Data-Driven Optimization Of Metal Additive Manufacturing Solutions

    Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review


    The freedoms of additive manufacturing (AM) go beyond geometry, with metal AM it is possible to tailor powders, alloys, microstructures as well as processes and manufacturing parameters, to name a few. It is expected that with global data-driven optimization it becomes possible to tailor product and application specific metal AM solutions, subsequently significantly improving the competitiveness of respective AM products. In current work integrated computational materials engineering and machine learning (ML) are utilized to create a workflow for optimization of metal AM solutions. Physics-based models aid in the delivery of ML training data, and the resulting data-driven models are suited for fast and thorough optimization of metal AM products. Use case is presented with different performance metrics targeting critical product material properties which are optimized across the metal AM process to product performance chain.
    Original languageEnglish
    Title of host publicationEuro PM2019 Proceedings
    PublisherEuropean Powder Metallurgy Association (EPMA)
    ISBN (Electronic)978-1-899072-51-4
    Publication statusPublished - Oct 2019
    MoE publication typeA4 Article in a conference publication
    EventEuro PM2019 Congress & Exhibition: Europe's annual powder metallurgy congress and exhibition - Maastricht, Netherlands
    Duration: 13 Oct 201916 Oct 2019


    ConferenceEuro PM2019 Congress & Exhibition


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