The development of an augmented machine learning approach for the additive manufacturing of thermoelectric materials

Headley, Connor V.; Herrera del Valle, Roberto J.; Ma, Ji; Balachandran, Prasanna; Ponnambalam, Vijayabarathi; LeBlanc, Saniya; Kirsch, Dylan; Martin, Joshua B.

doi:10.1016/j.jmapro.2024.02.045

Title: The development of an augmented machine learning approach for the additive manufacturing of thermoelectric materials

Journal Article · 27 February 2024 · Journal of Manufacturing Processes

DOI: https://doi.org/10.1016/j.jmapro.2024.02.045 · OSTI ID:2377738

^[1]; Herrera del Valle, Roberto J. ^[2]; Ma, Ji ^[2]; Balachandran, Prasanna ^[2]; Ponnambalam, Vijayabarathi ^[3]; LeBlanc, Saniya ^[3]; Kirsch, Dylan ^[4];

^[5]

University of Virginia, Charlottesville, VA (United States); University of Virginia
University of Virginia, Charlottesville, VA (United States)
George Washington University, Washington D.C. (United States)
National Institute of Standards and Technology (NIST), Gaithersburg, MD (United States); University of Maryland, College Park, MD (United States)
National Institute of Standards and Technology (NIST), Gaithersburg, MD (United States)

Through the integration of machine learning (ML) techniques alongside additive manufacturing (AM) experimentation, we demonstrate an iterative process to rapidly predict laser-material interactions and melt pool geometries throughout the build parameter space for a bismuth telluride thermoelectric (TE) material. In doing so, we determined process parameters that created crack-free, highly dense (>99 %) n-type bismuth telluride (Bi₂Te_2.7Se_0.3) parts through laser powder bed fusion (LPBF). Furthermore, the ML-assisted understanding of the processing space allowed for the identification of build parameters that successfully yielded geometrically enhanced Bi₂Te_2.7Se_0.3 parts with reduced build times and no increase in experimental effort.

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Research Organization:: University of Virginia, Charlottesville, VA (United States)

Sponsoring Organization:: USDOE

Grant/Contract Number:: EE0009100

OSTI ID:: 2377738

Journal Information:: Journal of Manufacturing Processes, Journal Name: Journal of Manufacturing Processes Vol. 116; ISSN 1526-6125

Publisher:: Society of Manufacturing Engineers; ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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36 MATERIALS SCIENCE
Additive manufacturing
Bismuth telluride
Laser powder bed fusion
Machine learning
Processing maps
Thermoelectric materials

Title: The development of an augmented machine learning approach for the additive manufacturing of thermoelectric materials

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