Response Modeling and Optimization of Process Parameters in Turning Medium Carbon Steel Under Minimum Quantity Lubrication (MQL) with Vegetable Oil and Oil Blends
Abstract
1. Introduction
2. Materials and Methods
2.1. Workpiece and Cutting Tool Material
2.2. Machine Tool and Experimental Conditions
2.3. MQL Setup and Oil Preparation
2.4. Preparation of Cutting Oil Blends
2.5. Measurement of Responses
2.6. RSM-Based Approach for Process Optimization
3. Results and Discussions
3.1. Design of Experiment (DOE) and Data Collection
3.2. Effect of Vegetable-Based Oil Compared to Mineral Oil
3.3. Response Surface Modeling (RSM)
3.4. Optimum Process Parameters Selection by Response Optimizer
4. Conclusions
- Performance of three vegetable oil-based cutting fluids were compared with mineral oil under MQL conditions. Due to the long fatty acid structure and higher viscosity index of vegetable oils, they performed better compared with mineral oils concerning cutting temperature and cutting force.
- Among the three oils, both oil blends performed better than pure coconut oil because, blending can improve the properties of oil as cutting fluids. In addition, between the two oil blends, the coconut + olive oil blend outperformed the other blend (coconut + rice bran oil).
- Based on the experimental data, predictive models for cutting temperature and cutting force were developed. Both of the models preserved over 92% accuracy (R2 value). This accuracy value indicated that the regression models explained more than 92% of the variability in the response data.
- For both of the models, very little differences between the adjusted R2 and predicted R2 values confirmed the adequacy of the model. These models can accurately predict the responses within the range of process parameters used in the experimentation.
- By using residual plots, the normality and randomness of the residuals for both regression models were also well established. Additionally, the residuals also showed constant variance and were correlated to each other. These confirmed the reliability of the regression models and data.
- The regression models were verified with new observation data where the MAPE values were less than 10% for both responses.
- By using a composite desirability-based response optimizer, input parameters for this experiment were optimized for minimum cutting temperature and force simultaneously. Optimum process parameters were found as cutting speed at 80.15 m/min, feed rate at 0.1 mm/rev, and depth of cut at 0.5 mm under the coconut + olive oil blend-based MQL. A high composite desirability value (0.96) of the optimum process parameter settings indicated that this could achieve satisfactory results for all the responses.
- The optimum result was validated and found reasonable improvement of the responses in these settings compared with the initial settings. This optimal setting was proved to be more effective for minimizing the cutting force compared with the cutting temperature. This result was aligned with the individual desirability values of the responses (Force-0.99 and T-0.92).
5. Future Recommendations
- Different vegetable oils can be studied for new blending. Also, the mixing ratio can be varied in further investigation to reveal the effect.
- Due to limited resources, this work only addresses two response parameters (temperature and force), but additional response parameters, such as tool wear, surface roughness, material removal rate (MRR), etc., can be experimentally analyzed.
- The addition of nanoparticles in vegetable oil blends can be considered for advanced analysis.
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Machine Tool | : Lathe Machine (KL-3280C/2000), spindle power 7.5 KW |
Work Material | : Medium Carbon Steel |
Tool Material | : Uncoated Tungsten Carbide |
Cooling Condition | : MQL |
MQL Supply Parameters: | |
Air Pressure | : 23 bar |
Oil Pressure | : 25 bar |
Oil Quantity | : 150 mL/h |
Nozzle Diameter | : 1 mm |
MQL Coolants: | |
i. Mineral Oil (VG 68 Cutting Fluid) | |
ii. Coconut Oil | |
iii. Coconut + Rice Bran Oil | |
iv. Coconut + Olive Oil | |
Process Parameters: | |
Cutting Speed (V) | : 78, 113.5, 149 m/min |
Feed Rate (f) | : 0.1, 0.13, 0.16 mm/rev |
Depth of Cut (d) | : 0.5, 0.75, 1 mm |
Oil Sample | Vegetable Oil | Oil Quantity | Blend Ratio |
---|---|---|---|
Oil blend 1 | Coconut oil | 500 mL | 1:1 |
Rice bran oil | 500 mL | ||
Oil blend 2 | Coconut oil | 500 mL | 1:1 |
Olive oil | 500 mL |
Sl. No | Variable | Symbols | Unit | Lower Level | Upper Level | ||
---|---|---|---|---|---|---|---|
1 | Cutting Speed | V | m/min | 78 | 149 | ||
2 | Feed Rate | f | mm/rev | 0.10 | 0.16 | ||
3 | Depth of Cut | d | mm | 0.5 | 1 | ||
4 | Cooling Environment | CE | Categorical | MO | CO | CO + RBO | CO + OLO |
Exp No. | V (m/min) | f (mm/rev) | d (mm) | CE | T | Fc |
---|---|---|---|---|---|---|
1 | 78 | 0.1 | 0.75 | MO | 828 | 335 |
2 | 149 | 0.1 | 0.75 | MO | 898 | 285 |
3 | 78 | 0.16 | 0.75 | MO | 857 | 432 |
4 | 149 | 0.16 | 0.75 | MO | 932 | 407 |
5 | 78 | 0.13 | 0.5 | MO | 811 | 314 |
6 | 149 | 0.13 | 0.5 | MO | 898 | 304 |
7 | 78 | 0.13 | 1 | MO | 851 | 520 |
8 | 149 | 0.13 | 1 | MO | 944 | 471 |
9 | 113.5 | 0.1 | 0.5 | MO | 840 | 265 |
10 | 113.5 | 0.16 | 0.5 | MO | 863 | 373 |
11 | 113.5 | 0.1 | 1 | MO | 857 | 382 |
12 | 113.5 | 0.16 | 1 | MO | 892 | 481 |
13 | 113.5 | 0.13 | 0.75 | MO | 875 | 353 |
14 | 113.5 | 0.13 | 0.75 | MO | 869 | 368 |
15 | 113.5 | 0.13 | 0.75 | MO | 875 | 351 |
16 | 78 | 0.1 | 0.75 | CO | 782 | 297 |
17 | 149 | 0.1 | 0.75 | CO | 857 | 270 |
18 | 78 | 0.16 | 0.75 | CO | 817 | 425 |
19 | 149 | 0.16 | 0.75 | CO | 869 | 392 |
20 | 78 | 0.13 | 0.5 | CO | 748 | 244 |
21 | 149 | 0.13 | 0.5 | CO | 840 | 236 |
22 | 78 | 0.13 | 1 | CO | 800 | 445 |
23 | 149 | 0.13 | 1 | CO | 915 | 455 |
24 | 113.5 | 0.1 | 0.5 | CO | 800 | 210 |
25 | 113.5 | 0.16 | 0.5 | CO | 817 | 230 |
26 | 113.5 | 0.1 | 1 | CO | 817 | 324 |
27 | 113.5 | 0.16 | 1 | CO | 863 | 438 |
28 | 113.5 | 0.13 | 0.75 | CO | 823 | 312 |
29 | 113.5 | 0.13 | 0.75 | CO | 805 | 307 |
30 | 113.5 | 0.13 | 0.75 | CO | 811 | 311 |
31 | 78 | 0.1 | 0.75 | CO + RBO | 754 | 265 |
32 | 149 | 0.1 | 0.75 | CO + RBO | 840 | 245 |
33 | 78 | 0.16 | 0.75 | CO + RBO | 777 | 405 |
34 | 149 | 0.16 | 0.75 | CO + RBO | 857 | 384 |
35 | 78 | 0.13 | 0.5 | CO + RBO | 731 | 235 |
36 | 149 | 0.13 | 0.5 | CO + RBO | 823 | 230 |
37 | 78 | 0.13 | 1 | CO + RBO | 754 | 427 |
38 | 149 | 0.13 | 1 | CO + RBO | 851 | 397 |
39 | 113.5 | 0.1 | 0.5 | CO + RBO | 800 | 195 |
40 | 113.5 | 0.16 | 0.5 | CO + RBO | 805 | 254 |
41 | 113.5 | 0.1 | 1 | CO + RBO | 811 | 301 |
42 | 113.5 | 0.16 | 1 | CO + RBO | 840 | 385 |
43 | 113.5 | 0.13 | 0.75 | CO + RBO | 782 | 280 |
44 | 113.5 | 0.13 | 0.75 | CO + RBO | 765 | 290 |
45 | 113.5 | 0.13 | 0.75 | CO + RBO | 771 | 285 |
46 | 78 | 0.1 | 0.75 | CO + OLO | 707 | 221 |
47 | 149 | 0.1 | 0.75 | CO + OLO | 811 | 240 |
48 | 78 | 0.16 | 0.75 | CO + OLO | 754 | 319 |
49 | 149 | 0.16 | 0.75 | CO + OLO | 840 | 361 |
50 | 78 | 0.13 | 0.5 | CO + OLO | 696 | 242 |
51 | 149 | 0.13 | 0.5 | CO + OLO | 811 | 221 |
52 | 78 | 0.13 | 1 | CO + OLO | 748 | 418 |
53 | 149 | 0.13 | 1 | CO + OLO | 800 | 379 |
54 | 113.5 | 0.1 | 0.5 | CO + OLO | 777 | 181 |
55 | 113.5 | 0.16 | 0.5 | CO + OLO | 794 | 255 |
56 | 113.5 | 0.1 | 1 | CO + OLO | 794 | 342 |
57 | 113.5 | 0.16 | 1 | CO + OLO | 817 | 355 |
58 | 113.5 | 0.13 | 0.75 | CO + OLO | 777 | 234 |
59 | 113.5 | 0.13 | 0.75 | CO + OLO | 771 | 231 |
60 | 113.5 | 0.13 | 0.75 | CO + OLO | 765 | 236 |
Sl. No. | V m/min | F mm/rev | D mm | CO % Reduction | CO + RBO % Reduction | CO + OLO % Reduction | |||
---|---|---|---|---|---|---|---|---|---|
T | Fc | T | Fc | T | Fc | ||||
1 | 78 | 0.1 | 0.75 | 5.56 | 11.35 | 8.94 | 20.9 | 14.62 | 34.03 |
2 | 149 | 0.1 | 0.75 | 4.57 | 5.27 | 6.46 | 14.04 | 9.69 | 15.79 |
3 | 78 | 0.13 | 0.5 | 7.77 | 22.30 | 9.87 | 25.16 | 14.19 | 22.93 |
4 | 149 | 0.13 | 0.5 | 6.46 | 22.37 | 8.36 | 24.35 | 9.69 | 27.31 |
5 | 78 | 0.13 | 1 | 6.00 | 14.43 | 11.4 | 17.89 | 12.11 | 19.62 |
6 | 149 | 0.13 | 1 | 3.08 | 3.40 | 9.86 | 15.72 | 15.26 | 19.54 |
7 | 113.5 | 0.1 | 0.5 | 4.77 | 20.76 | 4.77 | 26.42 | 7.50 | 31.70 |
8 | 113.5 | 0.16 | 0.5 | 5.34 | 38.34 | 6.73 | 31.91 | 8.00 | 31.64 |
9 | 113.5 | 0.16 | 1 | 3.26 | 8.94 | 5.83 | 19.96 | 8.41 | 26.20 |
AVG | 5.20 | 16.35 | 8.02 | 21.82 | 11.05 | 25.42 |
Source | DF | Adj SS | Adj MS | F-Value | p-Value |
---|---|---|---|---|---|
Model | 7 | 150,999 | 21,571.3 | 115.55 | 0.000 |
Linear | 6 | 148,515 | 24,752.6 | 132.59 | 0.000 |
V | 1 | 58,739 | 58,738.8 | 314.64 | 0.063 |
f | 1 | 5539 | 5538.8 | 29.67 | 0.000 |
d | 1 | 7813 | 7812.5 | 41.85 | 0.000 |
Env | 3 | 76,425 | 25,475.1 | 136.46 | 0.000 |
Square | 1 | 2484 | 2484.0 | 13.31 | 0.001 |
V*V | 1 | 2484 | 2484.0 | 13.31 | 0.001 |
Error | 52 | 9709 | 186.7 | ||
Lack-of-Fit | 44 | 9295 | 211.2 | 4.10 | 0.020 |
Pure Error | 8 | 413 | 51.6 | ||
Total | 59 | 160,707 | |||
Model Summary | |||||
S | R-sq | R-sq (adj) | R-sq (pred) | ||
13.6632 | 93.96% | 93.15% | 91.89% |
Source | DF | Adj SS | Adj MS | F-Value | p-Value |
---|---|---|---|---|---|
Model | 8 | 368,502 | 46,063 | 74.45 | 0.000 |
Linear | 6 | 348,293 | 58,049 | 93.83 | 0.000 |
V | 1 | 2228 | 2228 | 3.60 | 0.063 |
f | 1 | 73,920 | 73,920 | 119.48 | 0.000 |
d | 1 | 200,186 | 200,186 | 323.57 | 0.000 |
Env | 3 | 71,959 | 23,986 | 38.77 | 0.000 |
Square | 2 | 20,209 | 10,105 | 16.33 | 0.000 |
V*V | 1 | 17,817 | 17,817 | 28.80 | 0.000 |
d*d | 1 | 3401 | 3401 | 5.50 | 0.023 |
Error | 51 | 31,553 | 619 | ||
Lack-of-Fit | 43 | 31,304 | 728 | 23.36 | 0.000 |
Pure Error | 8 | 249 | 31 | ||
Total | 59 | 400,055 | |||
Model Summary | |||||
S | R-sq | R-sq (adj) | R-sq (pred) | ||
24.8734 | 92.11% | 90.88% | 88.90% |
Run | Input Parameters | Experimental Output | Predicted Output | APE | ||||||
---|---|---|---|---|---|---|---|---|---|---|
V (m/min) | f (mm/rev) | D (mm) | CE | T (°C) | Fc (N) | T (°C) | Fc (N) | T (%) | Fc (%) | |
1 | 113.5 | 0.1 | 0.75 | MO | 905 | 294 | 866 | 302 | 4.36 | 2.56 |
2 | 78 | 0.13 | 0.5 | 836 | 314 | 807 | 329 | 3.43 | 4.65 | |
3 | 149 | 0.16 | 1 | 928 | 485 | 950 | 518 | 2.41 | 6.84 | |
4 | 113.5 | 0.1 | 0.75 | CO | 848 | 258 | 817 | 252 | 3.64 | 2.39 |
5 | 78 | 0.13 | 0.5 | 813 | 273 | 759 | 279 | 6.65 | 2.16 | |
6 | 149 | 0.16 | 1 | 882 | 439 | 902 | 468 | 2.26 | 6.71 | |
7 | 113.5 | 0.1 | 0.75 | CO + RBO | 756 | 217 | 790 | 231 | 4.53 | 6.28 |
8 | 78 | 0.13 | 0.5 | 716 | 240 | 732 | 258 | 2.24 | 7.37 | |
9 | 149 | 0.16 | 1 | 842 | 422 | 875 | 447 | 3.93 | 5.99 | |
10 | 113.5 | 0.1 | 0.75 | CO + OLO | 727 | 195 | 770 | 208 | 5.93 | 6.58 |
11 | 78 | 0.13 | 0.5 | 664 | 249 | 712 | 235 | 7.25 | 5.67 | |
12 | 149 | 0.16 | 1 | 796 | 398 | 855 | 424 | 7.41 | 6.65 |
Optimum Parameters | |
---|---|
Cutting Speed | 80.15 m/min |
Feed Rate | 0.10 mm/min |
Depth of Cut | 0.50 mm |
Cutting Environment | Coconut + Olive Oil Blend |
Responses | Initial Settings | RSM Settings | Improvement in Responses | ||
---|---|---|---|---|---|
Predicted Value | Experimental Value | Predicted Value | Experimental Value | ||
Parameter settings (V, f, d, Env) | 78, 0.1, 0.75, MO | 80.15, 0.1, 0.5, CO + OLO | - | ||
Cutting temperature | 823 | 828 | 714 | 698 | 15.7% |
Cutting force | 344 | 335 | 182 | 194 | 42% |
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© 2024 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 (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Das, I.; Zaman, P.B. Response Modeling and Optimization of Process Parameters in Turning Medium Carbon Steel Under Minimum Quantity Lubrication (MQL) with Vegetable Oil and Oil Blends. Lubricants 2024, 12, 444. https://doi.org/10.3390/lubricants12120444
Das I, Zaman PB. Response Modeling and Optimization of Process Parameters in Turning Medium Carbon Steel Under Minimum Quantity Lubrication (MQL) with Vegetable Oil and Oil Blends. Lubricants. 2024; 12(12):444. https://doi.org/10.3390/lubricants12120444
Chicago/Turabian StyleDas, Indranil, and Prianka Binte Zaman. 2024. "Response Modeling and Optimization of Process Parameters in Turning Medium Carbon Steel Under Minimum Quantity Lubrication (MQL) with Vegetable Oil and Oil Blends" Lubricants 12, no. 12: 444. https://doi.org/10.3390/lubricants12120444
APA StyleDas, I., & Zaman, P. B. (2024). Response Modeling and Optimization of Process Parameters in Turning Medium Carbon Steel Under Minimum Quantity Lubrication (MQL) with Vegetable Oil and Oil Blends. Lubricants, 12(12), 444. https://doi.org/10.3390/lubricants12120444