|About this Abstract
||2022 TMS Annual Meeting & Exhibition
||Additive Manufacturing and Innovative Powder Processing of Functional and Magnetic Materials
||The Development of a Machine Learning Guided Process for the Additive Manufacturing of Thermoelectric Materials
||Connor Headley, Roberto J. Herrera del Valle, Ji Ma, Prasanna Balachandran, Vijayabarathi Ponnambalam, Dylan Kirsch, Saniya LeBlanc, Joshua B. Martin
|On-Site Speaker (Planned)
The implementation of additive manufacturing promises to create thermoelectric devices with increased efficiency and lowered production costs. However, the optimal additive manufacturing processing parameters for any thermoelectric material are currently unknown, and the development of an additive manufacturing process for a new material is traditionally an arduous task that requires numerous rounds of experimental trial-and-error. Through the integration of machine learning techniques alongside well-curated additive manufacturing experimentation, we quickly draw vital connections between processing parameters, melt pool geometries, and defects while significantly reducing experimental burden. We rapidly developed process parameters for laser powder bed fusion that created highly dense, geometrically complex bismuth telluride parts through additive manufacturing. The thermal conductivities, electrical conductivities, and Seebeck coefficients of these parts were also measured for comparison to traditional thermoelectric devices. Finally, microstructure characterization was carried out to make connections between the additive manufacturing process and the resulting thermoelectric properties.
||Additive Manufacturing, Energy Conversion and Storage, Machine Learning