 | 30 | 9 | 30 | 200 | 64 | 101 | 0.63 |

17 | 15 | 7 | 25 | 150 | 41 | 97 | 0.42 |

18 | 35 | 7 | 25 | 150 | 56 | 118 | 0.53 |

19 | 25 | 3 | 25 | 150 | 17 | 85 | 0.31 |

20 | 25 | 11 | 25 | 150 | 70 | 94 | 0.71 |

21 | 25 | 7 | 15 | 150 | 54 | 112 | 0.47 |

22 | 25 | 7 | 35 | 150 | 46 | 118 | 0.51 |

23 | 25 | 7 | 25 | 50 | 57 | 134 | 0.5 |

24 | 25 | 7 | 25 | 250 | 50 | 74 | 0.45 |

25 | 25 | 7 | 25 | 150 | 44 | 109 | 0.49 |

26 | 25 | 7 | 25 | 150 | 47 | 107 | 0.47 |

27 | 25 | 7 | 25 | 150 | 45 | 111 | 0.48 |

28 | 25 | 7 | 25 | 150 | 47 | 108 | 0.45 |

29 | 25 | 7 | 25 | 150 | 38 | 106 | 0.47 |

30 | 25 | 7 | 25 | 150 | 45 | 108 | 0.46 |

**Q**: MQL flow rate; ${R}_{a}$: Average surface roughness.

No. | GRC of Surface Roughness | GRC of Temperature | GRC of Normal Force | GRG |
---|---|---|---|---|

1 | 0.7143 | 0.5000 | 0.5870 | 0.6004 |

2 | 0.6897 | 0.3896 | 0.4500 | 0.5098 |

3 | 0.4255 | 0.4286 | 0.3600 | 0.4047 |

4 | 0.4082 | 0.3448 | 0.3333 | 0.3621 |

5 | 0.7143 | 0.4615 | 0.6000 | 0.5919 |

6 | 0.5000 | 0.3896 | 0.5745 | 0.4880 |

7 | 0.3922 | 0.4110 | 0.3750 | 0.3927 |

8 | 0.3636 | 0.3371 | 0.3971 | 0.3659 |

9 | 0.9091 | 0.7895 | 0.7105 | 0.8030 |

10 | 0.7018 | 0.5882 | 0.5745 | 0.6215 |

11 | 0.4444 | 0.7317 | 0.4154 | 0.5305 |

12 | 0.4000 | 0.5660 | 0.3506 | 0.4389 |

13 | 0.8333 | 0.7317 | 0.7105 | 0.7585 |

14 | 0.6061 | 0.5455 | 0.6585 | 0.6034 |

15 | 0.4762 | 0.6000 | 0.4286 | 0.5016 |

16 | 0.3846 | 0.5263 | 0.3649 | 0.4253 |

17 | 0.6452 | 0.5660 | 0.5294 | 0.5802 |

18 | 0.4762 | 0.4054 | 0.4091 | 0.4302 |

19 | 1.0000 | 0.7317 | 1.0000 | 0.9106 |

20 | 0.3333 | 0.6000 | 0.3375 | 0.4236 |

21 | 0.5556 | 0.4412 | 0.4219 | 0.4729 |

22 | 0.5000 | 0.4054 | 0.4821 | 0.4625 |

23 | 0.5128 | 0.3333 | 0.4030 | 0.4164 |

24 | 0.5882 | 1.0000 | 0.4500 | 0.6794 |

25 | 0.5263 | 0.4615 | 0.5000 | 0.4960 |

26 | 0.5556 | 0.4762 | 0.4737 | 0.5018 |

27 | 0.5405 | 0.4478 | 0.4909 | 0.4931 |

28 | 0.5882 | 0.4688 | 0.4737 | 0.5102 |

29 | 0.5556 | 0.4839 | 0.5625 | 0.5340 |

30 | 0.5714 | 0.4688 | 0.4909 | 0.5104 |

Source | SS | df | MS | F-Value | p-Value |
---|---|---|---|---|---|

Model | 0.4686 | 14 | 0.0335 | 61.1807 | <0.0001 |

Depth of Cut (${a}_{p})$ | 0.0476 | 1 | 0.0476 | 86.9630 | 0.0001 |

Table Speed (${v}_{w})$ | 0.2664 | 1 | 0.2664 | 487.0265 | <0.0001 |

Cutting Speed (${v}_{s})$ | 0.0011 | 1 | 0.0011 | 2.0556 | 0.1722 |

MQL Flow rate (Q) | 0.0929 | 1 | 0.0929 | 169.8022 | 0.0001 |

${a}_{p}\times {v}_{w}$ | 0.0054 | 1 | 0.0054 | 9.8734 | 0.0067 |

${a}_{p}\times {v}_{s}$ | 0.0001 | 1 | 0.0001 | 0.2234 | 0.6432 |

${a}_{p}\times Q$ | 0.0036 | 1 | 0.0036 | 6.6193 | 0.0212 |

${v}_{w}\times {v}_{s}$ | 0.0001 | 1 | 0.0001 | 0.2026 | 0.6591 |

${v}_{w}\times Q$ | 0.0032 | 1 | 0.0032 | 5.8058 | 0.0293 |

${v}_{s}\times Q$ | 0.0003 | 1 | 0.0003 | 0.5101 | 0.4861 |

${{a}_{p}}^{2}$ | 0.0005 | 1 | 0.0005 | 0.9133 | 0.3544 |

${{v}_{w}}^{2}$ | 0.0359 | 1 | 0.0359 | 65.7029 | 0.0001 |

${{v}_{s}}^{2}$ | 0.0051 | 1 | 0.0051 | 9.3403 | 0.008 |

${Q}^{2}$ | 0.0011 | 1 | 0.0011 | 2.0548 | 0.1722 |

Residual | 0.0082 | 15 | 0.0005 | ||

Lack of Fit | 0.0071 | 10 | 0.0007 | 3.2625 | 0.102 |

Pure Error | 0.0011 | 5 | 0.0002 | ||

Cor. Total | 0.4768 | 29 | |||

Std. Dev. | 0.023 | R-Squared | 0.9828 | ||

Mean | 0.53 | Adj R-Squared | 0.9667 | ||

C.V. % | 4.44 | Pred R-Squared | 0.9107 | ||

PRESS | 0.043 | Adeq Precision | 30.121 |

Initial Cutting Conditions | Optimal Cutting Conditions | ||
---|---|---|---|

Predicted Results | Experimental Results | ||

Levels | ${a}_{p}$^{3}-${v}_{w}$^{1}-${v}_{s}$^{3}-Q^{3} | ${a}_{p}$^{1}-${v}_{w}$^{1}-${v}_{s}$^{3}-Q^{5} | ${a}_{p}$^{1}-${v}_{w}$^{1}-${v}_{s}$^{3}-Q^{5} |

Surface roughness (µm) | 0.31 | 0.29 | |

Temperature (°C) | 85 | 81 | |

Normal force (N) | 17 | 16 | |

GRG | 0.9106 | 0.9242 | 0.9396 |

Improvement in GRG = 0.029; the % improvement in GRG = 3.18 |

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Khan, A.M.; Jamil, M.; Mia, M.; Pimenov, D.Y.; Gasiyarov, V.R.; Gupta, M.K.; He, N. Multi-Objective Optimization for Grinding of AISI D2 Steel with Al_{2}O_{3} Wheel under MQL. *Materials* **2018**, *11*, 2269.
https://doi.org/10.3390/ma11112269

**AMA Style**

Khan AM, Jamil M, Mia M, Pimenov DY, Gasiyarov VR, Gupta MK, He N. Multi-Objective Optimization for Grinding of AISI D2 Steel with Al_{2}O_{3} Wheel under MQL. *Materials*. 2018; 11(11):2269.
https://doi.org/10.3390/ma11112269

**Chicago/Turabian Style**

Khan, Aqib Mashood, Muhammad Jamil, Mozammel Mia, Danil Yurievich Pimenov, Vadim Rashitovich Gasiyarov, Munish Kumar Gupta, and Ning He. 2018. "Multi-Objective Optimization for Grinding of AISI D2 Steel with Al_{2}O_{3} Wheel under MQL" *Materials* 11, no. 11: 2269.
https://doi.org/10.3390/ma11112269