Paper Titles
Preface
Advanced Control of Stretch-Reducing Mills Using Artificial Intelligence
p.1
Proxy-Physics-Informed and Transfer Learning Networks for Radial-Axial Ring Rolling (RARR) under Varying Data Availability
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Material Parameter Identification Using Bayesian Data Assimilation and Biaxial Tensile Test
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Innovation of the “Cut-Clamp-Play” Concept for Robust and Efficient Sheet Metal Material Characterization
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Method of an Automated Tool Design of Punch-Bending Processes Using the Asset Administration Shell
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Uncertainty Detection in Sheet Metal Bending Processes with Machine Learning
p.55
AI-Driven Design and Optimization of Bending Processes
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Hybrid Numerical and Data-Driven Modelling for Defect Prediction in Screw Press Hot Bulk Forging of the En AW-6060 Part
p.75

Advanced Control of Stretch-Reducing Mills Using Artificial Intelligence

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Abstract:

The stretch-reducing mill is a forming process to manufacture tubes by progressive metal deformation. This process is characterized by high complexity and a high number of variables that are strongly interconnected. To overcome the limitations and substantial simplifications of the traditional modelling and offer higher flexibility and suitability for real-time control, an Artificial Neural Networks approach is employed. By defining three parallel networks, they were predicted the milling status, the tube thickness and the angular speeds of the stands composing the process. With the models results, an optimization algorithm is employed to determine the best configuration of angular speeds of the stands to obtain a defined final tube thickness. The Artificial Neural Networks show extremely low RMSE across training, validation, and test sets, confirming their ability to model complex nonlinear dependencies. The optimisation stage reaches the target thickness with only 0.0079% error while preventing unstable operating conditions. The overall methodology provides a tool for implementing the intelligent and data-driven control of the stretch-reducing mill.

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Periodical:

Key Engineering Materials (Volume 1049)

Pages:

1-11

DOI:

https://doi.org/10.4028/p-5o49gU

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Cite this paper

Online since:

April 2026

Authors:

Laura Antonini*, Mariangela Quarto, Claudio Giardini, Giuseppe Pellegrini, Marco Zambelli

Keywords:

Artificial Neural Network, Intelligent Process Control, Stretch-Reducing Mill

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* - Corresponding Author

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