Bayesian Optimized Deep Convolutional Network for Electrochemical Drilling Process
Abstract
:1. Introduction
2. Deep Convolutional Network Prediction Model for ECM
2.1. Deep Convolutional Network
2.2. Bayesian Optimization of Hidden Layers with Gaussian Process Priors
3. Experimental Study
4. Results
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
Appendix A
No. | Voltage (V) | Pulse-On Time (µs) | Feed Rate (µm/s) | Din (µm) | Dout (µm) | Taper | Overcut (µm) |
---|---|---|---|---|---|---|---|
1 | 16 | 25 | 8 | 893 | 860 | 0.066 | 3.5 |
2 | 18 | 25 | 8 | 929 | 913 | 0.032 | 14.5 |
3 | 20 | 25 | 8 | 923 | 910 | 0.026 | 11.5 |
4 | 16 | 25 | 6 | 904 | 892 | 0.024 | 2 |
5 | 18 | 25 | 6 | 934 | 931 | 0.006 | 17 |
6 | 20 | 25 | 6 | 999 | 977 | 0.044 | 49.5 |
7 | 16 | 25 | 4 | 983 | 979 | 0.008 | 41.5 |
8 | 18 | 25 | 4 | 1050 | 1045 | 0.01 | 75 |
9 | 20 | 25 | 4 | 1125 | 1123 | 0.004 | 112.5 |
10 | 8 | 50 | 8 | 657.5 | 627.5 | 0.06 | 121.25 |
11 | 10 | 50 | 8 | 809.5 | 807.25 | 0.0045 | 45.25 |
12 | 12 | 50 | 8 | 866.25 | 858 | 0.0165 | 16.875 |
13 | 8 | 50 | 6 | 760 | 741 | 0.038 | 70 |
14 | 10 | 50 | 6 | 828.5 | 829.5 | 0.002 | 35.75 |
15 | 12 | 50 | 6 | 908.75 | 905.5 | 0.0065 | 4.375 |
16 | 8 | 50 | 4 | 781.75 | 780.25 | 0.003 | 59.125 |
17 | 10 | 50 | 4 | 887.25 | 881.75 | 0.011 | 6.375 |
18 | 12 | 50 | 4 | 957.75 | 970 | 0.0245 | 28.875 |
19 | 8 | 60 | 8 | 771.33 | 759.33 | 0.024 | 64.335 |
20 | 10 | 60 | 8 | 806.75 | 799.5 | 0.0145 | 46.625 |
21 | 12 | 60 | 8 | 862.75 | 847 | 0.0315 | 18.625 |
22 | 8 | 60 | 6 | 756.5 | 739.75 | 0.0335 | 71.75 |
23 | 10 | 60 | 6 | 776.75 | 777.5 | 0.0015 | 61.625 |
24 | 12 | 60 | 6 | 840.25 | 841.25 | 0.002 | 29.875 |
25 | 8 | 60 | 4 | 769 | 771.5 | 0.005 | 65.5 |
26 | 10 | 60 | 4 | 854.75 | 865.25 | 0.021 | 22.625 |
27 | 12 | 60 | 4 | 928.25 | 945.5 | 0.0345 | 14.125 |
28 | 8 | 70 | 8 | 718 | 721.5 | 0.007 | 91 |
29 | 10 | 70 | 8 | 779 | 796.75 | 0.0355 | 60.5 |
30 | 12 | 70 | 8 | 841.5 | 849.75 | 0.0165 | 29.25 |
31 | 8 | 70 | 6 | 736.5 | 744.5 | 0.016 | 81.75 |
32 | 10 | 70 | 6 | 802 | 829.75 | 0.0555 | 49 |
33 | 12 | 70 | 6 | 858.75 | 865 | 0.0125 | 20.625 |
34 | 8 | 70 | 4 | 783.25 | 783.25 | 0 | 58.375 |
35 | 10 | 70 | 4 | 878.75 | 872 | 0.0135 | 10.625 |
36 | 12 | 70 | 4 | 946.25 | 955.25 | 0.018 | 23.125 |
37 | 8 | 50 | 8 | 874 | 704 | 0.34 | 13 |
38 | 9 | 50 | 8 | 914 | 789 | 0.25 | 7 |
39 | 10 | 50 | 8 | 999 | 827 | 0.344 | 49.5 |
40 | 8 | 50 | 6 | 922 | 765 | 0.314 | 11 |
41 | 9 | 50 | 6 | 955 | 807 | 0.296 | 27.5 |
42 | 10 | 50 | 6 | 1039 | 837 | 0.404 | 69.5 |
43 | 8 | 50 | 4 | 932 | 797 | 0.27 | 16 |
44 | 9 | 50 | 4 | 1044 | 790 | 0.508 | 72 |
45 | 10 | 50 | 4 | 1130 | 858 | 0.544 | 115 |
46 | 8 | 60 | 8 | 903 | 708 | 0.39 | 1.5 |
47 | 9 | 60 | 8 | 967 | 766 | 0.402 | 33.5 |
48 | 10 | 60 | 8 | 1084 | 817 | 0.534 | 92 |
49 | 8 | 60 | 6 | 917 | 760 | 0.314 | 8.5 |
50 | 9 | 60 | 6 | 1043 | 856 | 0.374 | 71.5 |
51 | 10 | 60 | 6 | 1115 | 871 | 0.488 | 107.5 |
52 | 8 | 60 | 4 | 1071 | 754 | 0.634 | 85.5 |
53 | 9 | 60 | 4 | 1087 | 972 | 0.23 | 93.5 |
54 | 10 | 60 | 4 | 1263 | 1044 | 0.438 | 181.5 |
55 | 8 | 70 | 8 | 875 | 789 | 0.172 | 12.5 |
56 | 9 | 70 | 8 | 1071 | 842 | 0.458 | 85.5 |
57 | 10 | 70 | 8 | 1158 | 862 | 0.592 | 129 |
58 | 8 | 70 | 6 | 987 | 846 | 0.282 | 43.5 |
59 | 9 | 70 | 6 | 1212 | 886 | 0.652 | 156 |
60 | 10 | 70 | 6 | 1243 | 1056 | 0.374 | 171.5 |
61 | 8 | 70 | 4 | 1134 | 877 | 0.514 | 117 |
62 | 9 | 70 | 4 | 1260 | 935 | 0.65 | 180 |
63 | 10 | 70 | 4 | 1348 | 1016 | 0.664 | 224 |
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Validation Split | Drop-Out Layer Ratio | Dense Layer 1 Neurons | Dense Layer 2 Neurons | Dense Layer 3 Neurons | Training Iteration | |
---|---|---|---|---|---|---|
Range | 0–0.2 | 0–0.3 | 3–8 | 3–8 | 3–8 | 50–400 in increments of 50 |
Data Type | Continuous | Continuous | Discrete | Discrete | Discrete | Discrete |
Validation Split | Drop-Out Layer Ratio | Dense Layer 1 Neurons | Dense Layer 2 Neurons | Dense Layer 3 Neurons | Training Iteration | |
---|---|---|---|---|---|---|
Value | 89.5% training 10.5% validation | 5.23% | 8 | 7 | 6 | 400 |
Simulation # | Training MSE | Validation MSE | Training MAE | Validation MAE |
---|---|---|---|---|
1 | 0.0297 | 0.0329 | 0.135 | 0.143 |
2 | 0.0257 | 0.0219 | 0.125 | 0.119 |
3 | 0.0246 | 0.0235 | 0.121 | 0.121 |
4 | 0.0276 | 0.0261 | 0.131 | 0.129 |
5 | 0.0248 | 0.0207 | 0.122 | 0.115 |
Average | 0.0265 | 0.0250 | 0.127 | 0.125 |
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Lu, Y.; Wang, Z.; Xie, R.; Liang, S. Bayesian Optimized Deep Convolutional Network for Electrochemical Drilling Process. J. Manuf. Mater. Process. 2019, 3, 57. https://doi.org/10.3390/jmmp3030057
Lu Y, Wang Z, Xie R, Liang S. Bayesian Optimized Deep Convolutional Network for Electrochemical Drilling Process. Journal of Manufacturing and Materials Processing. 2019; 3(3):57. https://doi.org/10.3390/jmmp3030057
Chicago/Turabian StyleLu, Yanfei, Zengyan Wang, Rui Xie, and Steven Liang. 2019. "Bayesian Optimized Deep Convolutional Network for Electrochemical Drilling Process" Journal of Manufacturing and Materials Processing 3, no. 3: 57. https://doi.org/10.3390/jmmp3030057