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Numerical Data-Driven Modelling of Modified Samanta Process for Cold Extrusion of Gears

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Abstract:

The Guided Material Flow (GMF) process is an advanced variant of the Samanta process designed for the net shape cold extrusion of gears. The GMF process employs a modified die geometry to control material flow and significantly reduce maximum tool loads, effectively overcoming traditional process limitations. Key advantages include enhanced tooth tip strength and a reduction in face end deformations, which are characteristic defects in the conventional Samanta process. Minimising these deformations reduces the requirement for subsequent machining and enhances overall material efficiency. A numerical dataset was generated to train and validate data driven surrogate models, facilitating rapid process analysis without the computational cost of continuous Finite Element Analysis (FEA). The models developed in this paper enable the precise prediction of critical process outputs, including maximum punch force, die filling behaviour, material utilisation and strain hardening at the tooth tip. This paper details the numerical data acquisition, the specific training and validation methodologies of the machine learning models and demonstrates their capability to accurately predict complex process outcomes when varying the geometry of the die active surface in the GMF process.

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Periodical:

Key Engineering Materials (Volume 1048)

Pages:

165-177

DOI:

https://doi.org/10.4028/p-wC1Lah

Citation:

Cite this paper

Online since:

April 2026

Authors:

Tahsin Deliktas*, Marcel Görz, Adrian Schenek, Marco Speth, Mathias Liewald

Keywords:

Cold Forging, Gears, Machine Learning, Metal Forming, Simulation

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Creative Commons CC BY 4.0

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* - Corresponding Author

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