Extreme gradient boosting (XGB)-driven simulator for radial-axial force estimation in ring rolling
Mattia Perin, Guido Berti
Abstract. Force estimation in metal forming processes is crucial for identifying the correct machine during production and determining the best process parameters to achieve good quality, reduce scrap and minimize energy consumption. In the hot Radial-Axial Ring Rolling (RARR) process force estimation is normally carried out by highly time-consuming finite element analysis (FEA) or empiric-analytical models. To achieve a reliable and yet real-time estimation of the process loads, this research presents a freeware software based on a Python script, to estimate mandrel and axial roll forces based on the initial/final geometry of the ring, its material and temperature, process time and tools motion laws’ initial and final values. The Mandrel Force (FM) and Axial roll Force (FA) forces are predicted from 10% to 100% of the process time, excluding the calibration phase. The prediction algorithm is based on the Extreme Gradient Boosting (XGB) ensemble algorithm, trained and validated with FEA and experimental data. A total of 50 cases belonging to rings with the final outer diameter ranging between 429 and 946 mm were gathered and standardized, and data augmentation was carried out to improve the robustness of the model. Training and validation sets are randomly constructed, employing a 5-fold cross-validation technique to ensure uniformity of the database and reliability of the results. During the validation phase, the prediction of the mandrel force and axial roll force showed an average error of 8.2% and 9.9% respectively. A simple Graphic User Interface (GUI) has been developed to easily utilize the algorithm to predict the mandrel and axial roll forces over the completion percentage. The proposed methodology aims to answer the call for agile, user-friendly and reliable analysis tools to be employed directly at manufacturing plants, limiting the utilization of FEA simulations to highly complex scenarios, and thus enhancing global efficiency.
Keywords
Ring Rolling, Radial Force, Axial Force, Extreme Gradient Boosting
Published online 5/7/2025, 10 pages
Copyright © 2025 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA
Citation: Mattia Perin, Guido Berti, Extreme gradient boosting (XGB)-driven simulator for radial-axial force estimation in ring rolling, Materials Research Proceedings, Vol. 54, pp 839-848, 2025
DOI: https://doi.org/10.21741/9781644903599-90
The article was published as article 90 of the book Material Forming
Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
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