New insight into the multivariate relationships among process, structure, and properties in laser powder bed fusion AlSi10Mg

Qixiang Luo, Nancy Huang, Tianyi Fu, Jinying Wang, Dean L. Bartles, Timothy W. Simpson, Allison M. Beese

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


While AlSi10Mg has been extensively studied since its introduction, only a relatively narrow range of processing conditions has been examined during laser powder bed fabrication of this material system. This study expands the knowledge of the process-structure-property (PSP) relationships of AlSi0Mg through the quantification of multivariate feature correlations found during laser powder bed fusion. Specifically, extensive ranges of laser power and scan speed were studied over two different hatch spacings to build fully dense and flawed test coupons. Porosity arising from lack of fusion and keyholing was assessed with X-ray computed tomography, and microstructural features, including grain/sub-grain features, were characterized along with mechanical properties under uniaxial tension. This extensive dataset was used with data-driven multivariate feature correlation modeling to gain new insights into the PSP relationships of AlSi10Mg fabricated with laser powder bed fusion. It was confirmed that processing conditions strongly influenced porosity and mechanical properties over a wider range than what was previously known, but grain/sub-grain features were not as heavily influenced despite the wide range studied. Processing parameters and porosity contributed significantly to the prediction of mechanical properties for all samples, and cell size was the highest contributing feature to predict strength and ductility for samples with greater than 99.5% density among the many microstructural features studied. In addition to this new knowledge about the influence of processing conditions on the PSP relationships in AlSi10Mg, this large and unique dataset is made publicly available to enhance and accelerate verification and validation of Integrated Computational Materials Engineering and other machine learning approaches being applied to laser powder bed fusion.

Original languageEnglish (US)
Article number103804
JournalAdditive Manufacturing
StatePublished - Sep 5 2023

All Science Journal Classification (ASJC) codes

  • Biomedical Engineering
  • General Materials Science
  • Engineering (miscellaneous)
  • Industrial and Manufacturing Engineering

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