Reliability Prediction of Near-Isothermal Rolling of TiAl Alloy Based on Five Neural Network Models
<p>The relationship between the sub-models in the mechanism prediction model. Pink line is Thermal expansion boundary. Blue line is Roll wear.</p> "> Figure 2
<p>Bounce curve.</p> "> Figure 3
<p>Finite element model.</p> "> Figure 4
<p>Longitudinal deformation diagram.</p> "> Figure 5
<p>Rolling plate thickness.</p> "> Figure 6
<p>Optimization process of GA.</p> "> Figure 7
<p>Controllable two-roll mill.</p> "> Figure 8
<p>(<b>a</b>–<b>d</b>) Database scatter plot.</p> "> Figure 8 Cont.
<p>(<b>a</b>–<b>d</b>) Database scatter plot.</p> "> Figure 9
<p>Predictive flow chart.</p> "> Figure 10
<p>Training performance comparison.</p> "> Figure 11
<p>Maximum and minimum prediction error distribution.</p> "> Figure 12
<p>Fitness curve.</p> "> Figure 13
<p>GANN model training process.</p> "> Figure 14
<p>GANN model regression curve.</p> "> Figure 15
<p>GANN model prediction curve.</p> "> Figure 16
<p>TiAl alloy specimen after near-isothermal rolling.</p> ">
Abstract
:1. Introduction
2. Theoretical Model of Strip Thickness
2.1. The Mill Bounces
2.2. Mill Wear Compensation Value
2.3. Roll Thermal Expansion Compensation Value
3. Finite Element Model
4. Artificial Neural Network Prediction Model
4.1. Introduction to Genetic Algorithm
4.2. Determination of the Neural Network Input Parameters
4.3. Determination of the Number of Hidden Layer Units in Neural Network
5. Analysis and Discussion of Prediction Model
6. Conclusions
- (1)
- After comparison, the GANN model has high prediction accuracy, with a maximum prediction error of only 0.18 mm, an average prediction error of 0.05 mm, and a root mean square error of 0.0064;
- (2)
- The GANN model has the function of online monitoring, with a training time of 1.8457 ± 1.2359 s and a testing time of 0.01284 ± 0.01157 s;
- (3)
- As the rolling speed increases, the entrance temperature increases, the stiffness coefficient increases, and the outlet thickness decreases;
- (4)
- Neural networks have the characteristic of fast response, and using this powerful tool to create more intelligent devices for human society will have a significant impact on the future.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Input Parameters | Gray Relation |
---|---|
Rolling speed | 0.984856 |
Inlet temperature | 0.986253 |
Stiffness Coefficient | 0.995423 |
Reduction rate | 0.99867 |
Number of Hidden Layer Elements | Total Error |
---|---|
1 | 1.1974 |
2 | 0.7901 |
3 | 0.5174 |
4 | 0.5815 |
5 | 0.4809 |
6 | 0.4458 |
7 | 0.5813 |
8 | 0.5776 |
9 | 0.5702 |
10 | 0.5977 |
Algorithm | Transfer Functions |
---|---|
SNN | Compet-logsig-poslin |
DNN | Purelin-radbas-logsig |
DBN | Satlins-purelin-logsig |
GANN | Trainlm-tansig-purelin |
FNN | Radbas-satlins-compet |
Algorithm | RMSE |
---|---|
SNN | 0.0168 |
DNN | 0.0154 |
DBN | 0.0132 |
GANN | 0.0064 |
FNN | 0.0153 |
Algorithm | Training Time (s) | Testing Time (s) |
---|---|---|
SNN | 1.8569 ± 1.3547 | 0.01352 ± 0.01536 |
DNN | 7.6524 ± 3.6528 | 0.02549 ± 0.01626 |
DBN | 50.3521 ± 5.2169 | 0.01529 ± 0.01752 |
GANN | 1.8457 ± 1.2359 | 0.01284 ± 0.01157 |
FNN | 10.5821 ± 2.5674 | 0.02463 ± 0.01784 |
Algorithm | Max Prediction Error (mm) | Total Forecast Error (mm) |
---|---|---|
DBN | 0.1911 | 4.5776 |
DNN | 0.2934 | 8.0541 |
SNN | 0.3203 | 8.0829 |
GANN | 0.1838 | 4.4577 |
FNN | 0.3191 | 8.9736 |
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Lian, W.; Du, F. Reliability Prediction of Near-Isothermal Rolling of TiAl Alloy Based on Five Neural Network Models. Materials 2023, 16, 6709. https://doi.org/10.3390/ma16206709
Lian W, Du F. Reliability Prediction of Near-Isothermal Rolling of TiAl Alloy Based on Five Neural Network Models. Materials. 2023; 16(20):6709. https://doi.org/10.3390/ma16206709
Chicago/Turabian StyleLian, Wei, and Fengshan Du. 2023. "Reliability Prediction of Near-Isothermal Rolling of TiAl Alloy Based on Five Neural Network Models" Materials 16, no. 20: 6709. https://doi.org/10.3390/ma16206709