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dc.contributor{"last":"Kahl","first":"Matthias","role":"Researcher","affiliation":"University of Kassel, Institute of System Analytics and Control, Department of Measurement and Control"}
dc.contributor{"last":"Richter","first":"Julia","role":"Researcher","affiliation":"University of Kassel, Institute of Materials Engineering, Metallic Materials"}
dc.contributor{"last":"Krooß","first":"Philipp Dr.-Ing.","role":"Researcher","affiliation":"University of Kassel, Institute of Materials Engineering, Metallic Materials"}
dc.contributor{"last":"Kroll","first":"Andreas Prof. Dr.-Ing.","role":"ProjectLeader","affiliation":"University of Kassel, Institute of System Analytics and Control, Department of Measurement and Control"}
dc.contributor{"last":"Niendorf","first":"Thomas Prof. Dr.-Ing.","role":"ProjectLeader","affiliation":"University of Kassel, Institute of Materials Engineering, Metallic Materials"}{"last":"Engelhardt","first":"Anna","affiliation":"University of Kassel, Institute of Materials Engineering, Metallic Materials"}
dc.descriptionTo reduce production time and decrease production cost, the increase of layer thickness is an adequate option in powder bed fusion. To investigate the influence of high layer thicknesses in the production of parts, a wide range of process parameters must be considered since appropriate processing windows are expected to be very narrow. Therefore, exploring the design space solely by the use of experimental investigations would be inefficient. A more efficient way is to use experiments in combination with a mathematical modeling approach. Such an approach was made in the study described in In order to determine the relationships between process parameters in laser powder bed fusion (PBF-LB/M) and final porosity in AlSi10Mg, samples were processed following a space-filling experimental design using a standard industrial PBF-LB/M system equipped with a 400 W laser. A total of 144 samples were fabricated considering layer thicknesses of 30 μm, 45 μm, 60 μm, and 90 μm. Afterwards, porosity was assessed using image analysis and computed tomography. Different types of defects were found as expected, however, fully dense parts were realized in case of every considered layer thickness. Predictive models were developed using data-driven approaches, eventually enabling multivariate analysis of the correlations and determination of appropriate processing conditions resulting in both low porosity of parts and high build rates.de_DE
dc.descriptionThis dataset provides the process parameter combinations and the porosity values which were used to develop the predictive models using data-driven approaches. Moreover, meta data about the experimental design, the sample manufacturing, the image capturing, and the porosity determination are given.de_DE
dc.descriptionIMPORTANT: In case you use the data please cite our corresponding article
dc.description.sponsorship{"funderName":"Universität Kassel - Programmlinie Zukunft","awardTitle":"Digitalisierung in der Werkstofftechnik (DigiWerk)"}
dc.rightsCreative Commons Attribution 4.0
dc.subjectSelective laser meltingde_DE
dc.subjectData-driven modelingde_DE
dc.subjectPredictive modelsde_DE
dc.subjectBuild ratede_DE
dc.subjectProcessing windowsde_DE
dc.subjectAdditive manufacturingde_DE
dc.subject.classification405-02 Materialien und Werkstoffe der Sinterprozesse und der generativen Fertigungsverfahrende_DE
dc.subject.classification406-04 Computergestütztes Werkstoffdesign und Simulation von Werkstoffverhalten von atomistischer bis mikroskopischer Skalade_DE
dc.titleInvestigation of processing windows in additive manufacturing of AlSi10Mg for faster production utilizing data-driven modelingde_DE
local.ka.departmentInstitute of Materials Engineering, Metallic Materialsde_DE

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Creative Commons Attribution 4.0
Except where otherwise noted, this item's license is described as Creative Commons Attribution 4.0