Designing materials by laser powder bed fusion with machine learning-driven bi-objective optimization

Publikation: Beitrag in FachzeitschriftForschungsartikelBeigetragenBegutachtung

Beitragende

  • Denys Y. Kononenko - , Leibniz-Institut für Festkörper- und Werkstoffforschung Dresden (Autor:in)
  • Dmitry Chernyavsky - , Leibniz-Institut für Festkörper- und Werkstoffforschung Dresden (Autor:in)
  • Wayne E. King - , The Barnes Global Advisors LLC (Autor:in)
  • Julia Kristin Hufenbach - , Leibniz-Institut für Festkörper- und Werkstoffforschung Dresden, Technische Universität Bergakademie Freiberg (Autor:in)
  • Jeroen van den Brink - , Professur für Festkörpertheorie (gB/IFW), Leibniz-Institut für Festkörper- und Werkstoffforschung Dresden, Technische Universität Dresden (Autor:in)
  • Konrad Kosiba - , Leibniz-Institut für Festkörper- und Werkstoffforschung Dresden (Autor:in)

Abstract

To exploit the full industrial potential of additive manufacturing (AM) beyond prototyping, the resource-consuming identification of the optimal processing conditions needs to be minimized. This task becomes more challenging when multiple properties of the part shall be simultaneously optimized. We utilize machine learning (ML) methods in a case study on laser powder bed fusion (LPBF) of a Zr-based glass-forming alloy. Our experiments show that processing parameters affect density and amorphicity opposingly, demonstrating the efficacy of our ML-based approach. We employ multi-objective optimization using Gaussian Process Regression to model and predict target properties and their uncertainties of parts fabricated by LPBF – a widely used metal AM technology. With density and amorphicity as target parameters, we optimize models using the Pareto front facilitated by the Non-Dominated Sorting Genetic Algorithm II. Despite deviations in the amorphicity data, we demonstrate this method to identify the high-performance region of the process parameters and its ability to be iteratively enhanced with additional experimental data. This bi-objective optimization approach provides a robust toolset for navigating LPBF processing. It can be easily extended to a larger set of target properties and transferred to further AM technologies.

Details

OriginalspracheEnglisch
Seiten (von - bis)6802-6811
Seitenumfang10
FachzeitschriftJournal of Materials Research and Technology
Jahrgang30
PublikationsstatusVeröffentlicht - 1 Mai 2024
Peer-Review-StatusJa

Schlagworte

Schlagwörter

  • Additive manufacturing, Bulk metallic glass, Gaussian processes, Laser powder bed fusion, Machine learning