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Open AccessFeature PaperArticle

Evaluation of Ti–Mn Alloys for Additive Manufacturing Using High-Throughput Experimental Assays and Gaussian Process Regression

1
School of Materials Science and Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0245, USA
2
George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0405, USA
3
Department of Materials Science and Engineering, Iowa State University, Ames, IA 50011, USA
*
Author to whom correspondence should be addressed.
Materials 2020, 13(20), 4641; https://doi.org/10.3390/ma13204641
Received: 16 September 2020 / Revised: 12 October 2020 / Accepted: 14 October 2020 / Published: 17 October 2020
(This article belongs to the Special Issue Empowering Materials Processing and Performance from Data and AI)
Compositionally graded cylinders of Ti–Mn alloys were produced using the Laser Engineered Net Shaping (LENS™) technique, with Mn content varying from 0 to 12 wt.% along the cylinder axis. The cylinders were subjected to different post-build heat treatments to produce a large sample library of α–β microstructures. The microstructures in the sample library were studied using back-scattered electron (BSE) imaging in a scanning electron microscope (SEM), and their mechanical properties were evaluated using spherical indentation stress–strain protocols. These protocols revealed that the microstructures exhibited features with averaged chord lengths in the range of 0.17–1.78 μm, and beta content in the range of 20–83 vol.%. The estimated values of the Young’s moduli and tensile yield strengths from spherical indentation were found to vary in the ranges of 97–130 GPa and 828–1864 MPa, respectively. The combined use of the LENS technique along with the spherical indentation protocols was found to facilitate the rapid exploration of material and process spaces. Analyses of the correlations between the process conditions, several key microstructural features, and the measured material properties were performed via Gaussian process regression (GPR). These data-driven statistical models provided valuable insights into the underlying correlations between these variables. View Full-Text
Keywords: high-throughput experimentation; additive manufacturing; Ti–Mn alloys; spherical indentation; statistical analysis; Gaussian process regression high-throughput experimentation; additive manufacturing; Ti–Mn alloys; spherical indentation; statistical analysis; Gaussian process regression
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Gong, X.; Yabansu, Y.C.; Collins, P.C.; Kalidindi, S.R. Evaluation of Ti–Mn Alloys for Additive Manufacturing Using High-Throughput Experimental Assays and Gaussian Process Regression. Materials 2020, 13, 4641.

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