| About this Abstract |
| Meeting |
MS&T23: Materials Science & Technology
|
| Symposium
|
Additive Manufacturing: Design, Materials, Manufacturing, Challenges and Applications
|
| Presentation Title |
The Control of Tailored Microstructure and Thermoelectric Properties in Additively Manufactured Materials |
| Author(s) |
Connor Headley, Roberto Herrera del Valle, Ji Ma, Prasanna Balachandran, Vijayabarathi Ponnambalam, Saniya LeBlanc, Dylan Kirsch, Joshua Martin |
| On-Site Speaker (Planned) |
Connor Headley |
| Abstract Scope |
The implementation of additive manufacturing promises to create thermoelectric devices with increased efficiency and lowered production costs. Through the integration of machine learning techniques alongside well-curated additive manufacturing experimentation, we quickly drew vital connections between processing parameters, melt pool geometries, and defects to produce highly dense, geometrically complex bismuth telluride parts. Further, a system of high throughput sample fabrication and characterization was devised to rapidly determine the connections between processing conditions, material structure, and resulting thermoelectric properties. With the aid of machine learning, key characteristics such as composition, porosity, and microstructural features were accurately predicted and mapped across the processing parameter space. The microstructure and thermoelectric properties of the bismuth telluride builds could then be intentionally altered using the additive manufacturing process. Ultimately, this understanding of the processing-structure-property relationships has allowed us to deliberately vary the character of these samples from n-type to p-type through processing parameter modifications. |