Automated Hybrid Machine Learning System for Production

Procedia CIRP 2026 Open access

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Authors: Hendrik Mende, Henrik Heymann, Laura Battistella Fiorini, Gustavo Schewinski, Dennis Grunert, Albina Karimova, Hamidreza Paria, Christian Strobl, Robert H. Schmitt, Thomas Bergs
DOI: 10.1016/j.procir.2026.01.007
Cited by: 1
References: 25

Abstract

Hybrid Machine Learning (ML) models have potential for high performance, as they learn based on data and incorporate existing knowledge. In this work, an automated system is developed which creates the best possible hybrid ML model without Data Science expertise. First, the system architecture is defined including the breakdown of functionalities into a workflow along the sub-components of the system. Then, the system is validated on a use case from optics production, i.e., quality prediction during nonisothermal glass forming. A performance comparison between a baseline ML model with the automatically generated hybrid ML model is demonstrated.

Fields of Study & Subjects

Machine Learning in Materials Science Laser and Thermal Forming Techniques Machine Learning and Data Classification

Publication details

Journal / source
Procedia CIRP
Publisher
Elsevier BV
Volume
138
Pages
30–35
Publication date
1 January 2026
Language
EN