Prediction of In-Flight Particle Properties and Mechanical Performances of HVOF-Sprayed NiCr–Cr3C2 Coatings Based on a Hierarchical Neural Network
Abstract
:1. Introduction
2. Experimental Procedures
2.1. HVOF Spray Process Parameters
2.2. Coating Microstructure Characterization
3. Hierarchical PINN-CNN Models and Their Implementations
3.1. Feature Selection Based on the Random Forest (RF) Model
3.2. Data Collection and Preprocessing
3.3. PINN Model: First-Layer Building, Training, and Validation
3.4. CNN Model: Building, Training, and Validation of the Second Layer
4. Results and Discussion
4.1. Analysis of RF Feature Selection Results
4.2. Analysis of the PINN Training Results
4.3. Analysis of the CNN Training Results
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Parameters | Scope |
---|---|
O2 flow (slpm) | 200–240 |
CH4 flow (slpm) | 120–200 |
Air flow (slpm) | 300 |
Carrier gas flow (slpm) | 40 |
stand-off distance (mm) | 200–320 |
Spray gun speed (mm/s) | 400 |
Powder feeding speed (g/min) | 30 |
No. | Q(CH4) (slpm) | SOD (mm) | Q(O2) (slpm) | V (m/s) | T (K) | MH (HV0.3) | WR × 10−5 (mm3/N/m) | PO (% Area) |
---|---|---|---|---|---|---|---|---|
1 | 120 | 200 | 200 | 467 | 2223 | 664 | 6.039 | 1.08 |
2 | 120 | 200 | 240 | 508 | 2239 | 721 | 2.962 | 0.206 |
3 | 120 | 240 | 200 | 389 | 2234 | 598 | 10.587 | 0.731 |
4 | 120 | 240 | 240 | 423 | 2256 | 637 | 6.585 | 1.597 |
5 | 120 | 280 | 200 | 292 | 2455 | 565 | 15.589 | 1.473 |
6 | 120 | 280 | 240 | 301 | 2457 | 543 | 10.249 | 1.558 |
7 | 120 | 320 | 200 | 269 | 2395 | 469 | 5.061 | 1.113 |
8 | 120 | 320 | 240 | 274 | 2405 | 483 | 15.732 | 0.883 |
9 | 140 | 200 | 200 | 476 | 2186 | 661 | 5.175 | 1.159 |
10 | 140 | 200 | 240 | 509 | 2204 | 843 | 1.624 | 0.151 |
11 | 140 | 240 | 200 | 409 | 2201 | 707 | 7.142 | 0.863 |
12 | 140 | 240 | 240 | 450 | 2233 | 738 | 2.802 | 1.274 |
13 | 140 | 280 | 200 | 298 | 2432 | 592 | 7.122 | 1.372 |
14 | 140 | 280 | 240 | 312 | 2460 | 659 | 4.203 | 0.871 |
15 | 140 | 320 | 200 | 270 | 2388 | 502 | 9.154 | 0.915 |
16 | 140 | 320 | 240 | 281 | 2432 | 598 | 9.927 | 0.721 |
17 | 160 | 200 | 200 | 468 | 2139 | 727 | 1.516 | 0.204 |
18 | 160 | 200 | 240 | 531 | 2170 | 958 | 1.197 | 0.361 |
19 | 160 | 240 | 200 | 404 | 2151 | 625 | 10.144 | 1.134 |
20 | 160 | 240 | 240 | 462 | 2204 | 832 | 2.926 | 0.444 |
21 | 160 | 280 | 200 | 300 | 2390 | 706 | 6.122 | 1.777 |
22 | 160 | 280 | 240 | 328 | 2453 | 759 | 4.467 | 0.996 |
23 | 160 | 320 | 200 | 265 | 2350 | 544 | 9.698 | 0.671 |
24 | 160 | 320 | 240 | 278 | 2423 | 596 | 5.54 | 0.803 |
25 | 180 | 200 | 200 | 461 | 2099 | 699 | 2.217 | 0.182 |
26 | 180 | 200 | 240 | 518 | 2120 | 893 | 0.926 | 0.655 |
27 | 180 | 240 | 200 | 400 | 2127 | 628 | 9.235 | 0.237 |
28 | 180 | 240 | 240 | 463 | 2167 | 833 | 2.221 | 0.299 |
29 | 180 | 280 | 200 | 314 | 2400 | 624 | 10.172 | 1.697 |
30 | 180 | 280 | 230 | 317 | 2433 | 618 | 4.997 | 1.03 |
31 | 180 | 320 | 200 | 263 | 2341 | 541 | 7.693 | 0.856 |
32 | 180 | 320 | 233 | 273 | 2406 | 597 | 6.941 | 0.995 |
33 | 186 | 200 | 240 | 515 | 2096 | 781 | 1.764 | 0.144 |
34 | 188 | 240 | 240 | 468 | 2145 | 802 | 1.59 | 0.576 |
35 | 200 | 200 | 200 | 455 | 2064 | 678 | 1.944 | 0.799 |
36 | 200 | 240 | 200 | 399 | 2097 | 650 | 6.955 | 0.266 |
37 | 200 | 280 | 200 | 297 | 2354 | 604 | 11.741 | 1.231 |
38 | 200 | 280 | 225 | 306 | 2406 | 678 | 4.001 | 0.83 |
39 | 200 | 320 | 200 | 254 | 2269 | 538 | 8.92 | 0.977 |
40 | 200 | 320 | 230 | 269 | 2368 | 578 | 6.881 | 0.87 |
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Gui, L.; Wang, B.; Cai, R.; Yu, Z.; Liu, M.; Zhu, Q.; Xie, Y.; Liu, S.; Killinger, A. Prediction of In-Flight Particle Properties and Mechanical Performances of HVOF-Sprayed NiCr–Cr3C2 Coatings Based on a Hierarchical Neural Network. Materials 2023, 16, 6279. https://doi.org/10.3390/ma16186279
Gui L, Wang B, Cai R, Yu Z, Liu M, Zhu Q, Xie Y, Liu S, Killinger A. Prediction of In-Flight Particle Properties and Mechanical Performances of HVOF-Sprayed NiCr–Cr3C2 Coatings Based on a Hierarchical Neural Network. Materials. 2023; 16(18):6279. https://doi.org/10.3390/ma16186279
Chicago/Turabian StyleGui, Longen, Botong Wang, Renye Cai, Zexin Yu, Meimei Liu, Qixin Zhu, Yingchun Xie, Shaowu Liu, and Andreas Killinger. 2023. "Prediction of In-Flight Particle Properties and Mechanical Performances of HVOF-Sprayed NiCr–Cr3C2 Coatings Based on a Hierarchical Neural Network" Materials 16, no. 18: 6279. https://doi.org/10.3390/ma16186279
APA StyleGui, L., Wang, B., Cai, R., Yu, Z., Liu, M., Zhu, Q., Xie, Y., Liu, S., & Killinger, A. (2023). Prediction of In-Flight Particle Properties and Mechanical Performances of HVOF-Sprayed NiCr–Cr3C2 Coatings Based on a Hierarchical Neural Network. Materials, 16(18), 6279. https://doi.org/10.3390/ma16186279