Prediction of Final Rolling Temperature for TiAl Alloy Hot Rolling Based on Machine Learning
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View abstract on PubMed
Summary
This summary is machine-generated.Accurately predicting the final rolling temperature of titanium-aluminum (TiAl) alloys is crucial for material properties. A novel genetic algorithm-based BP neural network (GABP) model achieves high-precision temperature prediction, essential for aerospace applications.
Area Of Science
- Materials Science
- Metallurgy
- Artificial Intelligence
Background
- Final rolling temperature critically influences grain recrystallization and mechanical properties of materials.
- The aerospace industry demands high-quality TiAl alloys, necessitating precise control over their narrow and high-temperature rolling range.
- Traditional finite element analysis is too slow for real-time online monitoring of rolling temperatures.
Purpose Of The Study
- To develop an accurate and efficient model for predicting the final rolling temperature of TiAl alloys.
- To address the challenges of real-time temperature control in TiAl alloy rolling.
- To optimize the rolling plan and enable online control for improved TiAl alloy production.
Main Methods
- A genetic algorithm-based BP neural network (GABP) prediction model was proposed.
- MATLAB was utilized to analyze the influence of various factors on final rolling temperature to determine optimal neural network inputs.
- The GABP model's performance was compared against fuzzy neural networks (FNN).
Main Results
- The GABP model demonstrated high prediction accuracy, with errors primarily within 0-1 °C.
- The proposed GABP model significantly outperformed fuzzy neural networks (FNN) in prediction accuracy.
- The model effectively predicts the final rolling temperature of TiAl alloys.
Conclusions
- The GABP model offers a viable solution for accurate and real-time final rolling temperature prediction in TiAl alloys.
- This approach facilitates improved quality control and process optimization in TiAl alloy manufacturing.
- The developed model is suitable for online monitoring and control applications in the aerospace industry.
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