Automated Hybrid Machine Learning System for Production

Procedia CIRP, vol. 138, pp. 30–35

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.

Authors 10

1. Hendrik Mende corresponding Aachen Fraunhofer Institute for Production Technology IPT

   Fraunhofer Institute for Production Technology IPT

   Affiliation as printed

   Fraunhofer Institute for Production Technology IPT, Steinbachstr. 17, Aachen 52074, Germany

2. Henrik Heymann Aachen Fraunhofer Institute for Production Technology IPT

   Fraunhofer Institute for Production Technology IPT

   Affiliation as printed

   Fraunhofer Institute for Production Technology IPT, Steinbachstr. 17, Aachen 52074, Germany

3. Laura Battistella Fiorini Aachen Fraunhofer Institute for Production Technology IPT

   Fraunhofer Institute for Production Technology IPT

   Affiliation as printed

   Fraunhofer Institute for Production Technology IPT, Steinbachstr. 17, Aachen 52074, Germany

4. Gustavo Schewinski Aachen Fraunhofer Institute for Production Technology IPT

   Fraunhofer Institute for Production Technology IPT

   Affiliation as printed

   Fraunhofer Institute for Production Technology IPT, Steinbachstr. 17, Aachen 52074, Germany

5. Dennis Grunert Aachen Fraunhofer Institute for Production Technology IPT

   Fraunhofer Institute for Production Technology IPT

   Affiliation as printed

   Fraunhofer Institute for Production Technology IPT, Steinbachstr. 17, Aachen 52074, Germany

6. Albina Karimova Aachen Fraunhofer Institute for Production Technology IPT

   Fraunhofer Institute for Production Technology IPT

   Affiliation as printed

   Fraunhofer Institute for Production Technology IPT, Steinbachstr. 17, Aachen 52074, Germany

7. Hamidreza Paria Aachen Fraunhofer Institute for Production Technology IPT

   Fraunhofer Institute for Production Technology IPT

   Affiliation as printed

   Fraunhofer Institute for Production Technology IPT, Steinbachstr. 17, Aachen 52074, Germany

8. Christian Strobl Aachen Fraunhofer Institute for Production Technology IPT

   Fraunhofer Institute for Production Technology IPT

   Affiliation as printed

   Fraunhofer Institute for Production Technology IPT, Steinbachstr. 17, Aachen 52074, Germany

9. Robert Heinrich Schmitt Aachen Fraunhofer Institute for Production Technology IPT Laboratory for Machine Tools and Production Engineering WZL

   Fraunhofer Institute for Production Technology IPT · RWTH Aachen University

   Affiliation as printed

   Fraunhofer Institute for Production Technology IPT, Steinbachstr. 17, Aachen 52074, Germany

   Laboratory for Machine Tools and Production Engineering WZL of RWTH Aachen University, Campus-Boulevard 30, Aachen 52074, Germany

10. Thomas Bergs Aachen Fraunhofer Institute for Production Technology IPT Manufacturing Technology Institute MTI

    Fraunhofer Institute for Production Technology IPT · RWTH Aachen University

    Affiliation as printed

    Fraunhofer Institute for Production Technology IPT, Steinbachstr. 17, Aachen 52074, Germany

    Manufacturing Technology Institute MTI, RWTH Aachen University, Campus-Boulevard 30, Aachen 52074, Germany