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

dc.bibliographicCitation.firstPage6802
dc.bibliographicCitation.journalTitleJournal of Materials Research and Technology
dc.bibliographicCitation.lastPage6811
dc.bibliographicCitation.volume30
dc.contributor.authorKononenko, Denys Y.
dc.contributor.authorChernyavsky, Dmitry
dc.contributor.authorKing, Wayne E.
dc.contributor.authorHufenbach, Julia Kristin
dc.contributor.authorvan den Brink, Jeroen
dc.contributor.authorKosiba, Konrad
dc.date.accessioned2024-10-15T08:49:11Z
dc.date.available2024-10-15T08:49:11Z
dc.date.issued2024
dc.description.abstractTo 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.eng
dc.description.versionpublishedVersioneng
dc.identifier.urihttps://oa.tib.eu/renate/handle/123456789/16782
dc.identifier.urihttps://doi.org/10.34657/15804
dc.language.isoeng
dc.publisherRio de Janeiro : Elsevier
dc.relation.doihttps://doi.org/10.1016/j.jmrt.2024.05.046
dc.relation.essn2214-0697
dc.relation.issn2238-7854
dc.rights.licenseCC BY 4.0 Unported
dc.rights.urihttps://creativecommons.org/licenses/by/4.0
dc.subject.ddc670
dc.subject.otherAdditive manufacturingeng
dc.subject.otherBulk metallic glasseng
dc.subject.otherGaussian processeseng
dc.subject.otherLaser powder bed fusioneng
dc.subject.otherMachine learningeng
dc.titleDesigning materials by laser powder bed fusion with machine learning-driven bi-objective optimizationeng
dc.typeArticle
dc.typeText
tib.accessRightsopenAccess
wgl.contributorIFWD
wgl.subjectChemieger
wgl.subjectPhysikger
wgl.typeZeitschriftenartikelger
Files
Original bundle
Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
1-s2-0-S2238785424010883-main.pdf
Size:
2.08 MB
Format:
Adobe Portable Document Format
Description:
Collections