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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

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Abstract:

Numerical simulation, particularly through the Finite Element Method (FEM), is widely applied in the design and optimization of metal forming processes. However, certain real-process effects are not fully captured by numerical models alone, creating a need for complementary data-driven approaches. This study presents a hybrid modelling framework that integrates FEM simulations with machine-data-based predictive modelling to improve defect prediction in hot forging. Experimental data were collected from automated forging trials on an SMS SPPE 6.3 screw press equipped with position, force, strain, acceleration, and frame-deflection sensors. A series of trials with varied process parameters enabled the development of a data-driven classification model for detecting the “underfilling” defect. In parallel, the FEM model was refined by incorporating additional real-process phenomena, including ram tilt, press-frame deflection, and air entrapment in die cavities. The combined approach significantly enhanced defect prediction accuracy and provided deeper insight into the mechanisms driving defect formation. These results demonstrate the effectiveness of hybrid numerical–data-driven modelling for improving the robustness of metal forming process design.

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Periodical:

Key Engineering Materials (Volume 1049)

Pages:

75-87

DOI:

https://doi.org/10.4028/p-Nz4ICc

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Cite this paper

Online since:

April 2026

Authors:

Artem Alimov*, Yuyao Jiang, Markus Gardill, Sebastian Härtel

Keywords:

Data-Driven Modeling, Defect Prediction, FEM Modelling, Hot Bulk Forging

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Creative Commons CC BY 4.0

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* - Corresponding Author

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