Predicting Thermoelectric Power Factor of Bismuth Telluride During Laser Powder Bed Fusion Additive Manufacturing

• URL: http://arxiv.org/abs/2303.15663v1
• Date: Tue, 28 Mar 2023 01:09:15 GMT
• Title: Predicting Thermoelectric Power Factor of Bismuth Telluride During Laser Powder Bed Fusion Additive Manufacturing
• Authors: Ankita Agarwal (1), Tanvi Banerjee (1), Joy Gockel (2), Saniya LeBlanc (3), Joe Walker (4), John Middendorf (4) ((1) Wright State University, (2) Colorado School of Mines, (3) The George Washington University, (4) Open Additive, LLC)
• Abstract summary: In thermoelectric materials, the power factor is a measure of how efficiently the material can convert heat to electricity. In this study, we train different machine learning models to predict the power factor of bismuth telluride (Bi2Te3) during the additive manufacturing process.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: An additive manufacturing (AM) process, like laser powder bed fusion, allows for the fabrication of objects by spreading and melting powder in layers until a freeform part shape is created. In order to improve the properties of the material involved in the AM process, it is important to predict the material characterization property as a function of the processing conditions. In thermoelectric materials, the power factor is a measure of how efficiently the material can convert heat to electricity. While earlier works have predicted the material characterization properties of different thermoelectric materials using various techniques, implementation of machine learning models to predict the power factor of bismuth telluride (Bi2Te3) during the AM process has not been explored. This is important as Bi2Te3 is a standard material for low temperature applications. Thus, we used data about manufacturing processing parameters involved and in-situ sensor monitoring data collected during AM of Bi2Te3, to train different machine learning models in order to predict its thermoelectric power factor. We implemented supervised machine learning techniques using 80% training and 20% test data and further used the permutation feature importance method to identify important processing parameters and in-situ sensor features which were best at predicting power factor of the material. Ensemble-based methods like random forest, AdaBoost classifier, and bagging classifier performed the best in predicting power factor with the highest accuracy of 90% achieved by the bagging classifier model. Additionally, we found the top 15 processing parameters and in-situ sensor features to characterize the material manufacturing property like power factor. These features could further be optimized to maximize power factor of the thermoelectric material and improve the quality of the products built using this material.

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