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Reliable roll force prediction in cold mill using multiple neural networks.

S Cho¹, Y Cho, S Yoon

¹Dept. of Comput. Sci., Pohang Univ. of Sci. and Technol.

IEEE Transactions on Neural Networks

|January 1, 1997

Summary

This summary is machine-generated.

Accurate steel cold rolling requires precise roll force prediction. This study uses two neural networks to enhance existing models, improving prediction accuracy by up to 50% for better product quality.

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Area of Science:

Materials Science
Mechanical Engineering
Artificial Intelligence

Background:

The cold rolling mill process is crucial for achieving desired steel strip thickness.
Accurate roll force prediction is vital for ensuring product quality in steel manufacturing.
Current mathematical models for roll force prediction are suboptimal.

Purpose of the Study:

To improve the accuracy of roll force prediction in cold rolling mills.
To develop a more reliable system by enhancing existing mathematical models.
To leverage artificial intelligence for optimizing steel production processes.

Main Methods:

Training two multilayer perceptrons (MLPs) for roll force prediction.
One MLP directly predicts roll force; the second calculates a corrective coefficient.
Integrating MLPs with a suboptimal mathematical model for enhanced reliability.

Main Results:

Both MLPs individually improved prediction accuracy by 30-50%.
The combined system, integrating MLPs with the mathematical model, demonstrated significantly improved reliability.
Neural network integration offers a substantial advancement over traditional modeling.

Conclusions:

Artificial intelligence, specifically MLPs, can significantly enhance the accuracy of roll force prediction.
Combining predictive models with corrective MLPs offers a robust solution for industrial applications.
The developed system improves reliability and product quality in steel cold rolling.