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ML-Based Prediction of Tool Kinematics in Cold Forging of Tailored Hollow Shafts with Variable Wall Thickness

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Abstract:

The production of tailored hollow shafts usually requires multiple manufacturing processes such as multi-stage forming processes and subsequently several machining operations, resulting into high costs and high manufacturing times. To address these challenges, a novel cold forging process featuring an adjustable forming zone was developed by the authors. This new approach enables the production of tailored hollow shafts with varying cross-sections in their length direction as well as internal undercuts within one stroke of the ram. In order to achieve the desired target geometry of a hollow shaft, a specific tool kinematic is required to precisely adjust the cross-section of the forming zone during the process. Currently, determining geometry-specific tool kinematics requires a time consuming iterative numerical procedure. In this paper, a machine learning approach for the prediction of the tool kinematics for a given target geometry of a tailored hollow shaft with variable wall thickness in its length direction is presented.

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Periodical:

Materials Science Forum (Volume 1186)

Pages:

47-57

DOI:

https://doi.org/10.4028/p-92DAUf

Citation:

Cite this paper

Online since:

April 2026

Authors:

Alexander Weiss*, Mathias Liewald

Keywords:

Cold Forging, Tool Kinematic Prediction, Variable Wall Thickness

Funder:

The publication of this article was funded by the University of Stuttgart

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