Optimal Parameter Selection in Robotic Belt Polishing for Aeroengine Blade Based on GRA-RSM Method
Abstract
:1. Introduction
2. Experiment Procedure
2.1. Experimental Design
2.2. Experimental Materials and Experimental Preparation
2.3. Measurement Methods
3. Multiobjective Optimization Method
4. Results and Discussion
4.1. Effect of Polishing Parameters on a Single Response Variable
4.2. Gray Correlation Analysis
4.3. Model Establishment
− 3.05046 × 10−5 vf × vs + 5.42413 × 10−5 ap × vs − 3.62566×10−6 vf2 + 15.99582 ap2
+ 1.85397 × 10−3 vs2
4.4. Optimal Gray Correlation Prediction
4.5. Verification
5. Conclusions
- In the robotic belt polishing for aeroengine blades, the main parameters influencing the aeroengine blade polishing include feed rate vf, compression amount ap, and belt line speed vs. The belt line speed vs is the main process parameter affecting material removal rates and surface roughness.
- The results of principal component analysis show that surface roughness is the priority principal component, then followed by material removal rate. The corresponding contribution rates were 54.6% and 45.4%, respectively. The proposed GRA-RSM method can effectively predict the optimal setting of process parameters in the robotic belt polishing for aeroengine blades, then achieving the important aim of reducing the surface roughness, and improving the material removal rate simultaneously.
- For the maximum grey correlation grade, which increased by 10.96%, the optimum polishing parameter combination was selected as follows: the feed rate vf is 232.09 mm/min, the compression amount ap is 0.08 mm, and the belt line speed vs is 16m/s. Finally, the surface roughness was reduced by 6.29%, and the material removal rate was increased by 16.11%.
Author Contributions
Funding
Conflicts of Interest
References
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Experimental Parameters | Symbol | Units | Levels of Experimental Parameters | ||
---|---|---|---|---|---|
Level 1 | Level 2 | Level 3 | |||
Feed rate | vf | mm/min | 100 | 200 | 300 |
Compression | ap | mm | 0.02 | 0.05 | 0.08 |
Belt line speed | vs | m/s | 8 | 12 | 16 |
Number | Experiment Parameters | SR | MRR | ||||
---|---|---|---|---|---|---|---|
vf | ap | vs | Ra (μm) | S/N | Zw (mm²/min) | S/N | |
1 | 200 | 0.02 | 8 | 0.415 | 7.6390 | 0.0123 | −38.2019 |
2 | 100 | 0.08 | 12 | 0.448 | 6.9744 | 0.0374 | −28.5426 |
3 | 200 | 0.08 | 8 | 0.527 | 5.5638 | 0.0428 | −27.3711 |
4 | 200 | 0.05 | 12 | 0.383 | 8.3360 | 0.0337 | −29.4474 |
5 | 100 | 0.05 | 8 | 0.558 | 5.0673 | 0.0181 | −34.8464 |
6 | 300 | 0.05 | 8 | 0.543 | 5.3040 | 0.0262 | −31.6340 |
7 | 200 | 0.05 | 12 | 0.407 | 7.8081 | 0.0351 | −29.0939 |
8 | 100 | 0.02 | 12 | 0.413 | 7.6810 | 0.0234 | −32.6157 |
9 | 300 | 0.05 | 16 | 0.394 | 8.0901 | 0.0574 | −24.8218 |
10 | 200 | 0.02 | 16 | 0.311 | 10.1448 | 0.0193 | −34.2889 |
11 | 200 | 0.08 | 16 | 0.413 | 7.6810 | 0.0701 | −23.0856 |
12 | 100 | 0.05 | 16 | 0.347 | 9.1934 | 0.0457 | −26.8017 |
13 | 300 | 0.08 | 12 | 0.476 | 6.4479 | 0.0547 | −25.2403 |
14 | 300 | 0.02 | 12 | 0.403 | 7.8939 | 0.0281 | −31.0259 |
15 | 200 | 0.05 | 12 | 0.421 | 7.5144 | 0.0381 | −28.3815 |
Chemical Composition | Al | V | Fe | Si | C | N | H | O | Other |
---|---|---|---|---|---|---|---|---|---|
% | 5.5–6.8 | 3.5–4.5 | ≤0.30 | ≤0.15 | ≤0.10 | ≤0.05 | ≤0.01 | ≤0.20 | 0.11 |
Source | Process Parameters | ||
---|---|---|---|
vf | ap | vs | |
SR | |||
Level 1 | 7.2290 | 8.3397 | 5.8935 |
Level 2 | 7.8124 | 7.3305 | 7.5222 |
Level 3 | 6.9340 | 6.6668 | 8.7773 |
Max-min | 0.8785 | 1.6729 | 2.8838 |
Rank | 3 | 2 | 1 |
Optimal level | A2 | B1 | C3 |
MRR | |||
Level 1 | −30.7016 | −34.0331 | −33.0134 |
Level 2 | −29.9815 | −29.2895 | −29.1924 |
Level 3 | −28.1805 | −26.0599 | −27.2495 |
Max-min | 2.5211 | 7.9732 | 5.7639 |
Rank | 3 | 1 | 2 |
Optimal level | A3 | B3 | C3 |
Number | Deviation Sequence Δ0i | Gray Relational Coefficients | Gray Relational Grades | ||
---|---|---|---|---|---|
SR | MRR | SR | MRR | ||
1 | 0.4935 | 1.0000 | 0.5033 | 0.3333 | 0.4105 |
2 | 0.6244 | 0.3610 | 0.4447 | 0.5807 | 0.5190 |
3 | 0.9022 | 0.2835 | 0.3566 | 0.6382 | 0.5103 |
4 | 0.3562 | 0.4209 | 0.5840 | 0.5430 | 0.5616 |
5 | 1.0000 | 0.7780 | 0.3333 | 0.3912 | 0.3649 |
6 | 0.9534 | 0.5655 | 0.3440 | 0.4693 | 0.4124 |
7 | 0.4602 | 0.3975 | 0.5207 | 0.5571 | 0.5406 |
8 | 0.4852 | 0.6304 | 0.5075 | 0.4423 | 0.4719 |
9 | 0.4047 | 0.1149 | 0.5527 | 0.8132 | 0.6949 |
10 | 0.0000 | 0.7411 | 1.0000 | 0.4029 | 0.6740 |
11 | 0.4852 | 0.0000 | 0.5075 | 1.0000 | 0.7014 |
12 | 0.1874 | 0.2458 | 0.7274 | 0.6704 | 0.6963 |
13 | 0.7281 | 0.1425 | 0.4071 | 0.7782 | 0.6097 |
14 | 0.4433 | 0.5253 | 0.5300 | 0.4877 | 0.5069 |
15 | 0.5181 | 0.3503 | 0.4911 | 0.5880 | 0.5440 |
Principal Component | Eigenvalue | Contribution |
---|---|---|
SR | 1.0924 | 54.6% |
MRR | 0.90476 | 45.4% |
Total | 100% |
Initial Factor Setting | Optimal Process Condition | Improvement | ||
---|---|---|---|---|
Prediction | Validation | |||
vf | 200 mm/min | 232.09 mm/min | 232.09 mm/min | |
ap | 0.08 mm | 0.08 mm | 0.08 mm | |
vs | 16 m/s | 16 m/s | 16 m/s | |
SR | 0.413 μm | 0.387 μm | 6.29% | |
MRR | 0.0701 mm²/min | 0.0814 mm²/min | 16.11% | |
GRG | 0.7014 | 0.7841 | 0.7783 | 10.96% |
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Share and Cite
Guo, J.; Shi, Y.; Chen, Z.; Yu, T.; Zhao, P.; Shirinzadeh, B. Optimal Parameter Selection in Robotic Belt Polishing for Aeroengine Blade Based on GRA-RSM Method. Symmetry 2019, 11, 1526. https://doi.org/10.3390/sym11121526
Guo J, Shi Y, Chen Z, Yu T, Zhao P, Shirinzadeh B. Optimal Parameter Selection in Robotic Belt Polishing for Aeroengine Blade Based on GRA-RSM Method. Symmetry. 2019; 11(12):1526. https://doi.org/10.3390/sym11121526
Chicago/Turabian StyleGuo, Jian, Yaoyao Shi, Zhen Chen, Tao Yu, Pan Zhao, and Bijan Shirinzadeh. 2019. "Optimal Parameter Selection in Robotic Belt Polishing for Aeroengine Blade Based on GRA-RSM Method" Symmetry 11, no. 12: 1526. https://doi.org/10.3390/sym11121526