AI supported process optimisation in a multi-material cup backward extrusion process chain

verfasst von: Eduard Ortlieb, Hendrik Wester, Johanna Uhe, Bernd-Arno Behrens
Abstract: Fluctuations in process parameters are a significant cost driver in hot forging. Particularly in multi-stage processes, fluctuations in the early process steps can lead to components having to be declared as scrap. Especially in the processing of hybrid components, these fluctuations pose a major problem, as the manufacturing costs are higher and the joining zone properties, which are very susceptible to process fluctuations, have a strong influence on the properties of the resulting component. The aim of this work is to develop an AI-supported solution for inline parameter optimisation. This approach allows for the compensation of process fluctuations by adjusting subsequent process steps, in order to still achieve end products that meet the requirements. FE-Simulations were carried out, whereby the boundary conditions for the induction current and the press speed were each varied by multiplying the original time curves with a normal distributed factor in order to simulate process noise. The resulting data was used to train a machine learning model that predicts the maximum first principal stress as indicator for the condition of the joining zone and the contact from the semi-finished product to the workpiece as indicator for mould filling. An evolutionary algorithm was used to optimise the press speed and the stroke in order to maximise contact and minimise the maximum first principal stress. Finally, the prediction time was minimised while maintaining the prediction accuracy. The approach presented promises a significant reduction in waste by enabling dynamic and predictive adjustment of process parameters in real time. This not only leads to an increase in efficiency, but also to a reduction in costs in the manufacturing process.
Organisationseinheit(en): Institut für Umformtechnik und Umformmaschinen
Typ: Aufsatz in Konferenzband
Seiten: 889-898
Anzahl der Seiten: 10
Publikationsdatum: 2025
Publikationsstatus: Veröffentlicht
Peer-reviewed: Ja
ASJC Scopus Sachgebiete: Allgemeine Materialwissenschaften
Elektronische Version(en): https://doi.org/10.21741/9781644903599-95 (Zugang: Offen)
: Details im Forschungsportal „Research@Leibniz University“

BibTeX

@inproceedings{a5e78d53b0714f058bcb4f75d6719211,
title = "AI supported process optimisation in a multi-material cup backward extrusion process chain",
abstract = "Fluctuations in process parameters are a significant cost driver in hot forging. Particularly in multi-stage processes, fluctuations in the early process steps can lead to components having to be declared as scrap. Especially in the processing of hybrid components, these fluctuations pose a major problem, as the manufacturing costs are higher and the joining zone properties, which are very susceptible to process fluctuations, have a strong influence on the properties of the resulting component. The aim of this work is to develop an AI-supported solution for inline parameter optimisation. This approach allows for the compensation of process fluctuations by adjusting subsequent process steps, in order to still achieve end products that meet the requirements. FE-Simulations were carried out, whereby the boundary conditions for the induction current and the press speed were each varied by multiplying the original time curves with a normal distributed factor in order to simulate process noise. The resulting data was used to train a machine learning model that predicts the maximum first principal stress as indicator for the condition of the joining zone and the contact from the semi-finished product to the workpiece as indicator for mould filling. An evolutionary algorithm was used to optimise the press speed and the stroke in order to maximise contact and minimise the maximum first principal stress. Finally, the prediction time was minimised while maintaining the prediction accuracy. The approach presented promises a significant reduction in waste by enabling dynamic and predictive adjustment of process parameters in real time. This not only leads to an increase in efficiency, but also to a reduction in costs in the manufacturing process.",
keywords = "Cup Backward Extrusion, Machine Learning, Process Optimisation",
author = "Eduard Ortlieb and Hendrik Wester and Johanna Uhe and Bernd-Arno Behrens",
note = "Publisher Copyright: {\textcopyright} 2025, Association of American Publishers. All rights reserved.; 28th International ESAFORM Conference on Material Forming, ESAFORM 2025, ESAFORM 2025 ; Conference date: 07-05-2025 Through 09-05-2025",
year = "2025",
doi = "10.21741/9781644903599-95",
language = "English",
isbn = "9781644903599",
series = "Materials Research Proceedings",
pages = "889--898",
editor = "Pierpaolo Carlone and Luigino Filice and Domenico Umbrello",
booktitle = "Material Forming ESAFORM 2025",
}