Multi-Objective Optimization of Turning Operation of Stainless Steel Using a Hybrid Whale Optimization Algorithm
Abstract
:1. Introduction
2. Development of the Hybrid Algorithm
3. Methodology
3.1. Design of Experiment
3.2. Workpiece Material and Tool Geometry
3.3. Output Parameters and Simulation Conditions
4. Results and Discussion
4.1. Results of the Output Parametres
4.2. Generating Objective Functions
4.3. Optimization Process
4.4. Optimization Results
4.5. Effect of Cutting Parameters on Surface Roughness
4.6. Effect on Cutting Force (X Direction)
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
Nomenclature
MRR | Metal Removal Rate (mm3/min) |
PTT | Peak Tool Temperature (°C) |
H | Heat rate () |
P | Power (W) |
Fx | Force in x direction (N) |
Fy | Force in y direction (N) |
f | Feed rate (mm/rev) |
V | Cutting Speed () |
d | Depth of Cut (mm) |
References
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Parameters | |||||
---|---|---|---|---|---|
V | 42.64 | 58.91 | 98.18 | 137.45 | 153.71 |
f | 0.14 | 0.18 | 0.27 | 0.36 | 0.4 |
d | 0.32 | 0.4 | 0.6 | 0.8 | 0.88 |
C | Cr | Mn | Ni | P | S | Si |
---|---|---|---|---|---|---|
0.03 | 19 | 2 | 10 | 0.045 | 0.03 | 0.75 |
Hardness, Brinell | Hardness, Rockwell B | Tensile Strength, Ultimate | Tensile Strength, Yield | Elongation at Break | Modulus of Elasticity | Charpy Impact | Shear Modulus |
---|---|---|---|---|---|---|---|
123 | 70 | 505 MPa | 215 MPa | 70% | 193–200 GPa | 325 J | 86 GPa |
No. | Input Parameters | Output Parameters | ||||||||
---|---|---|---|---|---|---|---|---|---|---|
V | f | d | MRR | PTT, | Heat Rate, H | Power, P | Force X, | Force Y, | Roughness, | |
1 | 58.90 | 0.18 | 0.40 | 4048.60 | 540 | 5453 | 170 | 175.0 | 150.0 | 1.48 |
2 | 137.40 | 0.18 | 0.40 | 9993.08 | 675 | 12,726 | 400 | 178.0 | 160.0 | 0.95 |
3 | 58.90 | 0.36 | 0.40 | 8352.32 | 682 | 2727 | 282 | 290.0 | 190.0 | 2.24 |
4 | 137.40 | 0.36 | 0.40 | 19,672.08 | 820 | 6363 | 660 | 286.0 | 185.0 | 1.76 |
5 | 58.90 | 0.18 | 0.80 | 8386.32 | 720 | 5541 | 440 | 440.0 | 355.0 | 1.65 |
6 | 137.40 | 0.18 | 0.80 | 19,722.08 | 860 | 12,930 | 1000 | 440.0 | 355.0 | 1.08 |
7 | 58.90 | 0.36 | 0.80 | 16,794.64 | 830 | 2771 | 730 | 740.0 | 430.0 | 2.57 |
8 | 137.40 | 0.36 | 0.80 | 39,686.16 | 970 | 6465 | 1510 | 690.0 | 385.0 | 2.07 |
9 | 42.60 | 0.27 | 0.60 | 6999.58 | 740 | 2674 | 320 | 445.0 | 300.0 | 2.21 |
10 | 153.70 | 0.27 | 0.60 | 24,981.25 | 830 | 9640 | 850 | 330.0 | 235.0 | 1.38 |
11 | 98.20 | 0.14 | 0.60 | 8146.70 | 675 | 11,875 | 360 | 220.0 | 210.0 | 1.17 |
12 | 98.20 | 0.40 | 0.60 | 23,662.00 | 800 | 4156 | 790 | 470.0 | 279.0 | 2.52 |
13 | 98.20 | 0.27 | 0.32 | 8682.32 | 685 | 6010 | 320 | 190.0 | 142.0 | 1.72 |
14 | 98.20 | 0.27 | 0.88 | 23,426.38 | 700 | 6157 | 870 | 525.0 | 390.0 | 2.08 |
15 | 98.20 | 0.27 | 0.60 | 15,704.35 | 690 | 6062 | 570 | 345.0 | 262.0 | 2.11 |
16 | 98.20 | 0.27 | 0.60 | 15,704.35 | 690 | 6062 | 570 | 345.0 | 262.0 | 2.11 |
17 | 98.20 | 0.27 | 0.60 | 15,704.35 | 690 | 6062 | 570 | 345.0 | 262.0 | 2.11 |
18 | 98.20 | 0.27 | 0.60 | 15,704.35 | 690 | 6062 | 570 | 345.0 | 262.0 | 2.11 |
19 | 98.20 | 0.27 | 0.60 | 15,704.35 | 690 | 6062 | 570 | 345.0 | 262.0 | 2.11 |
20 | 98.20 | 0.27 | 0.60 | 15,704.35 | 690 | 6062 | 570 | 345.0 | 262.0 | 2.11 |
No. | Output Parameters | ||||||
---|---|---|---|---|---|---|---|
MRR | H | P | |||||
1 | 4029.84 | 576.6 | 5435.1 | 173.7 | 181.8 | 152.7 | 1.48 |
2 | 9920.53 | 693.2 | 12,938.1 | 401.1 | 175.0 | 155.4 | 0.95 |
3 | 8318.46 | 711.6 | 2686.8 | 301.0 | 308.3 | 196.5 | 2.24 |
4 | 20,052.02 | 829.9 | 6394.5 | 661.3 | 282.2 | 180.9 | 1.76 |
5 | 8259.00 | 714.9 | 5525.2 | 430.7 | 434.4 | 353.6 | 1.65 |
6 | 19,878.37 | 828.3 | 13,151.1 | 919.1 | 403.1 | 334.2 | 1.08 |
7 | 16,982.40 | 812.2 | 2731.3 | 714.2 | 733.0 | 431.1 | 2.57 |
8 | 40,023.97 | 912.9 | 6499.8 | 1449.9 | 646.8 | 368.4 | 2.07 |
9 | 7074.88 | 701.1 | 2744.2 | 307.9 | 416.8 | 286.5 | 2.21 |
10 | 24,526.73 | 867.5 | 9354.1 | 918.1 | 371.4 | 259.6 | 1.38 |
11 | 8269.97 | 654.5 | 11,662.7 | 377.8 | 229.8 | 218.8 | 1.17 |
12 | 23,138.70 | 817.3 | 4214.7 | 781.1 | 473.4 | 281.8 | 2.52 |
13 | 8676.45 | 618.1 | 6000.9 | 297.5 | 178.9 | 140.5 | 1.72 |
14 | 23,259.65 | 768.1 | 6140.1 | 973.3 | 588.3 | 416.2 | 2.08 |
15 | 15,724.00 | 691.1 | 6066.1 | 566.4 | 342.0 | 259.7 | 2.11 |
16 | 15,724.00 | 691.1 | 6066.1 | 566.4 | 342.0 | 259.7 | 2.11 |
17 | 15,724.00 | 691.1 | 6066.1 | 566.4 | 342.0 | 259.7 | 2.11 |
18 | 15,724.00 | 691.1 | 6066.1 | 566.4 | 342.0 | 259.7 | 2.11 |
19 | 15,724.00 | 691.1 | 6066.1 | 566.4 | 342.0 | 259.7 | 2.11 |
20 | 15,724.00 | 691.1 | 6066.1 | 566.4 | 342.0 | 259.7 | 2.11 |
No. | Data Pre-Processing | ||||||
---|---|---|---|---|---|---|---|
MRR | H | P | |||||
1 | 0.04 | 0.95 | 0.74 | 0.96 | 0.94 | 0.88 | 0.67 |
2 | 0.16 | 0.72 | 0.02 | 0.84 | 0.95 | 0.88 | 1 |
3 | 0.13 | 0.68 | 1 | 0.89 | 0.76 | 0.75 | 0.21 |
4 | 0.36 | 0.45 | 0.65 | 0.7 | 0.8 | 0.79 | 0.5 |
5 | 0.12 | 0.68 | 0.73 | 0.82 | 0.58 | 0.25 | 0.57 |
6 | 0.36 | 0.45 | 0 | 0.57 | 0.63 | 0.31 | 0.92 |
7 | 0.3 | 0.48 | 1 | 0.68 | 0.16 | 0 | 0 |
8 | 0.76 | 0.28 | 0.64 | 0.29 | 0.28 | 0.2 | 0.31 |
9 | 0.1 | 0.7 | 0.99 | 0.89 | 0.61 | 0.46 | 0.23 |
10 | 0.45 | 0.37 | 0.36 | 0.57 | 0.67 | 0.54 | 0.73 |
11 | 0.13 | 0.8 | 0.14 | 0.85 | 0.87 | 0.67 | 0.87 |
12 | 0.42 | 0.47 | 0.85 | 0.64 | 0.53 | 0.47 | 0.04 |
13 | 0.13 | 0.87 | 0.68 | 0.9 | 0.94 | 0.92 | 0.53 |
14 | 0.42 | 0.57 | 0.67 | 0.54 | 0.36 | 0.05 | 0.31 |
15 | 0.27 | 0.72 | 0.68 | 0.75 | 0.71 | 0.54 | 0.28 |
16 | 0.27 | 0.72 | 0.68 | 0.75 | 0.71 | 0.54 | 0.28 |
17 | 0.27 | 0.72 | 0.68 | 0.75 | 0.71 | 0.54 | 0.28 |
18 | 0.27 | 0.72 | 0.68 | 0.75 | 0.71 | 0.54 | 0.28 |
19 | 0.27 | 0.72 | 0.68 | 0.75 | 0.71 | 0.54 | 0.28 |
20 | 0.27 | 0.72 | 0.68 | 0.75 | 0.71 | 0.54 | 0.28 |
No. | Grey Relational Coefficient (GRC) | Grade (GRG) | ||||||
---|---|---|---|---|---|---|---|---|
MRR | H | P | ||||||
1 | 0.5 | 1 | 0.66 | 1 | 0.98 | 0.94 | 0.6 | 0.78 |
2 | 0.55 | 0.64 | 0.34 | 0.77 | 1 | 0.92 | 1 | 0.74 |
3 | 0.53 | 0.61 | 1 | 0.86 | 0.71 | 0.76 | 0.39 | 0.64 |
4 | 0.64 | 0.45 | 0.59 | 0.61 | 0.76 | 0.82 | 0.5 | 0.6 |
5 | 0.53 | 0.6 | 0.65 | 0.74 | 0.56 | 0.46 | 0.54 | 0.58 |
6 | 0.64 | 0.45 | 0.33 | 0.5 | 0.59 | 0.48 | 0.86 | 0.57 |
7 | 0.61 | 0.47 | 0.99 | 0.58 | 0.37 | 0.38 | 0.33 | 0.52 |
8 | 1 | 0.38 | 0.58 | 0.37 | 0.41 | 0.44 | 0.42 | 0.54 |
9 | 0.52 | 0.62 | 0.99 | 0.85 | 0.58 | 0.55 | 0.39 | 0.61 |
10 | 0.7 | 0.42 | 0.44 | 0.5 | 0.63 | 0.6 | 0.65 | 0.57 |
11 | 0.53 | 0.73 | 0.37 | 0.79 | 0.86 | 0.7 | 0.79 | 0.67 |
12 | 0.68 | 0.46 | 0.77 | 0.55 | 0.53 | 0.56 | 0.34 | 0.54 |
13 | 0.54 | 0.83 | 0.61 | 0.86 | 0.99 | 1 | 0.51 | 0.72 |
14 | 0.68 | 0.52 | 0.6 | 0.48 | 0.45 | 0.4 | 0.42 | 0.52 |
15 | 0.6 | 0.64 | 0.61 | 0.66 | 0.67 | 0.6 | 0.41 | 0.58 |
16 | 0.6 | 0.64 | 0.61 | 0.66 | 0.67 | 0.6 | 0.41 | 0.58 |
17 | 0.6 | 0.64 | 0.61 | 0.66 | 0.67 | 0.6 | 0.41 | 0.58 |
18 | 0.6 | 0.64 | 0.61 | 0.66 | 0.67 | 0.6 | 0.41 | 0.58 |
19 | 0.6 | 0.64 | 0.61 | 0.66 | 0.67 | 0.6 | 0.41 | 0.58 |
20 | 0.6 | 0.64 | 0.61 | 0.66 | 0.67 | 0.6 | 0.41 | 0.58 |
Trail No. | Weightage of Output Parameters | Optimize Results | Output Parameters Values for Optimum Conditions |
---|---|---|---|
1 | |||
2 |
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Share and Cite
Tanvir, M.H.; Hussain, A.; Rahman, M.M.T.; Ishraq, S.; Zishan, K.; Rahul, S.T.T.; Habib, M.A. Multi-Objective Optimization of Turning Operation of Stainless Steel Using a Hybrid Whale Optimization Algorithm. J. Manuf. Mater. Process. 2020, 4, 64. https://doi.org/10.3390/jmmp4030064
Tanvir MH, Hussain A, Rahman MMT, Ishraq S, Zishan K, Rahul STT, Habib MA. Multi-Objective Optimization of Turning Operation of Stainless Steel Using a Hybrid Whale Optimization Algorithm. Journal of Manufacturing and Materials Processing. 2020; 4(3):64. https://doi.org/10.3390/jmmp4030064
Chicago/Turabian StyleTanvir, Mahamudul Hasan, Afzal Hussain, M. M. Towfiqur Rahman, Sakib Ishraq, Khandoker Zishan, SK Tashowar Tanzim Rahul, and Mohammad Ahsan Habib. 2020. "Multi-Objective Optimization of Turning Operation of Stainless Steel Using a Hybrid Whale Optimization Algorithm" Journal of Manufacturing and Materials Processing 4, no. 3: 64. https://doi.org/10.3390/jmmp4030064