Optimization of Open Die Ironing Process through Artificial Neural Network for Rapid Process Simulation
Abstract
:1. Introduction
2. Methods
2.1. FEM Model for Open-Die Forging of 42crmo4 Steel Grade
2.2. Analytical Model for Open Die Process Simulation
- C1 and C2 represent the middle points of growth and decay phase respectively. In order to identify the inflection point for each sigmoidal branch, an analysis has been carried out on FEM results, identifying the point with the 50% of the maximum plastic strain at the core fiber.
- D1 and D2 represent the slopes of the growth and decay branches of the function.
- M is a multiplier coefficient. The Equation (1) varies in a range between 0 and 2, thus the coefficients M brings the maximum of double-sigmoidal curve to the maximum of plastic strain.
2.3. Forecasting Models Based on Artificial Neural Networks (ANNs)
- Training: about 400 examples;
- Validation: about 150 examples;
- Test: about 50 examples.
3. Results and Discussion
4. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Forging Parameter | Minimum Value | Maximum Value | Step |
---|---|---|---|
Temperature [°C] | 800 | 1200 | 100 |
Sb0 [mm] | 150 | 750 | 150 |
Reduction [%] | 5.0 | 25.0 | 2.5 |
ΔSb0 [%] (Pitch [%] respect Sb0) | 10 (90%) | 50 (50%) | (20%) |
Ingot initial diameter [mm] | 300 | 1500 | 300 |
Variable | Min Value | Max Value | Min Normalized Value | Max Normalized Value |
---|---|---|---|---|
Sb0 | 75 | 750 | 0.1 | 0.9 |
Temperature | 800 | 1200 | 0.1 | 0.9 |
Reduction [%] | 5 | 25 | 0.1 | 0.9 |
C1 | −384 | −28 | 0.1 | 0.9 |
D1 | 19 | 60 | 0.1 | 0.9 |
C2 | 9.5 | 350 | 0.1 | 0.9 |
D2 | 27 | 60 | 0.1 | 0.9 |
M | 0 | 0.24 | 0.1 | 0.9 |
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Mancini, S.; Langellotto, L.; Zangari, G.; Maccaglia, R.; Schino, A.D. Optimization of Open Die Ironing Process through Artificial Neural Network for Rapid Process Simulation. Metals 2020, 10, 1397. https://doi.org/10.3390/met10101397
Mancini S, Langellotto L, Zangari G, Maccaglia R, Schino AD. Optimization of Open Die Ironing Process through Artificial Neural Network for Rapid Process Simulation. Metals. 2020; 10(10):1397. https://doi.org/10.3390/met10101397
Chicago/Turabian StyleMancini, Silvia, Luigi Langellotto, Giovanni Zangari, Riccardo Maccaglia, and Andrea Di Schino. 2020. "Optimization of Open Die Ironing Process through Artificial Neural Network for Rapid Process Simulation" Metals 10, no. 10: 1397. https://doi.org/10.3390/met10101397