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

Published

Author(s)

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

Abstract

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 (Bi2Te2.7Se0.3) parts through laser powder bed fusion (LPBF). Further, the ML-assisted understanding of the processing space allowed for the identification of build parameters that successfully yielded geometrically enhanced Bi2Te2.7Se0.3 parts with reduced build times and no increase in experimental effort.
Citation
Journal of Manufacturing Processes
Volume
116

Keywords

Additive manufacturing, Laser powder bed fusion, Thermoelectric materials, Bismuth telluride, Machine learning, Processing maps

Citation

Headley, C. , Herrera del Valle, R. , Ma, J. , Balachandran, P. , Ponnambalam, V. , LeBlanc, S. , Kirsch, D. and Martin, J. (2024), The development of an augmented machine learning approach for the additive manufacturing of thermoelectric materials, Journal of Manufacturing Processes, [online], https://doi.org/10.1016/j.jmapro.2024.02.045, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=956324 (Accessed April 17, 2024)
Created February 28, 2024, Updated March 25, 2024