Rapid Assessment of Steel Machinability through Spark Analysis and Data-Mining Techniques
Abstract
:1. Introduction
2. Materials and Methods
2.1. Steel Samples
2.2. Experimental Setup
- Rotational speed of the grinding wheel: 400 RPM;
- Feed rate: 250 mm/s;
- Cutting length: 15 mm.
2.3. Machine Vision System
- Shutter Speed [s]: 1/160;
- Aperture: f/8;
- ISO Speed: 200;
- White Balance [K]: 2500.
2.4. Image Preprocessing Methods
2.5. CNNs for Machinability Prediction
3. Results
3.1. Statistical Performance of ResNet18_Small
3.2. Statistical Performance of ResNet18_Big
3.3. Comparative Analysis
3.4. Observations and Trends
4. Discussion
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Sample Index | Heat | Quality | HBW | V15 [m/min] | Machining Time [min] |
---|---|---|---|---|---|
1 | 92369 | 42CrMoS4 | 180 | 299 | 14.87 |
2 | 93137 | 16MnCrS5 | 143 | 411 | 15.12 |
3 | 93541 | 42CrMoS4 | 280 | 207 | 14.10 |
4 | 92047 | C45 | 186 | 389 | 20.56 |
5 | 92718 | 16MnCrS5 | 151 | 416 | 15.83 |
6 | 92249 | 42CrMoS4 | 257 | 229 | 21.25 |
7 | 92456 | C45 | 151 | 377 | 18.03 |
8 | 92331 | 20MnV6 | 210 | 411 | 16.66 |
9 | 92145 | 20MnV6 | 199 | 401 | 15.19 |
10 | 92796 | 42CrMoS4 | 198 | 236 | 23.98 |
11 | 91678 | C45 | 190 | 385 | 19.71 |
12 | 91688 | 30CrNiMo8 | 210 | 256 | 16.50 |
13 | 92330 | 20MnV6 | 206 | 405 | 15.73 |
14 | 93511 | C45 | 192 | 384 | 19.36 |
15 | 93677 | C45 | 189 | 382 | 18.97 |
16 | 93706 | 34CrNiMo6 | 206 | 266 | 16.55 |
17 | 91461 | C45/75 | 187 | 382 | 18.97 |
18 | 93644 | C45/75 | 183 | 374 | 17.48 |
19 | 93516 | 16MnCrS5 | 151 | 423 | 17.00 |
20 | 92147 | 16MnCrS5 | 157 | 433 | 18.76 |
21 | 91959 | C45 | 184 | 393 | 21.37 |
22 | 92613 | 16NiCrS4 | 189 | 476 | 18.78 |
23 | 92251 | 16MnCrS5 | 144 | 418 | 16.27 |
24 | 91958 | C45 | 192 | 374 | 17.48 |
25 | 91943 | 16MnCrS5 | 154 | 429 | 18.06 |
26 | 93648 | 42CrMoS4 | 212 | 227 | 20.49 |
27 | 91792 | 20MnV6 | 206 | 360 | 9.82 |
28 | 92623 | 20MnV6 | 206 | 401 | 15.19 |
29 | 92328 | 20MnV6 | 208 | 397 | 14.61 |
30 | 93696 | 34CrNiMo6 | 226 | 271 | 17.72 |
31 | 93647 | 42CrMoS4 | 276 | 222 | 18.71 |
32 | 92252 | 16MnCrS5 | 152 | 423 | 17.00 |
33 | 92329 | 20MnV6 | 202 | 405 | 15.73 |
34 | 93746 | 16MnCrS5 | 158 | 423 | 17.00 |
35 | 92065 | 20MnV6 | 202 | 408 | 16.22 |
36 | 93802 | 16MnCrS5 | 153 | 437 | 19.44 |
37 | 92715 | 42CrMoS4 | 214 | 229 | 21.25 |
38 | 93518 | 16MnCrS5 | 156 | 433 | 18.76 |
39 | 93873 | 34CrNiMo6 | 209 | 266 | 16.55 |
40 | 91721 | 38MnVS6 | 277 | 249 | 14.85 |
41 | 91724 | C45 | 194 | 374 | 17.48 |
42 | 91942 | 16MnCrS5 | 155 | 421 | 16.66 |
43 | 92624 | 20MnV6 | 207 | 408 | 16.22 |
44 | 92412 | C45 | 213 | 374 | 17.48 |
45 | 92781 | 42CrMoS4 | 209 | 239 | 25.36 |
Training Option | Value |
---|---|
Solver type | SGDM |
Learning rate | 0.000001 |
Mini batch size | 32 |
Validation frequency | 20 |
Shuffle | Every epoch |
Fold Number | Steel Samples | ||||
---|---|---|---|---|---|
Fold 1 | 92369 (42CrMoS4) | 93137(16MnCrS5) | 93541(42CrMoS4) | 92047(C45) | 92718(16MnCrS5) |
Fold 2 | 92249 (42CrMoS4) | 92456 (C45) | 92331 (20MnV6) | 92145 (20MnV6) | 92796 (42CrMoS4) |
Fold 3 | 91678 (C45) | 91688 (30CrNiMo8) | 93511 (C45) | 93677 (C45) | / |
Fold 4 | 93706 (34CrNiMo6) | 91461 (C45) | 93644 (C45) | 93516 (16MnCrS5) | 92147 (16MnCrS5) |
Fold 5 | 91959 (C45) | 92613 (16NiCrS4) | 92251 (16MnCrS5) | 91943 (16MnCrS5) | / |
Fold 6 | 93648 (42CrMoS4) | 91792 (20MnV6) | 92623 (20MnV6) | 92328 (20MnV6) | / |
Fold 7 | 93647 (42CrMoS4) | 92252 (16MnCrS5) | 92329 (20MnV6) | 93746 (16MnCrS5) | 92065 (20MnV6) |
Fold 8 | 92715 (42CrMoS4) | 93873 (34CrNiMo6) | 91721 (38MnVS6) | 91958 (C45) | / |
Fold 9 | 91724 (C45) | 91942 (16MnCrS5) | 92624 (20MnV6) | 92412 (C45) | 92781 (42CrMoS4) |
Fold 10 | 93802 (16MnCrS5) | 93518 (16MnCrS5) | 92330 (20MnV6) | 93696 (34CrNiMo6) | / |
Fold Number | RMSE [m/min] | MAPE [%] |
---|---|---|
Fold1 | 45.58 | 14.04 |
Fold2 | 63.42 | 22.90 |
Fold3 | 69.16 | 17.58 |
Fold4 | 45.62 | 10.33 |
Fold5 | 47.36 | 10.23 |
Fold6 | 59.92 | 22.93 |
Fold7 | 32.96 | 10.81 |
Fold8 | 68.08 | 23.29 |
Fold9 | 63.66 | 16.49 |
Fold10 | 34.36 | 7.58 |
Average | 53.01 | 15.62 |
Fold Number | RMSE [m/mn] | MAPE [%] |
---|---|---|
Fold1 | 40.55 | 13.86 |
Fold2 | 54.63 | 18.39 |
Fold3 | 56.33 | 13.54 |
Fold4 | 35.02 | 8.08 |
Fold5 | 41.78 | 8.58 |
Fold6 | 61.76 | 23.33 |
Fold7 | 29.35 | 9.07 |
Fold8 | 50.24 | 17.93 |
Fold9 | 36.26 | 8.74 |
Fold10 | 33.36 | 7.25 |
Average | 43.93 | 12.88 |
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Munđar, G.; Kovačič, M.; Brezočnik, M.; Stępień, K.; Župerl, U. Rapid Assessment of Steel Machinability through Spark Analysis and Data-Mining Techniques. Metals 2024, 14, 955. https://doi.org/10.3390/met14080955
Munđar G, Kovačič M, Brezočnik M, Stępień K, Župerl U. Rapid Assessment of Steel Machinability through Spark Analysis and Data-Mining Techniques. Metals. 2024; 14(8):955. https://doi.org/10.3390/met14080955
Chicago/Turabian StyleMunđar, Goran, Miha Kovačič, Miran Brezočnik, Krzysztof Stępień, and Uroš Župerl. 2024. "Rapid Assessment of Steel Machinability through Spark Analysis and Data-Mining Techniques" Metals 14, no. 8: 955. https://doi.org/10.3390/met14080955